This AI Does in Minutes What Takes Companies Months

Why does it take enterprises 12 to 16 months to ship an AI model?

And that led us to uh moving into the

space to create a [music] company to do

this cuz we ran into it again and again

and again where they either had no

process or they had so much process it

would take them 12 to 16 months to get a

model out the door.

Breaking it down.

A fresh idea.

Hey everyone and welcome to Built This

Week, the podcast where we share what

we're building, how we're building it,

and what it means for the world of AI

and startups. I'm Sam Nadler, co-founder

here at Rise Labs. And each and every

week, I'm joined by my co-host, my

business partner, friend Jordan Metser.

How you doing today, Jordan?

Yo, Sam, how are you? Another exciting

week. So much going on in all the AI

laboratories, new products dropping, new

realtime models. So, it's been another

crazy week. Stock market's crazy, too.

So, just lots going on all around, but

uh super excited for today's guest,

actually.

And [clears throat] with that, I'd like

to introduce our guest, Jim, the CTO of

Model OP. Um, and Jim, if you don't

mind, give us a brief intro of who you

are, kind of where you came from, and

then we'll get into a little bit of of

seeing a demo of Model Oop.

Who founded ModelOp, and who are its customers?

Uh, sure. Yeah, I'm the chief technology

officer of uh Model Op. I'm uh one of

the founders. Uh basically where we came

from was uh actually myself and the

current CEO of uh Model Op. Uh we're

working in in the big data space before

this and uh working with customers who

are actually training AI models and that

was over eight years ago. So uh working

with real situations and we saw a gap um

in the space and that led us to actually

developing this product uh and we've

been at it for over eight years now um

and evolving as you said it's constantly

something new on the horizon. So

evolving the product to keep up uh with

the capabilities and uh what's actually

out there and being actually consumed

within uh large enterprises. I know all

of our clients are Fortune 500. Uh so

we're working with uh what's actually

happening and being consumed within

those organizations.

What does an AI governance platform like ModelOp do?

Amazing. Well, I would love to see a

quick demo just so I can see kind of how

the product works. But if you don't

mind, Jordan's a more sophisticated

listener user than I am. Give me if you

could Eli5 like explain like I'm five

the core use case for model op and then

let's see the demo.

Okay. Absolutely. Uh the core usage is

basically you need if you're going to

bring an AI solution out to uh the

public especially as an enterprise I

mean many of our customers are banks um

you know financial institutions

healthcare institutions uh you better

have some idea of the potential risk to

your business to your patients uh to

your customers etc out there and we

basically provide methodologies for how

you assess that risk how you uh do

ongoing monitoring of the models

themselves and how they're performing.

for your business. Uh what are the costs

of the business of that use case? You

know, that's becoming a real concern

now. Uh understanding token usage, it

really stacks up. So is it providing the

value uh as opposed to the cost and many

of these different processes and helping

to automate that process and drive it

forward so it doesn't become a burden on

the organization and they can take these

great ideas out because kind of sucks if

it takes a year to get your idea out

there. It's already expired in the

space. So, how do we accelerate that and

get these AI solutions to market in a in

a safe manner?

Amazing. Uh, I would love to see a demo.

How can companies track AI token costs per business use case?

And while you're pulling something up, I

you know, my understanding is, you know,

at least one feature of model op is that

you're from the token usage use case,

you're not only looking at tokage usage

from the user, which I think, you know,

a lot of us are trying to understand,

but also from almost the business unit

or what's it trying to achieve? Did did

I understand that correctly?

Yes, that's correct. uh you know we can

look at there's the traditional view of

looking at it from like for instance uh

uh the model consumption overall but

that's not really useful to the business

or from the user that's not really

useful to the business because a user

using cloud code to do something and

develop some software is a very

different use case than the user using

the same model to perhaps you know

answer customer questions or these kinds

of things. So we need to actually

understand what is the solution costing

me. So for instance like if I have a

customer support chatbot uh that's out

there I want to know uh what that

particular business use case is uh

costing me as a business um on there and

also any potential like issues with it.

So for instance here we're integrating

with uh Kong gateway to pull in uh

information about uh guardrails it's

imposed and blocked specific requests on

there. So understanding that over time

as to how many exceptions you're

getting, but then also what is the

solution costing me? Because if it's

just like a general HR query tool and

it's costing me thousands per month,

that might not be worth it. But if it's

a a critical item like customer care,

support, etc., and it's saving me

things, that's a very different cost

equation. So being able to have that

kind of information at your fingertips,

so to speak, uh to to say yes, this is

worth the spend. Does that make sense?

It does. It does. And what am I looking

at here? Uh so this is actually uh

tracking uh a business use case a

hypothetical one here in this case a

customer support chatbot uh that you

know our we actually uh track all kinds

of information about it you know the

information about the business use case

but also uh you know potentially the

implementation and stuff which we'll

show an example of in a second but this

is tracking the token usage uh actually

in this case imported from Kong gateway

u we uh integrate with them as a partner

uh we cano So track it directly. Uh

we'll see that with some of uh our

aentic use case examples. Um so that we

can see that that cost over time of of

what it's actually uh providing as well

as an overall dashboard of just you know

all of the information about the

different use cases, models, etc. and

what they are are consuming in tokens.

So we can tie it all together and get

that view.

How does ModelOp find shadow AI across enterprise platforms?

Okay. And my follow-up question is like

we've we've covered uh token usage and I

and I think that makes a lot of sense on

on how they're evaluating, you know,

implementing or delivering um AI

solutions to their customer base or even

internally. You know, there's these

other features, security, compliance,

how does model op help in those areas?

Yeah. Well, let me give you a quick uh

demonstration of how we drive that

process that'll kind of tie all that

together. So for instance uh what we're

going to do is we have these concepts of

basically life cycles that are

configurable that can enforce different

policies but they drive the process

through rather than being something that

passively sits back. So we're going to

walk through a really really simple

example today where we actually use our

discovery mechanism to query our

enterprise uh platforms find new models

that aren't registered in the system yet

so called shadow AI. Um, and then we're

going to start a process to say, "Hey,

we discovered one. These are what you

got to do to make sure, you know, it's

up to compliance standards in this case

on that." So, what we're going to start

with is actually our discovery service

where we're going to go out, we can

query different uh platforms that are

out there that are used. We're not

interested particularly in uh desktop

level because the reality is everything

will have AI in the long run. Um, you

know, uh, basically Windows has AI built

in. you use a particular API, it may

have AI backing that you don't know and

etc. But what we really are interested

in from our standpoint is the ability to

actually discover. Yeah, we've got these

enterprise services such as uh agent

365, Amazon Quick, Amazon Bedrock, etc.

What is actually being run out there?

You know, Microsoft co-pilots, there's

people developing these within the

enterprises all the time on their own

and we don't always know about it. we

don't, you know, know what's being done

and that's a liability to the business

because those enterprise level resources

actually can have access to proprietary

information. So, we want to make sure

that they're so-called brought into the

fold, you know, automatically brought

into the inventory. As I said, it's

called the shadow AI problem. Some

people look at from a desktop

perspective. we're looking at from an

enterprise resource level perspective of

these real uh platforms that people are

utilizing and utilizing a multiplicity

of them. So it can be very hard to track

down all of these different usages that

are occurring out there. So our systems

doing right now is actually uh uh

scanning for uh those and and

reconciling it against known items

within the uh inventory on that. And

then what's going to happen is when we

bring them in, uh, when we bring a new

one in, we're going to start this

process where we go through and reach

out to people to go get documentation,

use our agents to autocomplete forms

based on the documentation they have,

our own AI and agents, and then actually

look at applicable policies based upon

those answers on that kind of things on

there. So now when we come back to the

screen, we're going to see it's actually

found a ton on purpose. I didn't

register a ton of stuff. So we found a

ton of stuff sitting out there. All

these co-pilots, agent cores, uh you

know, Amazon quick implementations, etc.

Can AI agents automate AI compliance paperwork like EU AI Act reviews?

that are out there and we make it really

easy to bring them into our system. For

the demo sake, we're just going to bring

in uh one. Today we're going to bring in

a healthcare advisor chatbot that's

written in uh Foundry. And for the

demo's pers uh perspective, we're going

to add a little tag on there if I can

type correctly today. Uh there we go.

And then it's this easy to bring it into

our inventory. So we're going to do

that, bring it into the system. Um, and

when we do that, that life cycle is

actually going to start. It's going to

start driving the process, moving it

forward, helping us get it out there. So

now it's telling us within the

inventory, uh, when we go back and look

at it and we look at our implementation,

we're going to see that Microsoft

Foundry uh, healthcare chatbot and it's

using GPD5 Mini. So, it brought both in

automatically into our system and

bringing that. And you can see it's

telling us we got to upload some

documents. So, we'll go ahead and

actually uh add the documentation to

that. And then when we do that, um if we

can just uh add our use case. So, we've

got our uh use case form. This is just

written plain English. And we get it in

there. And then we go back to uh there

and go ahead and approve and say yes,

we've added the documentation. Actually,

approve that.

on that completed. There we go. See, it

detected the fact that we actually

uploaded the document. So now in the

background, uh what's actually happening

is it's going out and reading that

document using our agents. We have a

full uh agent workforce behind here that

uh is implemented utilizing um MCP

tools, a contextual database to actually

store information about what's in our

inventory, etc. uh so that we have all

that information at hand. By doing this,

we're going to rapidly accelerate the

process. What it's going to do is this

chatbot here, um if I actually close it,

has a full form that actually needs to

be filled out. So, you can see here

where right now we don't have any of the

form information. Well, the agent has

actually just completed its work um on

that. So, uh let me sorry, close that

down. You can see now that we actually

go out. We've used the agent uh chatbot

to do that. And uh you'll see now where

it's actually filled out a confuser

configurable form with all the

information for us where it's going to

be used. All of that kind of information

done for us by the agents. So we're

accelerating the process of getting this

out here. And you know we can actually

go ahead and move it forward. And when

we do that now, it's going to take those

form submissions that were automatically

there and go out and continue the

process forward and we're going to see

now we're there's compliance violations

like we're comparing against the EU AI

act and telling us what we need to do.

So we can actually drive these processes

and make it go forward. And I'm going to

end there so we don't have to uh go

through all the form submissions etc and

that kind of things. But I think you get

the flavor where we're trying to make it

so you can be compliant with all these

regulations, all these things you have

to do. your internal policies etc. but

making it painless. You know that we

just walk through what would take

honestly months worth of work in some

organizations to pull that together and

we can do accelerate that into minutes.

Using AI to use AI maybe

correct [laughter]

helping to accelerate keep human in the

loop still but yes

and [laughter] and Jim like how how did

Why do companies need an inventory of their AI models?

you guys get into this kind of where

where did this thing start and how did

you get get it to where it's at today?

Obviously we've seen so much adoption

and change in AI in and of itself. So,

how did it kind of where did you guys

start with the product and and get it to

where it's at now with kind of what

we've seen in the market, whatnot?

Yeah. Well, as I I mentioned, we were

working uh I was doing consulting at the

time um with uh some very large

manufacturers

uh banks etc on that and we were it was

in the day of the big data you know the

data lakes all that stuff on that kind

of things and we were helping them uh to

establish uh these kinds of uh huge data

lakes for use specifically for training

AI models. um they wanted to basically,

you know, one was a manufacturer and

there's hundreds of models they had that

ran on their individual machines that

were critical to their business. Um when

working with one of those uh people, I

actually ran into a situation where the

only place where that model was being

trained that was being deployed into

customer sites was on a guy's desktop.

He's just right there. If if this person

got hit by a bus, god forbid, tomorrow,

they would have no idea how to reproduce

it. They had no tracking on it. They

didn't know what went in it. They didn't

know versioning. And I saw this again

and again and again where you know there

there was just nothing behind it. Nobody

had any insight into what was being

deployed. Uh so you know we we realized

there needs to be a different process

for that um to actually track this

information uh validate the information

make sure you're compliant with any

regulations all this stuff. So we began

What happens when a model your company uses is shut down or retired?

to think about what that would look

like. And that led us to uh moving into

the space to create a company to do this

because we ran into it again and again

and again where they either had no

process or they had so much process it

would take them 12 to 16 months to get a

model out the door. So this became a

real world situation when uh you know

Fable rolled out um and a bunch of

different people started using it. All

of a sudden it was banned out out of the

country. We had customers who now are

scrambling to say okay which solutions

are are using fable because it's now

shut down. So everything it's using is

broken. Uh we have to we have to go roll

this back to a different level of model.

So by providing this kind of information

about the actual implementations and

being able to look in inverse and say

yeah you know fable's being used by

these 16 things they are able to track

it down very quickly. Likewise you know

that's an extreme situation where a

model is completely shut down. you can

also imagine is yeah a new model comes

out well who's using you know GPT5 mini

for example you know oh it's going into

retirement what who's using it how do I

upgrade how do I ensure that it still

works correctly all of these kinds of

things so you have to know what you have

what is using what to even begin that

exploration is super critical when we

get into these rapidly evolving models

okay that's awesome

if you're a founder or running a

business you already know how powerful

video content can be for growing your

presence. That's where video comes in.

You just show up, share your

perspective, and we handle the rest.

How can uh people get a hold of you or

find out more or learn more?

Uh modelop.com is uh the easiest way. Uh

we have all of our information. We have

demos. As I said, you've only seen a

tiny tiny bit of the functionality. Uh

we have uh more information up there and

that kind of things as well as contact

forms etc.

Great. Um Sam, shall we do the news?

Do pet owners need an AI-powered pet feeder?

Let's do the news. This is, you know,

kind of a silly one. Jim Pet Libros new

AI powered feeder is a gamecher for

multicat homes. I got an opportunity to

uh scan this really quick. I think it

detects when your your food is low. Um,

so it can detect uh when your food is

low. You know, obviously I think this is

uh probably helpful to a multi- petet

owner. Um, you know, do you have four

cats?

Oh, wow. You're a number one user here.

[laughter]

And trust me, my cats already let me

know when the food's running low. That's

not That's not a big issue.

We don't need AI for that.

Yeah, that's the question. are, you

know, do are are there is everyone

trying to force AI into everything when

maybe it doesn't, you know, really need

to exist. You know, I have pets. It's

it's it's pretty simple to know when to

feed them. It's pretty routine. Uh

secondly, they ask for it. Um what do

you think of this product?

Well, as I said, it I haven't used the

product. There may be advantages and

disadvantages I'm not aware of, but I

mean, it does seem a little overkill to

me uh for the price point ratio of Yeah.

my cats will let me know. I can buy a a

kind of a self-feeding dish that just

dumps the food out from gravity for

about $10. So, um, and it's been working

fine for us so far. Uh, but I can see

maybe if you have specific dietary

restrictions or needs or something like

that, perhaps something like this might

be appropriate. But, uh, I would

probably not be a consumer at this time.

Jordan, I know you're more of a dog guy.

Um, but I guess this would be relevant

for Jordan has three dogs, so that could

be obviously relevant for dogs as well.

Would you buy something like this?

Um,

I mean, I guess like feeding the dogs is

a daily routine in my house. It's a

twice daily routine times three dogs, so

that's six times. Um, sure, I would love

for the dogs to get fed automatically.

Do automatic features work well with

dogs? Not really. Um, I think like Jim

said, there are some like, you know,

kind of like, you know, kind of

gravityfed ones on the market, but uh,

but yeah, I'm waiting for my Optimus

robot to feed the dogs and to, you know,

pick up the poop on the on the yard. So,

I don't think we're

there's more useful. Can I get AI to do

that? [laughter]

That's a That's my future plan.

How does ModelOp's CTO work off grid on solar and Starlink?

This will be more exciting when it's the

AI personal chef and I don't have to

think about preparing dinner for a

family of four every evening. The one

thing just to wrap up the show in one

minute, Jim, I read that you're off your

CTO. Yeah, you're off grid. You're

correct. Yeah.

Uh give us uh

Yeah, so I'm uh solar powered um on that

and uh we're working over Starlink um

right now. Uh so I have my own network

that's uh powered up over here and uh I

mean it's continued. I've had it since

the very beginning and it's continued to

improve. We get like uh I get like three

to 400 down um on that kind of thing and

about 30 or so up. So it, as you can

tell, it's working really good. It's

actually a hail stom out right now and

you haven't seen a glitch. So we're

doing the there. But yeah, we're uh

right now I live at 10,200 feet in

Colorado. And yeah, it's uh power lines

are miles away. Um and uh it's a dirt

road to get up here. So it is pretty

amazing that I can work from up here and

it's wood heat and all that kind of

thing. Our own water out of a spring and

the simple life.

Wow, that's incredible. So you have

solar charging up batteries and then and

then using the batteries in the

nighttime type of thing.

Yep. Correct. Uh so I have uh six

different arrays of uh solar panels. Um

and they go into lithium iron phosphate

uh batteries which is newer. I used to

use lead acid which wasn't nearly as

good. These batteries are amazing. They

charge great. They even heat themselves

up if they get too cold. Um so yeah, I

run fully off of that. I do have a

backup generator if needed. Uh I have

not run it in a long time. So uh you

know we do generally produce enough but

you get a long bout of rain or snow or

something you might need that. So it's

good to have.

Yeah. Like with no sunlight per se I you

know smithers blocks the sun. How long

can the batteries kind of power the

house for?

Well that'll depend on the load cuz you

know I can check I can obviously vary

how much I turn on or turn off. Uh but

if normal load uh I could go about six

to seven days with absolutely zero

sunlight. Wow, that's I currently have.

So, I have eight very large batteries.

So,

awesome.

Okay, great. Jim, it was a pleasure.

Thank you everyone. Uh, and we'll see

you next week.

Bye.

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