Episode 3

full
Published on:

17th Jun 2026

The "Golden Thread": Fixing AEC’s Information Sprawl

Carl Veillette, Chief Product Officer at Newforma, joins Evan Troxel and Randall Stevens to talk about the data problem hiding underneath every project. Anchored by the new Confluence Tech Stack Survey, where the tool count per project jumped from about 15 a few years ago to 84, Veillette argues the real issue is not the number of tools but the broken connections between them. He makes the case that a single source of truth does not exist, and that the goal is a single view across systems like Teams, Outlook, SharePoint, ACC, ProjectWise, and Procore. They get concrete about the golden thread, governance and retention, and why Newforma's job is to free the data from the files rather than host it.

This episode is especially relevant for CIOs, digital practice leaders, and project information managers wrestling with data sprawl, litigation risk, and AI adoption. Veillette walks through practical AI use cases, from mining the inbox for project records to agentic workflows for code compliance and lessons learned. The throughline is blunt and useful: AI is only as good as the data behind it, so the value lives in the record, not the model.

Episode Links:

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Watch this episode on YouTube.

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The Confluence podcast is a collaboration between TRXL and AVAIL, and is produced by TRXL Media.

Transcript
Randall Stevens:

Welcome to another Confluence podcast.

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I'm Randall Stevens and I've

got Evan TRXL with me as usual.

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And we've got a, maybe

our first repeat guest.

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I can't remember if we've

had, uh, had somebody on

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Carl Veillette: a big title.

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Evan Troxel: so.

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But, but if you're, but if you're

wrong, I mean, we're bo we're

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Randall Stevens: I know.

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Well,

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Evan Troxel: so,

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Randall Stevens: can fact check, right?

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It's like the, uh, the uh, on, uh,

on x, uh, whatever community note.

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Somebody can make a

community note about it.

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But, uh, anyway, we've got, uh, Carl

Vati from, uh, new former with us.

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Uh, I was looking back 'cause

I was reviewing, uh, you know,

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the, the last conversation we

had and it was almost a year ago.

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So, and I was joking just before we

came on here that I'm a year older and

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my, I've started to let my hair dry

'cause I had it, uh, cut pretty short

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on that last, uh, on that last call.

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But, uh, glad to have you back on Carl.

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And, you know, one of the things I think

about, uh, you know, we, we literally have

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about a year under our belt from the last

time we talked, but things are moving.

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You know, pretty quickly with

these kind of technology shifts.

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So I think it's, uh, appropriate for us to

kind of jump back on and, uh, 'cause last

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year we were beginning to talk about how's

the AI gonna affect some of these things.

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I know you guys have been working

on some of these strategies.

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Add new form and then also kind of

topical was that we just put out

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recently this, uh, confluence, uh, tech

stack survey, technology stack survey.

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So I know you've had a little

bit of time to look at that.

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It's kind of hot off, fresh off the

presses, but there's a lot of things

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I think that are there's, that are in

that report that, you know, kind of sets

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the stage for just this complexity of

the ecosystem, which is a lot of what

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we were talking about a year ago too.

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Um, so anyway, but, uh, welcome,

welcome back on and, uh, maybe, uh,

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maybe we can kick, kick this off.

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Um.

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I, I, you know, I was reviewing some

of the stuff that you've been, uh,

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talking about over the last year with

what you're doing in new form, and one

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of those things is you kind of refer

to this information sprawl and, uh, and

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how they kind of get this golden thread

that can kind of weave back through.

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I always, I, I talk about the same thing

with a veil, that there's this thread

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that, uh, we have a feature called related

content that's like, lets you connect this

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information, but maybe we can kind of kick

off there and, uh, maybe explain what you

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mean by that golden thread and then we

can, uh, we can kind of dig into maybe,

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uh, maybe this, this application sprawl

and just where all this information is.

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Carl Veillette: Yeah.

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So I, I think, uh, you know, when I

think about, um, our tech stack these

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days, and I guess that relates to the

survey that you guys did recently, right?

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But, um, you know, the, the tech stack

is growing and there's, um, you know,

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there's a survey we did a few years back

and we asked the question, how many tools

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do you have in your toolbox on a project?

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Right?

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And the answer was 15, right?

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At the time.

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And, uh, what struck me in your

survey is the number now is 84, right?

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So that's 84.

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The number of tools that organizations

are, are leveraging, right?

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So.

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I think this is like a, you know, this

is great because people are taking

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the best tool for the task and the

activities that they have to perform.

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Right?

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Um, so, um, this is great

for the industry, right?

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To, to be able to, you know,

have more technology choices and

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best in class tools for specific

things that they are trying to do.

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But at the same time, it is creating a

bit of, um, uh, governance issues, right?

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When it comes to data, right?

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And, uh, I think what we've seen in

the last year is, um, this increase

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in amount of tools and storage silos

and, you know, it creates datas for

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all issues and it's preventing you from

gathering insights and things like that.

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And think about the golden tread, right?

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In the uk it's a big thing because

of the creation is in place, right?

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And, um, this is, uh, this

is also contributing to, um.

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You know, that data scroll

and those information silos to

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break the golden thread, right?

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I think about the golden thread of

information as the, the ability to

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go back, you know, maybe like 15,

10 years down the road when the

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project's done and be able to reconcile

the audit trail of what happened

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when we sent what to who, right?

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And the ability to understand the

context of the project at the time.

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Right?

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So for me, like a golden tread is

not necessarily a single source

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of truth because this thing

in my opinion, doesn't exist.

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Uh, there's multiple

source of truth, right?

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And it's all about trying to

connect the dots together, right?

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And I think, you know, a lot

of people talk about command

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environment and those sort of things.

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Uh, some vendors call themself a

command time environment, which I

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think doesn't really exist, right?

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It's an a series of interconnected

tools that exchange information.

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So, um, really like the mission

we've been about, um, in the last,

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uh, you know, couple of years.

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And, uh, I think that's, you know,

a mission that's, uh, that's even

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more difficult these days now that

there are a lot more tools, right?

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Is, is to connect the dots so that

we can create a single view of

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information as opposed to a single

source of information, right?

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So, you know, when it comes to

technology adoption, I think one

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of the biggest challenge is, um,

well change management, right?

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Because we're all humans and, uh,

we like to work certain way, we

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don't like to change things, right?

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Um, so, you know, really where we've

been focused is, uh, not trying to

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change the way people work, right?

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Uh, but also create kind of an information

layer that connects to those various

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best in class tools that people use.

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So you think about, um,

Microsoft teams, right?

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So it's unlikely you're gonna displace

Microsoft team these days because it's a

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communication channel, number one, right?

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Uh, for, for project teams, right?

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And.

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Uh, I guess I would argue people thought

they, they were gonna replace emails, you

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know, you know, a few years ago, right?

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And this never happened.

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It's not gonna happen.

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Uh, we look at the statistics, right?

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Number of emails increase on projects

these days as a result of more complex

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projects and bigger teams and, uh,

regulations and, and such, right?

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So I guess the data proves

everybody wrong on this.

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You know, I was the first to think we

could replace emails in the been track

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days, but, you know, this hasn't happened.

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So now we just, we just have to, um, you

know, uh, connect those things together.

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And that's the approach we, we've, we've

been taking is like, okay, there is gonna

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be instant messaging in Microsoft teams.

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There's gonna be meeting minutes

captured with AI in there, there's

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gonna be emails, uh, so those are

all communications related, right?

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But you're gonna have like

a flow of information.

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Maybe a designer works with a, a GC

that's working out of Procore, right?

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So you've, you've, you're, you've

got all those, those information

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coming from various sources, right?

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So.

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Um, in instead of replacing those

sources or trying to be the center

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of the universe, um, why don't

we connect those things together?

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And then why don't we, um, create a,

a another trail kind of layer, uh, a

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data connection system that integrate

with those tools and, and bring the

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information in the context of the project.

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So when the last phase of construction

project kicks in, I'm talking

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about litigation here, you've got

the compete project record, right?

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You've got the evidences, you've got

the information that's been shared.

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Um, so for, for us, it, it's really

been looking at, um, you know,

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where is the data flowing from

and, um, kind of connecting that

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with the project activities, right?

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So, uh, for, for, for us, you know,

the, the golden thread is, is that

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ability to look at the complete project

archive, right from the, the very

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early stage of the project, all the

way to delivery and beyond, right?

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Randall Stevens: I know,

uh, Carl, uh, when I was.

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You know, looking back at the conversation

we had a year ago, one of the examples

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you gave was, um, you know, we were

talking about, you know, synchronous

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versus asynchronous communication and

how, you know, chat and email and all

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these different forms of communication.

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I, I agree with you.

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I think, I think every time, uh, every

time somebody thinks something's gonna

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come along and replace the other, it just

means we've just added another source

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or a form of, of, of communication.

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And you can't, it's hard to stop.

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But, uh, my, my question would be, you

know, when, uh, I think in that last,

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uh, conversation, you, you gave an

example where something had gone into

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some litigation and they had looked

back and said somebody had given like

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a thumbs up to, uh, to a message,

which was like a tacit approval, you

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know, um, s so do you think, uh, are,

are you all experimenting or do you

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think that AI is gonna be able to help?

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Um, you know, 'cause you can

get a lot of data points.

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Obviously you can get a lot of, uh,

information that on its own looks.

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It is not, it is not in

in the right context.

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Uh, and, and it seems like the LLMs,

that's one of the things that they're

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really good at, is if you can get a

good enough context window, it's like,

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okay, I can kind of boil this down.

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Or, uh, so I guess my question would be,

are you all thinking about using those

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kinds of technologies proactively so

that if somebody had, you know, let's

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say somebody gave a thumbs up, should the

system say, do, do you mean that that's

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approval or you like the, you know, what,

what does that, what does that mean?

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So you could think

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Evan Troxel: Or it says,

you shouldn't do that.

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Don't do that.

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Randall Stevens: right?

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Or just like, just to

be clear, is this right?

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Because you can imagine you'd want,

if it's something for approval and

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then ultimately, 'cause we've been

doing some experiments with it too.

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I think a really good use case of, of

the LLMs is to take, you know, kind

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of sparse, uh, detailed information

and, and roll it up into things

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that, you know, have meaning to

different people at different times.

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So kinda what, what are you

all seeing on that front?

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Carl Veillette: Yeah, so I think I will

just take a few, uh, a few use cases

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of ai and we just rolled out our first

AI feature in, you know, last year.

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So, um, I think AI can play

a role in multiple things.

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It can play a role in collecting

information, which is exactly what

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we did with our first implementation.

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So I can talk a little bit about that.

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I think it can also help, you know,

you're, you're thinking about,

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uh, information at scale, right?

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And you know, in my mind.

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It's, there's no question about, about

the fact we're, we're living in a data

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intensive, uh, world these days, right?

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And I look at these, the size of projects

archive five years ago and what the

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size of projects archive looks today,

the tools on the market have made it

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so easy to create more and more data.

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Uh, it, it's almost like we've reached

our human limit of how much information

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that we can consume, crunch, analyze,

you know, whatever on the project, right?

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So I think you would, you know,

you would think more tools equal

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more, like more efficient workflows.

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I have doubts about that actually.

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When you look at the index of productivity

and construction, it's been flat right?

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At past.

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So I, I feel like the, you know,

those tools are making certain

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activities more efficient, but it's

also like contributing to the over

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being of information overwhelm, right?

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So I, I think crunching and

analyzing information as.

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At scale is only gonna be possible

through the power of LLMs.

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'cause you would probably need like

10 people to look at the whole project

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information, try to figure out the project

summary, where are things at, right?

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But now with LLM, you can, you can

build like a search engine, you know,

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on top of your, your data layer on

the project, and then you can start

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to interact with that l and m and it's

gonna be almost instantly giving you

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answers for millions of data point.

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Right?

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So I, I'm, I'm really fascinated

about like, you know, the ability

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to, to summarize information, right?

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To make informed decisions.

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Uh, you know, search doesn't really

sound sexy when you say it fast, right?

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But, uh, it, it, it is fascinating, like,

you know, the, the dimensions of like what

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can search provide on the project, right?

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So, um, you know, talking, you thinking

about like informed decisions, right?

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So, um, think about like, um, uh,

code compliance for instance, right?

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So like these days, uh, veterans

are retiring, people are

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struggling with knowledge, right?

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The corporate memory that they've,

you know, evolved and developed

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over the last couple of years.

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And you've got new, like you,

you've got those folks retiring.

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You've got new folk coming out of school.

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They're not experts in building goals.

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You know, that knowledge that was, has

evolved, you know, from best practices

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and knowhow throughout the years.

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It, it's hard to transfer over, right?

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And people are struggling, but what,

what is the role of AI in there?

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AI can actually help onboard new

staff members by transferring

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that knowledge, right?

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So you think like, you know, AI these

days has trained itself on, on national

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billing codes, uh, city regulations

and all those sort of things, right?

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So what if, what if AI was brought in, in

the context of, you know, you're, you're

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reviewing documents before they issued out

of construction and it's raising flags.

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Oh, you forgot about this.

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You forget about that, right?

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Uh, you're about to issue something

that, you know that is not

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compliant with the code right there.

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There's miss a, a missing

fire device here, right?

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That it doesn't meet the, the,

the fire protection, uh, uh,

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part of the building code, right?

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So think of it as, as a safety net, right?

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But also to help, uh, potentially

train new staff members that, you

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know, maybe there's an omission

on the, on the design, right?

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And they, they didn't see it, right?

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And now you're exposing yourself

for litigation down the road.

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So I, I think those are, are

practical examples of ai.

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Um, and I, I think like we're talking a

lot of AI about like the ability for AI

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to crunch and, you know, find information,

search, you know, the chat GPT out there,

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you know, Irv, we talks about that.

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But AI can actually also play

a role in collecting data.

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And that's exactly what

we did, uh, last year.

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So we, we released an AI system that

actually help collect, uh, emails.

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Uh, from your inbox.

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So it's kind of like a smart scanning

system that stands through your

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inbox, collect information, associate

them with the project automatically

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so that you've got a decision No,

where every project record exists.

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So again, when litigation kicks in

at the end of project, you've got all

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evidences, you can fight back and,

um, be efficient during the course

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of the project when searching for

information and, and, um, you know,

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uh, documents shared and and so on.

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Right?

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Randall Stevens: So that's, so

that primary use case is, is a, uh.

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A historical kind of use case

to crawl back through it.

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Are you all thinking about though, how,

how you can use that technology to be

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proactive so that there's not, well, I

guess in your example of, of training

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and coaching along the way, that's

a good example of, uh, trying to be

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proactive instead of reactive to, uh.

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Carl Veillette: Yeah, no, definitely.

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And I think we've got a

unique position because we're,

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we're sitting on top of 20.3

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million projects

delivered with our system.

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So that's a lot of data, right?

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And so the, the next step of our

journey is gonna be a Gentech

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ai, and then we're setting the

foundation for that Agen Z system.

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And so those agents would be

performing various different things.

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Like I was talking about code

compliance, I was, um, you know, we've

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got some other examples with W2 NRFI

responses, things like that, right?

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Um, but, uh, we also see like

a, some, some somewhat of

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an, a knowledge agent, right?

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That would just basically, uh, pop

up, you know, and then tell you, okay.

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Um, you're, you're designing a project

right now where you're, uh, you have a,

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a green roof part of the design, right?

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Uh, well, your firm has worked on, on,

on the green roof before, but it wasn't

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called reroof on this other project.

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It's a sedum roof.

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Right?

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But it's, it's the same, it's related,

it's semantic association, right?

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So it would start to bring other

examples of, of, of that design and

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bring additional documents inside, maybe

like that, that green roof, uh, ended

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up, uh, having a bunch of like, change

orders or issues or additional RFIs that

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came in because those information were

missing from the construction documents.

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Right?

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Um, so it would bring those, um, lesson

learned from other projects that we

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don't repeat the same mistakes, right?

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Um, so that's kind of like the, the

idea of where we're going with this.

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Um, and, uh, you know, we're, as I,

as I mentioned, we're, we're sending

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a lot, a lot, a lot of data, but

also it's the, it's the relationship

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between, you know, the, the data

itself and what happened, right?

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So we kind of need that golden thread to

be able to provide like those insights.

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So, uh, you know, the, you run into

issues after construction, right?

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So, um, if you don't have those.

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You know, data point and those

relationship between the items, right?

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Between that, um, that RFI or that

contract change item, you know,

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that's related to the, the design.

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It, it, it's hard to pre, it's

hard to predict, it's hard to

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provide those insights, right.

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But, uh, we do so.

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Randall Stevens: Carl, we were talking,

uh, uh, in a podcast that we just

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did recently with the guys from, uh,

twin company called Twin Master, and

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they've been working on digital twin.

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How to kind of, you know, kind of

broadly, how do you contextually

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tie all this information together?

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And one of the con one of the parts

of that conversation were, uh, around

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like code and, um, uh, you know,

I just wanna get your thoughts.

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It seems like that the, the, the

safe approach to that because nobody

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wants to claim that they've, that

they're, you know, giving you the

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right information is to, is to let the,

the customer bring that information.

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To the system and then, and then let

the system use that as a source of

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information to give information back,

as opposed to you saying, we've, you

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know, we've got that information.

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How, how do y'all think about that?

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'cause you know, as you know, code

compliance can be very specific to

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Carl Veillette: Yeah.

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Randall Stevens: jurisdictions.

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Carl Veillette: Yeah, actually, I,

I had a quick chat with, uh, the

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guys from UP Codes recently, and,

uh, they have a great platform.

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It's, uh, you know, building

a very extensive, uh, building

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code, uh, library, if I can say.

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Right.

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And they've got some AI to make

some searches in there, right.

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So, um, yeah, we, we had an interesting,

very interesting conversation, you

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know, about, um, some, some, some, um,

uh, some litigation that went on around

337

:

the, you know, the building codes and,

you know, should you be able to, uh,

338

:

you know, uh, train and build AI and

ownership of like the, the law, right?

339

:

And those sort of things.

340

:

But, um, um, you know, I, I think they

would make a, a good partner, like

341

:

for the things we have in the roadmap.

342

:

So, uh, you know, being able to tap

into their, the library of codes is not

343

:

something, I mean, trying to maintain a,

a code library is not something that is

344

:

new format's mission, that's for sure.

345

:

Um, but um, uh, you know, like

kind of, uh, leveraging that.

346

:

Um, to, to be able to apply it to

specific construction documents, you

347

:

know, visual artifacts and things

like that is something that we're

348

:

definitely, definitely exploring.

349

:

Right.

350

:

So, um, uh,

351

:

Randall Stevens: I haven't

352

:

Carl Veillette: playing different roles.

353

:

Randall Stevens: yeah, I haven't kept up

with, uh, with what their strategy was.

354

:

But are they, uh, you

know, stop top of my head.

355

:

It would seem like if you could end up.

356

:

You basically befriending the

jurisdictions because they're

357

:

the ones doing the code review.

358

:

So if you could go to the city and say,

actually we've got a system that will

359

:

not only take your requirements, but

check the incoming stuff coming in, and

360

:

that way you would at least have, you

361

:

Carl Veillette: Yeah, no, I think,

I, I would envision, you know, uh,

362

:

we could geek out on this, right?

363

:

But I would envision any integration

between Newforma 'cause we're

364

:

managing the workflow, right.

365

:

For issuing construction documents

in our system through that control.

366

:

Right.

367

:

I would imagine like their code tying

into like, we've got the project

368

:

address location and such, and then.

369

:

You know, I, I could easily envision

like there, you know, they would be

370

:

suggesting the, the applicable codes

and then would be, you know, kind

371

:

of building some AI around that to,

uh, uh, kind of analyze the, uh, you

372

:

know, the, the construction documents.

373

:

So, um, I mean, we, we own the process

already and they own the library, so it,

374

:

you know, it would be a great partnership.

375

:

Evan Troxel: Oh, I just want

376

:

Carl Veillette: my request of codes.

377

:

Evan Troxel: i, I want

to give a shout out.

378

:

I, I love, I love what O Up codes is

doing, and I also want to give a shout out

379

:

to another platform that's doing exactly

what you're talking about, because I don't

380

:

think UP Codes is doing that, but it's

called Archie Star and they're working

381

:

with specific jurisdictions because

it's very difficult to roll that out.

382

:

I've talked to other people who say

there's, in the US alone, there's

383

:

30,000 different jurisdictions that

have their own version of code adoption,

384

:

and so they have different years of

code adoption, different, you know,

385

:

different layers of code adoption.

386

:

And so it's super

difficult problem to solve.

387

:

Um, but Archie Star was doing that.

388

:

For that exact reason that

you just mentioned Carl.

389

:

It's like, and, and Randall too.

390

:

Like it's, it's, you're working with

the jurisdiction to say, we wanna

391

:

take the load off of you and we want

to help the design professionals

392

:

make this whole process go faster.

393

:

And I think you're right, Carl, like

pairing that with the project data,

394

:

like you're talking about, and, and

not just current project data, but

395

:

project data throughout the firm.

396

:

Right.

397

:

And, and so I guess my question is, is

with, with a, with a tool like Newforma

398

:

and, and kind of the, the depth to

which it goes, I think the hard part

399

:

for bigger firms has always been user

behavior to get, and, and I know you,

400

:

you released this AI tool to kind of mine

the information autonomously because why?

401

:

Because users suck at this.

402

:

Like users are, are notoriously bad

at archiving the data and archiving it

403

:

properly for their future selves because.

404

:

Every project is a new startup, right?

405

:

It's a new team, it's new constraints

for the site, it's a new project brief.

406

:

It's all of these things.

407

:

And so a lot of times we

just file bankruptcy at the

408

:

end of every project, right?

409

:

Not literally financially, but,

but literally as far as the

410

:

team goes and knowledge goes.

411

:

Um, and, and then we start fresh.

412

:

And so I, I'm curious how you guys

think about that and how it seems

413

:

like you're thinking, well, AI can

help with this, but I mean, you've,

414

:

you've seen this before, right?

415

:

That this kind of weak chain link

that is in the system, which is

416

:

the actual user in their behavior.

417

:

Carl Veillette: Yeah, so I, you know,

I, I, I gave the, the example with

418

:

the, you know, the green roof earlier.

419

:

Right.

420

:

Which is, you know, okay.

421

:

Trying to, to gather information.

422

:

I'm gonna give you another example of

like some of the things we, we have, um.

423

:

In the kitchen right now.

424

:

So like I was talking about search, right?

425

:

But when you've got like a new intern

coming in, in the, in the firm and

426

:

they are trying to figure out what

projects have been delivered by the

427

:

organization before their time, right?

428

:

It's not an easy thing.

429

:

You think about project

proposal right there.

430

:

There's an, there is a,

there is an RFP, right?

431

:

And then this in turn is gonna have to

kind of like do a little bit of research

432

:

exercise here, try to figure out how many

hospital projects they delivered before.

433

:

Where are the files, right?

434

:

That might be in a, you

know, A CRM somewhere.

435

:

They might be on the SharePoint.

436

:

They might exist in

multiple locations, right?

437

:

So one of the things that our

system's gonna be able to do,

438

:

because we're connecting with

those various data source, right?

439

:

We're tapping into SharePoint,

we're tapping in local fast servers.

440

:

We've got connectors for

all of those things, right?

441

:

You're gonna be able to just go in there

and ask, give me the list of the proposals

442

:

no matter where they live, right?

443

:

Um, for the, the hospital

projects that are above a

444

:

billion dollar in value, right?

445

:

And it will just give you other

proposals, okay, now help me

446

:

write a proposal for this.

447

:

Here's the template, right?

448

:

And then, you know, bring the

resume, bring the, you know, so,

449

:

so those things exist, right?

450

:

They're just like, it's sprawl, right?

451

:

It, it's, it's living in different silos.

452

:

It's all over the place.

453

:

There is no process for it.

454

:

Right?

455

:

Um, so it, it can really help, you know,

not just a, not just the training, but the

456

:

onboarding of those new staff member and

tap into that collective knowledge that's

457

:

been created, um, by the organization

for the, the last decade or so.

458

:

Right?

459

:

Um, so I, I just, I just think about

like how much time would be saved in

460

:

that context as they write that proposal.

461

:

Right?

462

:

Um, so there, there is, um.

463

:

Yeah, there's a startup in, uh, Toronto.

464

:

It's called, uh, work Orb.

465

:

That's what these guys are doing.

466

:

It's pretty cool.

467

:

And they have connection with CRMs.

468

:

And so, you know, I can see AI playing

a big role in both the onboarding, the

469

:

training, the quality control, adding

additional security nets, you know,

470

:

um, around more junior staff members.

471

:

Um, so that, that's a, that's a

very exciting time because it's

472

:

multidimensional, if I can say.

473

:

Right.

474

:

So, um, uh, I think, um, I think

there's gonna be like, the more we

475

:

think about it, the more we're gonna

find, like, uh, you use cases for

476

:

almost every activity of the project.

477

:

Um, you know, just, uh, just

think about legal, right?

478

:

I was talking about the last

phase of construction project

479

:

being litigation, right?

480

:

Um, it's imagine you're, imagine

you're reviewing documents, right?

481

:

And then something pops on the

side of the screen says, uh,

482

:

you've run into litigation.

483

:

Uh, about that, you know,

on previous project.

484

:

Um, so pay attention to this.

485

:

Like, here's like, you know, how

much it, you know, this, uh, this

486

:

created in terms of costs, you know,

on the project level, and it was aate

487

:

whether this or this was good, right?

488

:

Um, and, uh, you know, the, the

firm ended up, uh, losing the, the

489

:

case, you know, for this, right?

490

:

So, uh, you, you, you could also like,

you know, bring those type of insights

491

:

in the context of the project activities.

492

:

So that's, uh, that's very exciting.

493

:

Randall Stevens: Carl when you

494

:

Evan Troxel: just want to just real quick.

495

:

I just wanna say that would be

incredible because again, it's not

496

:

even the same team that was on that

other project where litigation happened

497

:

and the current team doesn't know.

498

:

Right.

499

:

And so that would be

incredible to be able to source

500

:

Carl Veillette: it's almost like

we're not building planes here, right?

501

:

It's almost pro every project is

different and then the project starts.

502

:

You've got a, a set of different teams.

503

:

They have to learn how

to dance together, right?

504

:

If I can say, right.

505

:

So.

506

:

It's almost always like a new kind of

learning process and how we're gonna

507

:

collaborate, which tool we're gonna use.

508

:

Um, so it, it's, um, it's a, it's

a very dynamic environment and that

509

:

creates a lot of like uncertainty on,

you know, the, the predictability of

510

:

the, of the project outcomes, right.

511

:

The schedule, the, you know, like the

deliverables, quality and such, right?

512

:

So, uh, I think it's, uh, you know, if

we can, you know, if we can alleviate

513

:

some of those through the use of ai.

514

:

Uh, and just thinking about like the

data intensive era that we're living

515

:

in right now, uh, if, you know, AI can

certainly help us stay on top of it.

516

:

Randall Stevens: a little bit of, I guess

maybe a little technical question just to

517

:

think, to hear how you think about this.

518

:

So, when, when you talk about feeding

this information into the data, you know.

519

:

Bringing the context windows and, and

all this information in do, can, can

520

:

you actually just dump everything in?

521

:

Or do you need to think about a kind

of specific additive process for the

522

:

information that you know is va you know?

523

:

Correct.

524

:

Because there can be bad, you

know, incorrect information.

525

:

I was just thinking when you were

talking about training, you know, a

526

:

a, a young professional that's just

come in if, if they have access, you

527

:

know, if you're pointing to everything

that's been back there, not everything

528

:

necessarily is even correct or good.

529

:

How, how, how are you

all thinking about that?

530

:

And, uh, 'cause it is,

it's obviously easy.

531

:

Everybody wants to, everybody

wants to just throw everything.

532

:

I just, uh, I just, uh, had, uh,

somebody on our team say that we've

533

:

got a customer that's wanting to

point at 12 million files to, you

534

:

know, dump into, into our system.

535

:

And it's

536

:

Evan Troxel: Because

more is better, right?

537

:

Randall Stevens: well.

538

:

It usually is, and, and you know,

anybody, you know, and I'm guilty of

539

:

it too, it's like, well, if you're

a technologist, you like to, you

540

:

know, you also like to break things.

541

:

Like, okay, I'm gonna push it to its

limits and, and see where the edges are.

542

:

But, uh, but just kind of back to,

you know, this idea that you all do

543

:

have all those projects and all that

dense information, you know, you're

544

:

talking about millions and millions

of files over time inside, across,

545

:

you know, thousands of projects.

546

:

You know, do, do you have to take, uh,

if you're the CIO sitting in one of those

547

:

operations, do you have to begin to think

about plucking out, you know, specific

548

:

information to feed into the system?

549

:

Or do you think that the AI tools

that we're either have in front of

550

:

us or near term are gonna be able

to help us discern what was good and

551

:

bad information or help to do that?

552

:

What's, what's your thoughts there?

553

:

Carl Veillette: I mean, I, I, the way

I think about it is that, you know.

554

:

Garbage and garbage out, right?

555

:

So, I mean, uh, just dumping

a, a lot of unstructured data

556

:

to when AI is quite risky.

557

:

So it's like at the end it's just

think about a specific use case, right?

558

:

Like, I mean, let's say, um, you're

building AI that's gonna be, uh, helping

559

:

GCs on site, uh, find information

from construction drawings, right?

560

:

Okay.

561

:

Uh, well, construction drawings, there

maybe multiple revisions of it, right?

562

:

So if you don't have a good

structure, it's easy for AI to

563

:

get confused and give you the, the

answer from the previous revision.

564

:

And then all of a sudden you've got

an issue with, you know, the, the,

565

:

the concrete pour on site, right?

566

:

Uh, didn't put the right

rebar, you know, whatever.

567

:

Right?

568

:

So I do feel like the, the structure

of the information, uh, that

569

:

you're feeding ai, uh, with right?

570

:

Is, is very important.

571

:

Um, I almost think about the

project as a, there's like.

572

:

The, the way we're approaching

things is everything is times

573

:

stamped in our system, right?

574

:

So often, you know, where AI is gonna

get confused, it's, it's gonna take a

575

:

superseded set of drawings, or it's gonna

be taking conflicting information from

576

:

an email and it's gonna be superseding,

you know, with, you know, the answer from

577

:

the data's construction documents, right?

578

:

And things like that.

579

:

So everything being time timestamped, you

can actually create like a, a timeline on

580

:

the project, like a conceptual timeline.

581

:

So, uh, you know, this is how we've

structure the information so that

582

:

we ensure that, you know, competing

information, um, you know, is, is,

583

:

is not confusing the ai, right?

584

:

Um, so I, I think, you know,

having a good data structure behind

585

:

the scenes is very important.

586

:

Uh, you know, if you take, uh, if we,

if we, if we took our project archive

587

:

as is and we just dumped that to a

chat GPT, and we start to ask Chachi PD

588

:

questions, it's most likely it's gonna.

589

:

Provide you wrong answers.

590

:

It's gonna use a, a wrong document

or it's gonna use an email where

591

:

some information wasn't official,

but it was shared in there.

592

:

Um, so, so at the, at the end, I think

it's very important to, to look into

593

:

the, the backend system that's created

to manage the data in the first place, to

594

:

collect the information, to structure it.

595

:

Randall Stevens: I, I

would, I would agree.

596

:

But I also, I've been trying to train

myself because, you know, I, I've been

597

:

in, you know, doing technology now for

35 years and I'm fond now, you know,

598

:

the, the old saying that really good

software is indistinguishable for magic.

599

:

It should really be magical.

600

:

And we're watching, you know, I'm

using some of these tools and I'm like,

601

:

I, it, it's, it is really amazing.

602

:

Evan Troxel: magic than it's ever been.

603

:

Randall Stevens: Well, and it's magic.

604

:

And I've got a, uh, well we had, uh,

we had a friend of mine who's, who's

605

:

one of the world's, you know, renowned

machine learning and computer vision.

606

:

Guys who, you know, keyed

CCV this, this past year.

607

:

He told me, I don't, I don't, you

know, when he tells me he doesn't

608

:

understand it, I have a little solace

in like, 'cause usually, you know, if

609

:

it's technology, I wanna learn enough

about it to at least have a construct

610

:

to say, okay, I can kind of understand

what it's doing and how it works.

611

:

I think we're in the middle of, of this,

um, you know, of a new wave where we

612

:

can't even wrap our heads around it.

613

:

So what I was gonna

say about the, I agree.

614

:

You know, my, my tendency is to say

you want good structured data to do

615

:

this, but I also say, well, if I gave

that to a human, what, how would they

616

:

decide what was relevant or what wasn't?

617

:

And if a human can do it.

618

:

Why do we think that we can't

have an AI agent doing that too?

619

:

So, you know, it may not be today,

but maybe it's just around the

620

:

corner, then it's like, maybe it

can, 'cause I mean, I think we have

621

:

to ask ourselves if a human could do

it, why couldn't the AI engine do it?

622

:

'cause I think that's what wave that

we're in right now of this, you know,

623

:

fast rolling, uh, kind of updates and

changes to the way this technology works.

624

:

So anyway, I'll just

throw that out there like,

625

:

Carl Veillette: Yeah, no.

626

:

It's funny because like, you know,

I, I hear a lot these days, uh,

627

:

across all industries, right?

628

:

Like.

629

:

There, there is a, there is a sentiment

that AI is gonna disrupt, like large

630

:

enterprise softwares like Salesforce

or Oracle or things like that.

631

:

And, you know, you can see it,

you know, you're gonna, the stock

632

:

market and you know, the, these

stocks are crashing right now.

633

:

It's just, so I, I'm like, this is

really strange because AI is not gonna

634

:

disrupt enterprise software because

those software have data and, you know,

635

:

like AI is only as good as data that

it's, it is being trained on, right?

636

:

So I think where the value is, is data.

637

:

It's not the AI itself.

638

:

Um, now AI is a mechanism to crunch the

data to make something with it, right?

639

:

But it, you know, I guess like the

world we live in at your FOMO is

640

:

like, we have this data, right?

641

:

So like, you know, we're gonna be building

like things with AI on top of that, right?

642

:

But at, at the end, this is,

you know, this is, this is where

643

:

everything converge, right?

644

:

If, if you have data,

there's value, right?

645

:

So you can, you can do something with

it, but if you don't have data, then.

646

:

You're just gonna be like,

the flavor of the data.

647

:

It's like there's so

many of those AI tools.

648

:

Like at some point you may ask

cloud AI to build an app that

649

:

crunch, you know, documents for,

for you, for code compliance, right?

650

:

And we may not be that

far away from this, right?

651

:

So people are gonna be able to

create their own AI flavor, but if

652

:

you don't have the data, then you

know, you, you can't, you can't

653

:

train any, you cannot train on it.

654

:

So that's, that's where

the value is in my mind.

655

:

It's, you know, who owns the data, right?

656

:

Randall Stevens: So let's talk,

uh, let's talk text stack survey.

657

:

What, uh, I, you know, I was looking

back, uh, when we put that thing

658

:

together, we had, uh, you know,

there was over 400, you know, kind of

659

:

applications that we kind of knew about.

660

:

And, and literally it was go, let's

just do a, see how many people click

661

:

and say, I'm using that, I'm using that,

we're using that, we're using that.

662

:

And literally just to,

uh, take an inventory.

663

:

There was a little bit of nuance

to it in that for each one that

664

:

they were using, we set, we ask

on a scale of one to five, is it.

665

:

Deep penetration, you know, deep

in the workflow or, or just here

666

:

and maybe not being used very much.

667

:

And that was at least a, a, a, you

could read that in a lot of different

668

:

ways, but it actually, as we pulled

that data, you could do quite a bit of

669

:

kind of analysis across not only the

tool stack, but that kind of sentiment

670

:

for how deeply rooted that things are.

671

:

What I was gonna say though is we

broke those apps up, those two, 400

672

:

apps up into 20 different categories.

673

:

I think Newforma ended up in

the same category as we put our

674

:

Carl Veillette: That's really strange.

675

:

'cause when I, I think about

our respective platforms, we

676

:

don't do the same thing at

677

:

Randall Stevens: Yeah,

completely different.

678

:

And, and that's what, you know, I, I, my

my excuse is it was the first year we did

679

:

it and we were just trying to figure out

like, okay, how would we, uh, break this

680

:

up into some, you know, broad categories.

681

:

There's probably some nuance that we

need to add the next time we do it right,

682

:

which would be, so that was gonna be my

first question is what category, what.

683

:

What, what kind of broad area would

you call the, where Newforma lives

684

:

and what other applications kind of

live in that same, like is Autodesk

685

:

part of Autodesk platform would be

considered along those same workflows or,

686

:

Carl Veillette: yeah, so we play, we

play in a diff it's a, we, we got a, a,

687

:

a strange position because we're, um,

we're playing on different fronts, right?

688

:

But we're not doing the actual work.

689

:

So let me explain that a little bit.

690

:

So, in your survey, for instance, there

was a communication category, right?

691

:

We're not.

692

:

We're not Microsoft teams,

we're not Outlook, but we're

693

:

connecting with those, right?

694

:

So we're like, when it, when it comes

to email management and those sort of

695

:

things, we've got some, some of the best

in class features for that, but we're not

696

:

Outlook, we're not distributing emails.

697

:

Randall Stevens: not the core application.

698

:

You're the connective

tissue between those,

699

:

Carl Veillette: Exactly.

700

:

And that's, you know, we're

talking about, we're talking

701

:

about the golden thread, right?

702

:

And I think that's kind of the role

we're, we're playing, we're, we call that

703

:

project information management, right?

704

:

And we've been calling it this way for 20

years and it's almost like we're category

705

:

makers in this space because there

isn't, you know, other solutions that,

706

:

that, that do that to the extent we do.

707

:

So we connect with ERP system.

708

:

We're not an ERP system, but

we're gonna be enabling, just like

709

:

knowledge architecture does, right?

710

:

Feeding from like ERP systems to

create projects so that people

711

:

don't have to do it twice, right?

712

:

So the information

flows, it's being reused.

713

:

We recycle information.

714

:

Same

715

:

Randall Stevens: we do the,

716

:

Carl Veillette: administration,

717

:

Randall Stevens: we do the same.

718

:

We have the same problem.

719

:

We, we call our, you know, by default we

call it a content management platform,

720

:

but usually most people think of that.

721

:

They think that we're not gonna be the.

722

:

Take your that information, but really

what we do is connect in very much the

723

:

same way you all do to where those pieces

of information are and basically fuse it

724

:

together into one, you know, kind of view.

725

:

Uh, is is where our value add is.

726

:

So what do we call, what do we

what, what should we call that next?

727

:

Next time we do it, is

728

:

Carl Veillette: yeah.

729

:

I, I, I, I, I

730

:

Randall Stevens: bridge, bridges,

connective tissue, bridges, or,

731

:

Carl Veillette: project

information management.

732

:

Randall Stevens: yep.

733

:

Carl Veillette: there, there's

a few players out there that,

734

:

you know, use that umbrella, uh,

term for, for similar things.

735

:

Right.

736

:

Um, you know, I would, I would argue

that we, we also, uh, do a lot of like

737

:

construction administration, right?

738

:

We've got a lot features around that.

739

:

So.

740

:

Uh, that might be an overlap with

Autodesk, for instance, right?

741

:

We're certainly not competing

with Pole Core 'cause they

742

:

do construction management.

743

:

So they're from the general

contractor side of things.

744

:

We take it from the designer,

uh, perspective, right?

745

:

Uh, so we actually have a good partnership

and connectors, you know, with Pole core.

746

:

Um, so, uh, people love it in general.

747

:

So we were playing on that front too.

748

:

Um, think about bim, right?

749

:

We're not the clash detection software.

750

:

We're not, you know, um, we're not

an authoring system either, right?

751

:

But we've got some pretty unique,

you know, uh, issue tracking

752

:

features and functionality.

753

:

So I, I guess we do play in the

BIM coordination kind of space.

754

:

So you look, there's a lot

of softwares in that space.

755

:

Um, so again, with information

and management in mind,

756

:

we're not detecting clashes.

757

:

We're the bridge between, you

know, those in celebrity software

758

:

to the ordering system, right?

759

:

So we relay the information,

we make it accountable.

760

:

We create these three, the

audit trail, um, we make the,

761

:

the workflow more efficient.

762

:

But, um, at the end, we're not a differing

system either, but we're, we're also

763

:

living there as add-ons and, and add-ins.

764

:

Right?

765

:

So we're, we're kind of

connecting the dots, right.

766

:

So that that's the role we wanna play.

767

:

Evan Troxel: think it's interesting that

you're explaining all these different

768

:

places that you play in, and I bet most

of your users know you for one or two of

769

:

those things, not all of those things.

770

:

And so like, I think this kind of

comes back to the idea of what a

771

:

brand is known as or known for is

really up to the customers and not

772

:

the company that makes it itself.

773

:

Because if you asked me what new formal

was and you asked Randall what new formal

774

:

was, we might have two different answers.

775

:

And somehow you might want

to try to reconcile all that.

776

:

I don't know, maybe you don't,

but, but like when you ask that

777

:

question, Randall, I kind of think

you want to go to the users and

778

:

say, what would you put Newforma in?

779

:

And you're gonna get a

lot of answers, right?

780

:

And then it's the job of

try to reconcile it, but.

781

:

It's like when you say, you know,

veils content management, you know,

782

:

and, and what would be interesting to

see if people think of it as something

783

:

different than just content management.

784

:

Randall Stevens: Yeah.

785

:

As we were, you know, and really,

you know, we were, uh, we're not

786

:

professional surveyors and we

were kind of doing this on a,

787

:

Evan Troxel: You're not dodge

788

:

Randall Stevens: last fall.

789

:

Yeah.

790

:

Yeah.

791

:

So there wasn't a ton

of resources to do it.

792

:

So, no, I, you know, we,

we were, we did our best.

793

:

Uh, but, uh, in hindsight, you know.

794

:

By trying to split this stuff up.

795

:

We, we were trying to put each application

in one of the 20 broad categories, and

796

:

in reality, like you're saying, Carl,

it's like, oh shit, you, you could've

797

:

been a little bit over here, and a

little bit over there, and a little bit

798

:

over there, which then if somebody had

taken the surveys in that section, it

799

:

might've been like, you know, they were

probably just responding to what they did.

800

:

We did have a, we did have the ability

to write in, you know, so if we were

801

:

a glaring mistake, they would write in

like, how come you didn't have X, Y, Z?

802

:

But, uh, but it also could have just been

like, Hey, we, we didn't just, we're just

803

:

kinda responding to what you put in front

of us as opposed to really thinking about

804

:

if I ask you without any of that in front

of you, you might've said, oh yeah, we use

805

:

Newforma and Procore for doing that work,

or, you know, those parts of the flow.

806

:

So I think that's gonna be a challenge

for us to think about how to do it.

807

:

But, you know, like anything, it's

almost, you have to put a stake

808

:

in the ground and say, I, I wanna

create these kind of buckets.

809

:

And it doesn't mean that they're perfect,

but at least gives a conversation starter.

810

:

Carl Veillette: it, it's like

what's a collaboration software?

811

:

Is that a characteristic of, of, of a

workflow or is that an electoral category?

812

:

Right.

813

:

I mean it's, you can ask yourself

that question 'cause like almost

814

:

everybody these days, like

have collaboration features.

815

:

And I mean, as soon as you've got

multi users collaborating together,

816

:

is that called collaboration?

817

:

Like what's the true definition

818

:

Randall Stevens: You can

share a link and avail.

819

:

So is that

820

:

Carl Veillette: yeah.

821

:

Is that collaboration?

822

:

Randall Stevens: Yeah.

823

:

Uh, just, uh, you know, part of, there

was actually two categories that kind

824

:

of related specifically to what we

do, and I think to where what you do.

825

:

And, and that was, you know, basically

where are the, where are the primary,

826

:

I'll just call it data storage location.

827

:

So what does your file system look like?

828

:

Are you using SharePoint?

829

:

Are you on a, you know, a c, c, or,

you know, there's places where the,

830

:

there's literally file storage.

831

:

And then there was another category

that was more like content management

832

:

or that, I'll say a layer above it,

which is, we use this, but some people

833

:

would, would consider SharePoint that.

834

:

Right?

835

:

They think of it as like a website.

836

:

You know, back to what you were saying,

Evan, I think it's like to the user,

837

:

that's not a storage location, that's

like a, a website, you know, because of

838

:

the way the interface to SharePoint is.

839

:

But yeah, like, you know, I

always took Remind people is

840

:

just one drive back there.

841

:

It's like, it's just, it's just files

being stored somewhere and then you've

842

:

got some skin on the front of it.

843

:

But what we did find from, from, you know,

if you take both of those, uh, sections

844

:

of the survey across, you know that across

the, you know, a hundred plus people that

845

:

responded to it, they averaged about a

dozen of those locations in use, which

846

:

part of what we were wanting to show was.

847

:

There is data sprawl.

848

:

You know, we're, we're not only

producing more information,

849

:

it's living in more places.

850

:

And now the end user has to know, is

it over there or is it over there?

851

:

Is it over there?

852

:

And, you know, I think that that's

becoming a bigger and bigger part,

853

:

which is what part of the problem

that we're trying to tackle.

854

:

And I think you all, you know,

operate in the same space, which

855

:

is like, look, this, this data is

living in a lot of systems of record.

856

:

How do we, how do we fuse

it all back together?

857

:

Carl Veillette: And I was looking

at avail, and it looks like you

858

:

guys are doing it pretty much like

the, the same, uh, thing that we do.

859

:

So we, we don't really wanna

host like all the data, right?

860

:

So we're kind of like connecting with

those various different data sources.

861

:

So we connect SharePoint, a, c,

c file, like local file server.

862

:

Um, we, we have Ignite, you know, and,

and those, you know, other players like

863

:

Panzura and ADI and, and such, right?

864

:

So we, we're not a storage platform.

865

:

We're not an EDMS, but we're

providing a view into those things.

866

:

So, uh, as information is being shared

outside the organization, there are

867

:

proper retention policies for files.

868

:

You can populate document control

so that you can meet standards

869

:

like ISO 19 60, 50, right?

870

:

And things like that.

871

:

So kind of like ensuring the

compliance and the government's

872

:

layer on, on top of those various

different locations that exist.

873

:

Um, so like we're kind of trying

to place Switzerland in a way

874

:

that we're neutral, right?

875

:

And we're, we're gonna be connecting with

the various data source using the project.

876

:

And, you know, you think

about infrastructure projects,

877

:

people are gonna have files on

ProjectWise, well guess what?

878

:

We connect with ProjectWise.

879

:

Um, they're gonna have some

maybe files on that same project.

880

:

They're gonna be Revit

files who sit on acc, right?

881

:

Um, so when you're using all those

systems independently and you're

882

:

trying to manage like issuance and

things like that and get proper

883

:

audit trail, it's quite hard, right?

884

:

Um.

885

:

Randall Stevens: I know you, uh, you're

a relative newcomer to Newforma, but, uh,

886

:

how, how long do you think that, that,

that term governance has been in use?

887

:

In just in general?

888

:

'cause I would think new form.

889

:

If it was gonna be talked about, you

all probably would've been one of

890

:

the early ones to to, to at least

conceptually talk about what it means

891

:

to govern kind of information across

any, any idea of when that came into

892

:

the vernacular in this industry.

893

:

Carl Veillette: Yeah.

894

:

No, I, I, I think, you know, uh, we,

as we saw the industry evolve, and

895

:

I think, you know, AI and cloud has

certainly like, um, I would say woken

896

:

up a lot of people on governance, right?

897

:

Uh, and, uh, I think the

proliferation of tools has made

898

:

it, uh, a thing in the industry.

899

:

I, I'd say, you know, for the

last five years I've seen.

900

:

It and CIOs and organizations trying to

get a hold of their data and understand

901

:

how the data flows in the organization.

902

:

And, um, I think it has

become a, a top concern.

903

:

And I guess like the industry

report says so, uh, as well, right?

904

:

That that's becoming

an increasing problem.

905

:

Um, and especially because

data is gold, right?

906

:

So, and in these, uh, in that

AI era that we're living in now.

907

:

So, uh, I think a lot of people are

getting, um, other concern about where

908

:

the, the data is and concern about their

ability to tap into their own data and,

909

:

and how they're connecting it and how

they're consuming it right down the road.

910

:

So, um, organizations are, are

looking at strategies for that,

911

:

uh, in our timeframe, right?

912

:

I think, you know, you guys do it, you

know, you may look at the, you know,

913

:

co the content like li libraries,

like details, things like that, right?

914

:

So it's, it's really cool, uh,

to be able to reuse information

915

:

like that coming from various data

source and, and leverage that for.

916

:

Uh, for, for making like ongoing

projects more efficient, right.

917

:

Uh, that's just, you know, one

example of, of, um, uh, what

918

:

technology can do these days.

919

:

Um, um, but yeah, I'd say, you know,

governance in general, um, in your format.

920

:

I think we, we, we see also that, that

there's a lot of regulations, you think

921

:

about the Building Safety Act in the UK

and those sort of things which require,

922

:

um, uh, decision logs and things like

that to be, uh, documented, right?

923

:

So, uh, regulations are also

pushing organizations to get better

924

:

governance in place, uh, not just

from a series standpoint, right?

925

:

But from, um, uh, project records, right?

926

:

Uh, you think about contracts, right?

927

:

You've got retention policies for data,

certain contracts, uh, you know, it's been

928

:

the case for the last two decades, right?

929

:

Uh, more actually.

930

:

Um, so how do you make, like, how

as you grow your tech stack, can you

931

:

really, truly get, turn into a contract

where you've got project retention

932

:

policies and after X amount of years,

like you gotta delete the data.

933

:

Like, it's, it's not

934

:

Randall Stevens: Yeah, that's tough

935

:

Carl Veillette: It, it's tough, right?

936

:

Randall Stevens: Yeah, I've had

those conversations because a lot

937

:

of, you know, you take something like

Miro, which is, you know, being used

938

:

quite a bit, but it's like it is.

939

:

Cloud first.

940

:

It, you know, you're in,

you're in that database.

941

:

So what does it mean to archive that?

942

:

How do, how do you archive it?

943

:

What if that, you know, you can

keep your account and they may keep

944

:

your boards around, but what if they

aren't around in five or 10 years?

945

:

How, what does it mean to

archive that kind of information?

946

:

And I think it's a,

947

:

Carl Veillette: Yeah.

948

:

I.

949

:

Randall Stevens: think

anybody's figured it out

950

:

Carl Veillette: Yeah, I'm just

thinking like it is fun facts, right?

951

:

Uh, like we used to have backups for

seven years worth of data, right?

952

:

And recently we had, we had to shorten the

period because seven years is too much.

953

:

I mean, if, uh, you know, if a, if a

customer has retention policies in place

954

:

for project, maybe the, their policy

internally is to get rid of the data

955

:

after the retention policy is set, right?

956

:

Maybe that's because they're worried

about the growing storage costs.

957

:

Uh, maybe they just don't wanna

have the information when litigation

958

:

takes in beyond that date.

959

:

And, and I've seen examples of that.

960

:

Evan Troxel: Sure.

961

:

Carl Veillette: Yeah.

962

:

So, so some want, want, want

to keep it as a, you know, as a

963

:

backup to be able to fight back.

964

:

But some they just don't wanna

have the data in their hands.

965

:

So, uh, yeah.

966

:

So we ended up like, uh,

realigning our, our backup.

967

:

Um, uh, linked, you know, for

retention policies, purpose, right?

968

:

So, you know, it's easy with

I, Microsoft and AWS these

969

:

days to have like, just change.

970

:

Okay?

971

:

Now I've got 10 years

worth of retention there.

972

:

And it's gonna, because you have it,

it means you have to provide it, right?

973

:

So if you know it's kicks in

again, like the court's gonna

974

:

ask for it, well guess what?

975

:

It's in there.

976

:

Randall Stevens: yeah.

977

:

There's a litigation site.

978

:

It reminds me though, uh, of a, uh,

years ago I had a friend that worked.

979

:

It was a, he was an engineer

and, uh, worked at a, at a

980

:

pretty good sized company, and

he told me the story about that.

981

:

Uh, you know, people started

just in meetings saying, you

982

:

know, uh, well, you said this.

983

:

You know, six months ago, so they started

to have this policy that said if it

984

:

was more, if it was said more than 30

days ago, it's like it, it's as if it

985

:

didn't happen because things change.

986

:

So it's like you can't point back

to something I said a year ago.

987

:

The context may have changed or, you know,

988

:

Evan Troxel: I mean, this gets back to

your question earlier, Randall, like,

989

:

like with with, if somebody throws 12

million pieces of data into a, into

990

:

a thing that they want to cut cross

sections through and get insights

991

:

from, it's the same question, right?

992

:

Like a lot of that is old information.

993

:

And so like, what I like to think of

that as is like what the concept is.

994

:

What I think should happen is information

should have an expiration date.

995

:

Just like laws should

have an expiration date.

996

:

Right?

997

:

Or at least let's revisit them.

998

:

But no, it's just additive, right?

999

:

And, and the old stuff's in

there and it's just data.

:

00:51:31,071 --> 00:51:35,211

And so to qualify that I think is

kind of the, it's a very difficult

:

00:51:35,211 --> 00:51:40,131

thing to do, but, but same goes for,

what did you say in the meeting?

:

00:51:40,461 --> 00:51:43,371

30 days plus ago doesn't apply anymore.

:

00:51:43,671 --> 00:51:47,691

Well, chances are that old data,

you know, it might have a different

:

00:51:47,691 --> 00:51:51,411

usefulness, but it may not answer

the question that's being asked.

:

00:51:51,411 --> 00:51:56,661

And so that, that whole context is so

incredibly difficult, like challenging

:

00:51:56,661 --> 00:51:59,361

to address, but also extremely important.

:

00:51:59,556 --> 00:52:04,716

Randall Stevens: I will have to, uh,

I've been working on a, um, a concept.

:

00:52:04,716 --> 00:52:05,706

I'm writing a white paper.

:

00:52:05,706 --> 00:52:09,636

I, I'd love to share that with you,

Carl, uh, and, and get your feedback.

:

00:52:09,636 --> 00:52:12,336

But I've been talking about

it as capital resources.

:

00:52:12,366 --> 00:52:15,071

You know, from a, from a business

standpoint, it's like you want

:

00:52:15,071 --> 00:52:18,366

to have, you wanna have your

information, you want it to have value.

:

00:52:18,366 --> 00:52:21,816

So thinking about it in terms

of like capital resources, but

:

00:52:22,206 --> 00:52:23,376

part of the thinking is that.

:

00:52:24,786 --> 00:52:27,396

As you create these pieces of

information in whatever form they end

:

00:52:27,396 --> 00:52:33,006

up living on, we're missing the right

contextual wrappers of data around that.

:

00:52:33,666 --> 00:52:37,416

Maybe it's an expiration date,

maybe there's certain parts of

:

00:52:37,416 --> 00:52:41,136

this that can have value over time,

but other parts not over time.

:

00:52:41,286 --> 00:52:45,636

And I think, you know, I think what

our challenge is gonna be as, as the

:

00:52:45,636 --> 00:52:49,536

people developing these technologies is

those systems have never been in place.

:

00:52:49,536 --> 00:52:52,926

You know, back to the tech stack

survey, you got a hundred to 150

:

00:52:52,926 --> 00:52:54,906

applications, and you know what?

:

00:52:54,906 --> 00:52:57,396

They're pumping out information every day.

:

00:52:57,516 --> 00:53:02,496

And it's like, but we're missing,

you know, I, I've been saying for

:

00:53:02,496 --> 00:53:07,686

years we've been missing, um, the

idea of a, of a content router.

:

00:53:08,076 --> 00:53:12,336

Like we think we, we have technology,

our networks have routers, and there's

:

00:53:12,336 --> 00:53:14,046

rules about that information as it goes.

:

00:53:14,076 --> 00:53:15,756

Those bits go across and

where they should go.

:

00:53:15,756 --> 00:53:21,426

But we've never had, we've never had this

concept of, as we produce information from

:

00:53:21,426 --> 00:53:23,586

all these systems, there's nothing that.

:

00:53:24,096 --> 00:53:27,456

One says where it should go,

but I think you can extend that.

:

00:53:27,546 --> 00:53:31,536

Not only where do I wanna store it,

but what is the contextual wrapper of

:

00:53:31,536 --> 00:53:37,176

metadata around that that lets me now

know what to do with that in the future.

:

00:53:37,176 --> 00:53:41,046

It's all, you know, it's buried

inside the files, which complicates.

:

00:53:41,046 --> 00:53:42,696

It's like, so anyway, I

:

00:53:42,801 --> 00:53:45,231

Evan Troxel: Especially when it's

in a proprietary file format.

:

00:53:45,231 --> 00:53:45,531

Right.

:

00:53:45,531 --> 00:53:48,711

So you may not even have

that application any longer.

:

00:53:49,176 --> 00:53:54,111

I, I just, just an anecdote from a, a,

a digital practice leader at a firm.

:

00:53:54,141 --> 00:53:57,856

I mean, they, they talk about this idea

of, you know, data sprawl that you've

:

00:53:57,856 --> 00:53:59,931

brought up a few times on the show and.

:

00:54:01,291 --> 00:54:04,866

The, the practice is

like, we can't control it.

:

00:54:04,866 --> 00:54:08,826

There's too many people, everybody's

got their way of doing it.

:

00:54:08,856 --> 00:54:12,186

They all have their own

preferences, which brings so much

:

00:54:12,606 --> 00:54:14,856

chaos to this process, right?

:

00:54:15,366 --> 00:54:21,546

And so they just said, ultimately we've

decided that it is the project managers.

:

00:54:22,266 --> 00:54:26,856

It's, it's their charge at the end of

a project to make sure that happens.

:

00:54:27,186 --> 00:54:30,066

And my next question obviously

is, well, does that happen

:

00:54:31,746 --> 00:54:32,376

Randall Stevens: Of course not.

:

00:54:32,796 --> 00:54:35,946

Evan Troxel: Because like, what

hooks does a digital practice

:

00:54:35,946 --> 00:54:37,566

have into a project manager?

:

00:54:37,626 --> 00:54:38,436

Like Zero?

:

00:54:38,496 --> 00:54:39,666

They don't have any, right?

:

00:54:39,666 --> 00:54:43,416

And so there's no responsibility

between those two.

:

00:54:43,911 --> 00:54:45,831

Different entities inside of a firm.

:

00:54:46,041 --> 00:54:47,031

It's a standard.

:

00:54:47,031 --> 00:54:50,001

And if the firm says it,

but does it actually happen?

:

00:54:50,391 --> 00:54:52,851

Um, and, and the answer's most likely no.

:

00:54:52,851 --> 00:54:54,411

Like it probably still doesn't happen.

:

00:54:54,411 --> 00:54:55,911

But it's because why?

:

00:54:55,911 --> 00:54:57,561

Because what if litigation?

:

00:54:57,561 --> 00:55:00,171

What if we want to use that

information on the next project?

:

00:55:00,171 --> 00:55:01,191

What if, what if, what if?

:

00:55:01,401 --> 00:55:03,711

And of course, like that

would all be useful.

:

00:55:03,771 --> 00:55:07,911

And at the same time, like there's so much

information and at the end of the project,

:

00:55:08,241 --> 00:55:12,801

do they actually build in the time for

somebody to be able to do what seems

:

00:55:12,801 --> 00:55:14,661

to me like kind of a monumental task?

:

00:55:15,171 --> 00:55:16,581

I doubt it, right?

:

00:55:16,581 --> 00:55:17,751

So it's a tough

:

00:55:17,831 --> 00:55:20,196

Carl Veillette: and, and

I've heard that many times.

:

00:55:20,196 --> 00:55:24,846

And you know, we've got customers

knocking on our door, you know.

:

00:55:25,161 --> 00:55:27,261

With that exact same problem right now.

:

00:55:27,411 --> 00:55:29,301

Now their litigation goes up, right.

:

00:55:29,301 --> 00:55:30,921

They've got more lawsuits.

:

00:55:30,981 --> 00:55:32,151

They, they lose the case.

:

00:55:32,181 --> 00:55:33,561

'cause they don't have the evidences.

:

00:55:33,561 --> 00:55:33,981

Right.

:

00:55:34,581 --> 00:55:37,431

And so they start to look

into is oh 9,001, right?

:

00:55:37,431 --> 00:55:41,001

They start to look at structured

their processes and all of that.

:

00:55:41,001 --> 00:55:44,766

And then, you know, like, you know,

at the end what they're like, what

:

00:55:44,766 --> 00:55:47,871

they're, what they're seeking is like,

is there something like an information

:

00:55:47,871 --> 00:55:52,341

backbone or a platform that can help

us restructure our processes, how we

:

00:55:52,341 --> 00:55:54,411

collect data, how we archive data.

:

00:55:54,801 --> 00:55:58,641

And, and that's often, you

know, like how we, um, how we

:

00:55:58,641 --> 00:55:59,901

get in touch with those folks.

:

00:55:59,901 --> 00:56:00,201

Right?

:

00:56:00,531 --> 00:56:03,321

We, we got some customers that

were referred to us by lawyers,

:

00:56:03,921 --> 00:56:05,121

which is kind of strange.

:

00:56:05,271 --> 00:56:06,141

It's a funny story.

:

00:56:06,881 --> 00:56:07,661

Randall Stevens: Put 'em on the payroll.

:

00:56:08,211 --> 00:56:08,501

Yeah.

:

00:56:08,586 --> 00:56:10,986

I was gonna, uh, when I,

when I first started here.

:

00:56:11,826 --> 00:56:14,406

Started working on, you

know, what became a veil.

:

00:56:14,916 --> 00:56:17,586

I was always trying to like

quantify or how to figure out

:

00:56:17,586 --> 00:56:21,516

how to describe the scale of the

problem of this, you know, data.

:

00:56:21,576 --> 00:56:25,656

And one of the things that I've said

for years that I was like, if you

:

00:56:25,656 --> 00:56:30,666

think about every piece of software

that we're all using, you know, we're

:

00:56:30,666 --> 00:56:32,136

all like information workers now.

:

00:56:32,136 --> 00:56:34,326

We're all sitting behind a

keyboard with some piece of

:

00:56:34,326 --> 00:56:35,706

software producing information.

:

00:56:36,456 --> 00:56:40,536

And the way I would always describe

the scale of the problem, at least

:

00:56:40,536 --> 00:56:44,196

a part of the problem is every

piece of software that has a file

:

00:56:44,226 --> 00:56:49,806

open and a file saved dialogue

requires a human to now do something.

:

00:56:50,406 --> 00:56:51,216

Where is it?

:

00:56:51,816 --> 00:56:54,306

And now that I've produced

it, where should I put it?

:

00:56:54,396 --> 00:56:58,416

And that's what got me thinking about

this idea of like, like a route.

:

00:56:58,476 --> 00:57:02,076

Like you would never ask your router where

those bits should go on your network.

:

00:57:02,076 --> 00:57:02,316

So I'm

:

00:57:02,676 --> 00:57:03,666

Evan Troxel: Look at a phone, right?

:

00:57:03,666 --> 00:57:06,276

I mean, at the phone there's barely a file

:

00:57:06,696 --> 00:57:09,156

Randall Stevens: It was the first

one to, you know, that it was, that

:

00:57:09,156 --> 00:57:13,176

was our first little bit of a taste

and go, you know, Google Drive was

:

00:57:13,176 --> 00:57:14,946

at least, but even there, right.

:

00:57:14,946 --> 00:57:19,446

I still, we still have basically folders

and places that we put stuff in those

:

00:57:19,686 --> 00:57:20,376

Evan Troxel: but they hide it.

:

00:57:20,496 --> 00:57:21,486

They obscure that stuff

:

00:57:21,546 --> 00:57:21,966

Randall Stevens: Yeah.

:

00:57:21,966 --> 00:57:23,526

It, it's obscured behind there.

:

00:57:23,526 --> 00:57:27,696

But I, I, you know, I do, I'll

keep, i'll, I'm, we're trying

:

00:57:27,696 --> 00:57:28,956

to solve some of these problems.

:

00:57:28,956 --> 00:57:32,676

I'm sure Carl, y'all are in the same,

but I think this, we gotta keep pushing

:

00:57:32,676 --> 00:57:36,966

on this, uh, that, that there needs to

be this metadata, you know, I don't know

:

00:57:36,966 --> 00:57:41,731

if those can become standards or, uh,

you know, defacto standards around how

:

00:57:41,796 --> 00:57:45,846

to put the right contextual information

wrappers on this stuff so that these

:

00:57:45,846 --> 00:57:49,146

systems, you know, this is part of what

we were trying to do with this tech stack

:

00:57:49,146 --> 00:57:54,156

survey too, which was at the Confluence

events to talk about these systems.

:

00:57:54,711 --> 00:57:57,951

We've got so many of them now and they're

not necessarily talking to each other.

:

00:57:57,951 --> 00:58:01,041

So I'm glad to hear, you know, you all

continue to work on your own connectors

:

00:58:01,041 --> 00:58:04,881

and that We did a, we did a little

webinar yesterday where I had some,

:

00:58:05,001 --> 00:58:06,471

some people on talking about this.

:

00:58:06,471 --> 00:58:12,021

And you know, part of it was, you know,

framed it as you're using these, you've

:

00:58:12,021 --> 00:58:17,721

decided to use these tools to accomplish

certain goals and tasks, and then you've

:

00:58:17,721 --> 00:58:20,181

got another tool to do another task.

:

00:58:20,421 --> 00:58:24,081

And then you have to find a third tool

that's trying to help you solve the

:

00:58:24,081 --> 00:58:26,781

problem of bridging, because those

two systems don't talk to each other.

:

00:58:26,781 --> 00:58:31,431

So can we get back to like, can we just

have better exchange of information

:

00:58:31,431 --> 00:58:35,811

between the core platforms and

systems that are in use and then maybe

:

00:58:35,811 --> 00:58:41,121

that'll absolve the need for another

tool that trying to fill those gaps.

:

00:58:41,121 --> 00:58:43,701

So I was referring to it as a seam.

:

00:58:43,701 --> 00:58:44,211

It's like.

:

00:58:44,766 --> 00:58:48,366

You can go, you, you, you choose the

tool 'cause you think it has value and

:

00:58:48,366 --> 00:58:52,686

it's gonna bring value, but it breaks

down at the seams where we can't get

:

00:58:52,686 --> 00:58:54,006

these things talking to each other.

:

00:58:54,006 --> 00:58:57,846

And that's where you pay the, you pay

the cost of like, uh, the friction

:

00:58:57,846 --> 00:59:01,836

of, of getting that data moved back

and forth, or knowing that if it's

:

00:59:01,836 --> 00:59:03,426

the right version of the data that

:

00:59:03,491 --> 00:59:03,821

Carl Veillette: Yeah.

:

00:59:04,091 --> 00:59:07,811

And, and it's the APIs and it's a various

system and it's also the native files.

:

00:59:08,321 --> 00:59:09,941

We're just talking about that, right?

:

00:59:09,941 --> 00:59:13,901

Like you think about like native files

format and that create additional

:

00:59:13,901 --> 00:59:16,031

barriers for consuming information, right?

:

00:59:16,866 --> 00:59:20,136

Uh, that's the reason I, I'm allergic

to, uh, you know, native file formats

:

00:59:20,136 --> 00:59:23,166

in a way that, you know, we've always

created, like, created like, like,

:

00:59:23,166 --> 00:59:27,756

um, you know, bridges and created

open APIs, and we've got those

:

00:59:27,876 --> 00:59:29,976

indexer with our secret sauce, right?

:

00:59:30,036 --> 00:59:33,216

Uh, which is basically our way to

index native file formats of the

:

00:59:33,216 --> 00:59:36,426

industry, like CAD files, Revit

files, things like that, right?

:

00:59:36,426 --> 00:59:41,646

So we, we kind of index all of that, but

the, the goal there is, has always been

:

00:59:41,766 --> 00:59:43,896

to free the data from the files, right?

:

00:59:44,286 --> 00:59:45,156

Um,

:

00:59:45,346 --> 00:59:46,346

Randall Stevens: 'em in

a database somewhere.

:

00:59:46,626 --> 00:59:47,556

Carl Veillette: yeah, exactly.

:

00:59:47,616 --> 00:59:51,936

So that, that's kind of like the, you

know, the reason why people call us the

:

00:59:51,936 --> 00:59:55,866

Google search of construction projects

often, like, you know, I, I would say if

:

00:59:55,866 --> 00:59:59,886

you ask most of our Newforma customers,

the common thing that would come up

:

00:59:59,886 --> 01:00:01,776

is newforma as a search engine, right?

:

01:00:01,956 --> 01:00:06,966

And so that, that's kind of a,

it's a strange category to be in

:

01:00:07,206 --> 01:00:08,976

as a software vendor in this space.

:

01:00:09,141 --> 01:00:10,941

Evan Troxel: That's what goes

on the survey, Randall, they,

:

01:00:10,941 --> 01:00:12,141

they go in the search category,

:

01:00:12,171 --> 01:00:12,681

Randall Stevens: Right, right.

:

01:00:12,696 --> 01:00:13,086

Carl Veillette: yeah.

:

01:00:13,671 --> 01:00:14,331

Evan Troxel: search engine.

:

01:00:14,673 --> 01:00:14,841

Carl Veillette: Yeah.

:

01:00:14,901 --> 01:00:17,331

Randall Stevens: Well, it'll be

interesting and you know, I'll, I'll

:

01:00:17,331 --> 01:00:21,171

use, you know, your all's help and

anybody's help that wants to help to

:

01:00:21,291 --> 01:00:25,911

think through what's the better, you

know, and like I said, it's, you can

:

01:00:25,911 --> 01:00:29,451

live in multiple categories, so it kind

of hard do you put everybody in every

:

01:00:29,451 --> 01:00:32,901

category that they might be in That way

you're at least collecting, you know,

:

01:00:32,901 --> 01:00:34,401

or they thought of, of being in that.

:

01:00:34,491 --> 01:00:38,181

Uh, but, uh, anyway, we'll, we'll,

we will figure it out as we go.

:

01:00:38,211 --> 01:00:41,781

I think it'll be, uh, I'm, look, I'm

already looking forward to, at the

:

01:00:41,781 --> 01:00:44,961

end of this year doing the survey

again, just so we'll have trend date.

:

01:00:44,961 --> 01:00:48,981

I wanna see, you know, hey, how, how

much of this stuff shifting or moving and

:

01:00:49,341 --> 01:00:53,301

where are we seeing movement, uh, across

these applications that are in the stack?

:

01:00:53,301 --> 01:00:55,701

But, um, good.

:

01:00:55,851 --> 01:00:57,981

Well, it's good to catch up.

:

01:00:58,131 --> 01:01:01,251

Uh, may, maybe we'll make

this an annual thing every

:

01:01:01,266 --> 01:01:02,706

Carl Veillette: Yeah, let's do it.

:

01:01:03,036 --> 01:01:05,886

Randall Stevens: We, we jump on the

car, we'll put it on the calendar

:

01:01:05,886 --> 01:01:09,876

and, uh, I wish we would get out

to more, uh, shows, you know, uh,

:

01:01:09,936 --> 01:01:11,466

just to be able to see each other.

:

01:01:11,466 --> 01:01:12,126

'cause obviously,

:

01:01:12,246 --> 01:01:13,776

Carl Veillette: Yeah, we should,

yeah, we should catch up.

:

01:01:13,896 --> 01:01:17,496

So we've got our, uh, new former world

event coming up in the beginning of May.

:

01:01:17,496 --> 01:01:21,186

So if you guys, uh, wanna swing

by, it's gonna be in Florida

:

01:01:21,186 --> 01:01:22,566

where you know, you're welcome.

:

01:01:22,656 --> 01:01:26,166

Uh, we also have a, a bunch

of exhibitors and all of that.

:

01:01:26,166 --> 01:01:28,176

So if you're ever interested in that, uh,

:

01:01:28,446 --> 01:01:30,306

Randall Stevens: yeah, yeah,

I'd love to check on that.

:

01:01:30,306 --> 01:01:34,026

And then I'd be remiss to, to not

remind, I don't know when this podcast

:

01:01:34,026 --> 01:01:38,526

will be out, but we're doing two one

day Confluence events, one in Seattle,

:

01:01:38,556 --> 01:01:42,636

April 2nd, and in Chicago on May 20th.

:

01:01:43,146 --> 01:01:47,406

So, uh, if anybody's watching this and,

and, and it's before those dates go

:

01:01:47,406 --> 01:01:50,136

check out, uh, the Confluence website.

:

01:01:50,631 --> 01:01:53,481

Uh, page and, uh, get

your info on those events.

:

01:01:53,481 --> 01:01:54,441

But those are also good.

:

01:01:54,441 --> 01:01:58,311

We just try to do those regionally and

get, uh, you know, people together for a

:

01:01:58,311 --> 01:02:02,331

day and have these kinds of conversations

and then drink a beer afterwards

:

01:02:02,331 --> 01:02:03,801

and, and socialize a little bit.

:

01:02:03,801 --> 01:02:07,131

But, uh, anyway, but, uh,

good, good to have you back on.

:

01:02:07,131 --> 01:02:12,051

Glad to hear, uh, the update on the

progress and, uh, and, and helping to,

:

01:02:12,531 --> 01:02:14,091

this is gonna be a never ending battle.

:

01:02:14,091 --> 01:02:18,051

So, uh, but I, I do think that the

frequency that we can talk about

:

01:02:18,051 --> 01:02:22,161

some of this because of the way that

these, especially these AI tools are

:

01:02:22,551 --> 01:02:27,891

just rapidly, I, you know, I go back

and forth from being like incredibly

:

01:02:27,891 --> 01:02:29,841

excited and incredibly nervous.

:

01:02:29,841 --> 01:02:30,321

Like you said.

:

01:02:30,321 --> 01:02:34,911

I think the, uh, you know, if you do watch

the stock market, like you said earlier,

:

01:02:34,911 --> 01:02:38,571

the enterprise stock enterprise software

companies have been taking a beating.

:

01:02:38,931 --> 01:02:42,561

But I heard, uh, I was, I think

it was Brad Gerstner or one of

:

01:02:42,561 --> 01:02:48,261

the finance guys saying that the

way to, a way to look at that is.

:

01:02:48,696 --> 01:02:52,536

That, you know, the stock market,

you're always paying future value.

:

01:02:52,776 --> 01:02:57,096

It's not so much that the companies aren't

performing, it's that we can't look as far

:

01:02:57,096 --> 01:03:00,906

ahead as we used to be able to look ahead

because things are changing pretty quick.

:

01:03:00,906 --> 01:03:02,706

So there's a discount, right?

:

01:03:02,706 --> 01:03:06,936

On those, on those by saying, I, I used

to think that you were good for 10 years.

:

01:03:07,116 --> 01:03:10,326

Now I'm only, you know, confident

that, that it's good for five.

:

01:03:10,566 --> 01:03:13,416

So there's like a, a discount to

the stock price that's happening,

:

01:03:13,416 --> 01:03:16,776

which I thought was a, you know,

at least a, for those of us in the

:

01:03:17,036 --> 01:03:18,051

Carl Veillette: it's time to load.

:

01:03:18,396 --> 01:03:21,906

Randall Stevens: like, like a comforting

to know that you're not just gonna

:

01:03:21,906 --> 01:03:23,526

get, you know, disrupted overnight.

:

01:03:23,526 --> 01:03:27,516

But, um, we could probably have a

whole nother, uh, call on, you know,

:

01:03:27,516 --> 01:03:31,896

vibe coding and, and what does it

mean to, to, to write stuff on top

:

01:03:31,896 --> 01:03:34,086

for those of us that have APIs?

:

01:03:34,506 --> 01:03:35,586

What's that gonna do?

:

01:03:35,586 --> 01:03:40,956

And, you know, I, uh, I tend to think

it's like this software sprawl is only

:

01:03:40,956 --> 01:03:42,816

gonna get worse before it gets better.

:

01:03:42,876 --> 01:03:45,456

And, uh, anyway, there's a

whole conversation about.

:

01:03:46,611 --> 01:03:50,751

Whether or not being able to for, for

individuals, even within these firms, to

:

01:03:50,751 --> 01:03:53,721

start writing tools on top to be those.

:

01:03:53,721 --> 01:03:58,941

But within the organization now all of a

sudden it's like now you're managing even

:

01:03:58,941 --> 01:04:03,441

more pieces of software and even more

maybe places where this information's,

:

01:04:03,771 --> 01:04:07,851

you know, it, all those tools are

gonna start producing more information.

:

01:04:07,851 --> 01:04:11,931

So it might be this

exponential explosion of data

:

01:04:12,036 --> 01:04:12,326

Carl Veillette: Yeah.

:

01:04:12,531 --> 01:04:16,941

Randall Stevens: that's now being

generated and without these, I'll

:

01:04:16,941 --> 01:04:20,931

just keep beating on it without

these contextual wrappers where that

:

01:04:20,931 --> 01:04:24,171

stuff can be governed and where's

the audit trails on all this.

:

01:04:24,171 --> 01:04:26,391

And um, I don't know.

:

01:04:26,391 --> 01:04:27,891

It's gonna be an interesting next

:

01:04:27,906 --> 01:04:32,346

Carl Veillette: the, the, the era where

we had to procure software is gone.

:

01:04:32,376 --> 01:04:35,886

So now, like people are making

software and that's a pretty scary,

:

01:04:36,276 --> 01:04:36,566

Randall Stevens: Yeah.

:

01:04:36,571 --> 01:04:37,041

Yeah.

:

01:04:37,236 --> 01:04:38,736

Carl Veillette: moment

to be, to live, right?

:

01:04:38,736 --> 01:04:42,306

Because, uh, you know, information

can be anywhere, right?

:

01:04:42,306 --> 01:04:48,636

And, uh, it's, um, it's generated in

different ways and you've got, you

:

01:04:48,636 --> 01:04:51,576

know, everybody in your organization

works, you know, with their own

:

01:04:51,576 --> 01:04:52,956

tools that they've created, right?

:

01:04:52,956 --> 01:04:56,436

So it's a, yeah, it's gonna be,

uh, it's gonna be fun to watch

:

01:04:56,436 --> 01:04:57,696

the next 12 months for sure,

:

01:04:58,581 --> 01:04:59,361

Randall Stevens: So for all the,

:

01:04:59,406 --> 01:05:00,396

Carl Veillette: more accessible, right.

:

01:05:00,827 --> 01:05:03,651

Randall Stevens: for for all the

other, you know, the, the tech geeks

:

01:05:03,651 --> 01:05:07,161

and the people that are developing

these tools, uh, let, maybe we

:

01:05:07,161 --> 01:05:10,881

can just kind of wrap this up with

how, how much are you all using?

:

01:05:11,451 --> 01:05:13,881

Uh, are you using AI to write code now?

:

01:05:13,971 --> 01:05:15,051

Are you using Cursor?

:

01:05:15,051 --> 01:05:16,581

Are you using Clot or what?

:

01:05:16,641 --> 01:05:18,261

What's going on inside new format from

:

01:05:18,306 --> 01:05:22,626

Carl Veillette: We do, uh, we do a

lot, and I, I would say that it has

:

01:05:22,626 --> 01:05:28,956

made some software we use, uh, for,

uh, uh, security scanning even more

:

01:05:28,956 --> 01:05:32,946

important, uh, because now, you know,

they're generating, generating that

:

01:05:32,946 --> 01:05:36,426

code on the fly with, with ai, right?

:

01:05:36,426 --> 01:05:41,676

And, and, uh, AI is generating,

it is validating the code, right?

:

01:05:41,736 --> 01:05:43,596

So you've got less humans

in the loop, right?

:

01:05:43,596 --> 01:05:46,416

So how do you make sure that you

comply with the, you know, the

:

01:05:46,416 --> 01:05:49,056

security, uh, controls and all of that?

:

01:05:49,056 --> 01:05:54,486

So we, uh, yeah, we had this

stack, you know, with more security

:

01:05:54,486 --> 01:05:56,316

vulnerabilities getting software.

:

01:05:56,406 --> 01:05:58,026

Um, so we.

:

01:05:58,851 --> 01:06:00,381

We had some, we have more.

:

01:06:01,191 --> 01:06:03,471

And so that, that's how

we're gonna go after this.

:

01:06:03,471 --> 01:06:05,061

'cause like, we're not

gonna be slowing down.

:

01:06:05,061 --> 01:06:07,641

Like AI generated code is

gonna keep, keep going.

:

01:06:07,671 --> 01:06:08,001

Right?

:

01:06:08,001 --> 01:06:12,861

So, um, um, so that's, um,

that's changing practices for

:

01:06:12,861 --> 01:06:14,301

sure and how we build software.

:

01:06:14,811 --> 01:06:18,531

Um, we kind of have to, because, you

know, speed is everything, right?

:

01:06:18,531 --> 01:06:25,041

So, um, we're, we're not, we're not, uh,

reluctant in leveraging those technologies

:

01:06:25,041 --> 01:06:28,281

because if we don't, you know, someone's

gonna be going faster and we do, right?

:

01:06:28,311 --> 01:06:32,721

So, um, so I think it's, uh, you

know, I think the same applies

:

01:06:32,721 --> 01:06:34,041

for the easy space, right?

:

01:06:34,041 --> 01:06:37,311

Because if gonna an architecture

and non-engineering firm right there

:

01:06:37,311 --> 01:06:42,291

that's using AI and someone's kind

of fancy ai, well I bet like this,

:

01:06:42,291 --> 01:06:45,591

this organization gonna be struggling

with productivity, you know, and, and

:

01:06:45,591 --> 01:06:48,741

competitiveness with, with the other

that's using AI down the road, right?

:

01:06:48,741 --> 01:06:52,041

So it, it's uh, you know, people

should absolutely start to embrace

:

01:06:52,041 --> 01:06:56,721

that and, um, think about how this

is gonna change your organization.

:

01:06:56,721 --> 01:06:57,021

So.

:

01:06:58,104 --> 01:06:59,604

Randall Stevens: Always

a good conversation.

:

01:06:59,634 --> 01:07:02,574

Like I said, I wish, uh, we

could, uh, get together, you

:

01:07:02,574 --> 01:07:03,413

know, in some of these events.

:

01:07:03,413 --> 01:07:05,514

So I'll, I'll, I'll check

out y'all's May event.

:

01:07:05,514 --> 01:07:05,784

That would be a.

:

01:07:06,774 --> 01:07:10,674

Maybe a good opportunity,

Evan, good to see you as usual,

:

01:07:11,004 --> 01:07:11,304

Evan Troxel: Yep.

:

01:07:11,419 --> 01:07:11,839

Thanks

:

01:07:11,874 --> 01:07:15,114

Randall Stevens: Carl will will see

you if, if not sooner, next February.

:

01:07:17,034 --> 01:07:17,694

Thanks for joining.

:

01:07:17,989 --> 01:07:18,329

Carl Veillette: All right.

:

01:07:18,534 --> 01:07:18,954

Cheers.

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About the Podcast

Confluence
The director's commentary track for AEC industry software development.
The Confluence podcast is the director's commentary track for AEC industry software. Go behind the scenes with us to learn how and why decisions were made in the creation of your favorite software for the architecture, engineering, and construction industries.

It's a collaboration between Randall Stevens of AVAIL and Evan Troxel of TRXL.

About your hosts

Evan Troxel

Profile picture for Evan Troxel
An industry-leading design and technology expert with a passion for connecting people, Evan is a licensed architect in California and is most well known for his podcasts that focus on the AEC industry.

He has over 25 years of experience in the practice and technology in the architectural profession working with large teams to deliver large public projects for clients. He now brings his experiences together on the Archispeak and TRXL podcasts, and now on the Confluence podcast.

Randall Stevens

Profile picture for Randall Stevens
An AEC industry veteran with 25 years of software development, and sales and management experience, Randall offers a unique combination of expertise in software and graphics technology — coupled with a background and degree in architecture.

In 1991 he founded ArchVision, a software firm specializing in 3D graphics, specifically Rich Photorealistic Content (RPC). Through ArchVision, Randall has built an extensive network with the industry’s leading experts, architectural firms, and visualization software companies, which led him to product development of the AVAIL platform.