Production AI · On your terms

Stop renting your competitive edge.

We build production AI agents on a data foundation you own, so the advantage, the IP and the infrastructure stay yours.

ADAPT · 2+ years in production
100+ engineers upskilled on AI
PE · healthcare · fintech
CTO/CPO-led · fixed-price · outcome-aligned
Building applied AI since 2017

ADAPT · 2+ years in production
100+ engineers upskilled on AI
PE · healthcare · fintech
CTO/CPO-led · fixed-price · outcome-aligned
Building applied AI since 2017

01 / The tension

Everyone bought the same models.
That's exactly the problem.

The same models are available to you and to everyone you compete with, at the same price, on the same release train. Buying them gets you to parity fast, and parity is where it stops. Nobody ever built an enterprise on features a competitor can switch on this afternoon.

What compounds is the part only you could have built: your data, your workflows, the judgment your people carry around in their heads. Encode that into a system you own and technology stops being overhead. It starts showing up in the valuation.

RENTED

Everyone's stack

Same capability, same ceiling, same release notes. The vendor keeps the roadmap, the pricing power, and a growing claim on the data your advantage was made of.

OWNED

Your agent, your substrate

Shaped around how your business actually works, running on your infrastructure. The IP, the substrate and the economics stay on your balance sheet.

Pressure-test your build-vs-buy call

01 Data

The data was curated

Your pilot ran on a clean extract somebody prepared by hand. Production runs on the real thing: duplicated, stale, permissioned and occasionally wrong.

02 Measurement

Nobody scored it

"It looked good in the demo" isn't a quality bar. Without evals you can't tell an improvement from a regression, and neither can your board.

03 Economics

Unit costs were invisible

Ten users hide a lot. At four hundred, latency and token spend become a P&L line somebody has to defend every month.

04 Trust

No one owned the failure case

Production needs an answer for what happens when the model is wrong: who sees it, who fixes it, and what the system does in the meantime.

05 Adoption

The workflow didn't change

A tool bolted next to the job gets used twice. Value shows up when the work itself is redesigned around what the system can now do.

06 Ownership

It was built to be demoed, not kept

Pilot code assumes it's disposable. A platform assumes it's yours for a decade: different architecture, different decisions, from day one.

03 / What we build with you

Four ways technology stops being cost and starts being equity.

P01

Own your intelligence

The workflows, judgment and data your people carry become systems the company runs on, not knowledge that walks out the door on a Friday.

Knowledge → asset

P02

Protect what differentiates you

Models, operating logic and data stay inside your perimeter and on your balance sheet. Sovereignty by architecture, not by clause.

IP · data · control

P03

Modernize without losing momentum

Re-platform legacy products while the business keeps running. Customers feel continuity; you lose the constraints underneath.

Legacy → foundation

P04

Get from possibility to production

Strategy, product thinking and engineering in one team, so prototypes survive contact with real users, real load and real evals.

Idea → shipped

Most engagements start with one of these four, and the first conversation is usually about which one is actually worth your quarter.

Find Your Highest-Value Build

04 / Problems we solve

99 problems that didn't fit a mold.

Every one of these came out of a real engagement, across 21 companies in 6 sectors. Tap any that sounds like your Monday, or switch to desktop view in your browser to see the full list across all sectors.

Every one of these came out of a real engagement, across 21 companies in 6 sectors. Filter by sector, or hover anything that sounds like your Monday.

Where it came from

21 companies, one pattern

None of these arrived as a feature request. They arrived as work somebody was doing by hand, in a spreadsheet, or twice. Pick one above to see where it came from.

Tell us the one that's yours

Every correction your team makes inside a vendor's product is a free lesson, and not one you get to keep. The workflow logic, the edge cases, the hard-won exceptions: they improve a system that ships to your competitors on the same release train.

Most AI services require you to migrate to their platform. We don't. The agent, the substrate and the IP live with you, because ownership isn't a clause you negotiate at renewal, it's an architecture decision you make at the start.

Map what you'd actually keep

The ownership test

If you left the vendor tomorrow, what exactly would you take with you?

Who holds the corrections your experts make every day, and who profits from them?

Could you run the critical workflow if the API went dark for a week?

Where does your most sensitive data physically live, and under whose jurisdiction?

Is any of this on the balance sheet, or is all of it rent?

If more than one answer is uncomfortable, that's the conversation worth having.

06 / Trust

AI doesn't fail loudly. It simply drifts.

Traditional software breaks in ways you can see. An agent returns a confident, well-formatted, subtly wrong answer and nobody notices for six weeks. Most teams skip rigorous evaluation for a mechanical reason: building the ground-truth dataset is hard. So agents that dazzle in the demo degrade quietly in production.

We instrument for evaluation from day one, SME-annotated golden sets, regression suites, drift monitoring, error-analysis cycles, and our annotation framework makes that fast enough to fit inside the project timeline. Evals become infrastructure, not a tax. You can change models next quarter without re-litigating trust.

Written with your experts

The people who know what "right" looks like define it, in their language, before we tune anything.

Built on real failures

We annotate what actually went wrong in your data, not a benchmark somebody else published.

Wired into delivery

Evals run in CI. A regression blocks a release the same way a broken test does.

Owned by your team

The suite outlives us, the model choice, and whatever launches next quarter.

07 / The method

Five phases we learned the hard way.

This is how an agent actually gets built, and where the leverage sits in each phase. Select one to see the expertise we bring.

Phase 01

Discovery & Prioritization

ADAPT is the engine · weeks compressed to days

Before we build, we find the seam in the business where an agent creates operating leverage rather than just a good demo.

We immerse in the business: stakeholder interviews, SME walkthroughs, document and workflow analysis. The goal is to prioritize. By the end you have a clear, scored map of agent opportunities, and the highest-impact one selected with eyes open.

Our edge here: ADAPT ingests and synthesizes the inputs as they arrive. Discovery work that takes traditional consulting weeks happens in days.

ADAPT-powered Opportunity mapping Stakeholder immersion Domain absorption

08 / We get you moving faster

Skip the learning curve. Get ahead on our dime.

Four assets, built and refined through internal use across engagements. New projects inherit the work, and the mistakes, we've already made.

ADAPT

Discovery accelerator · 2+ years in production

Our knowledge platform. ADAPT ingests SME interviews, call recordings, documents and existing tools to build a working understanding of your domain. Discovery that takes traditional consulting weeks happens in days, and the output is structured context available for the rest of the engagement.

Accelerates Discovery & Prioritization

Opinionated tool stack

Curated patterns · pre-paid decisions

A curated set of frameworks, patterns and architectural choices we know well. Most AI-native decisions look reversible until you've made a few of the bad ones. We've made those already, internally, so you don't have to.

Accelerates Architecture & Build

AI-dev skills & plugins

Custom dev tooling · learned at scale

Custom skills, plugins and AI-assisted development workflows built across years of internal use. The headline productivity claims for AI dev tools only materialize if a team has learned to use them well. New engagements inherit the curve we've already paid.

Accelerates Build

Data annotation framework

SME knowledge to signal · evals as a timeline, not a tax

The asset that makes evals possible. Refined across healthcare, fintech and document-processing engagements, it turns SME expertise into machine-readable annotations efficiently enough that production-grade evals fit inside the project timeline.

Accelerates Deploy

09 / Where we engage

Start anywhere. We take it to production.

All services

A short, sharp read on where the leverage actually is, and which ideas deserve to die before they reach a budget line.

Typical first move

Two to four weeks, ending in a scored map of agent opportunities with feasibility and payback attached.

Explore this service

150+

Engineers and project managers upskilled on AI
15+

Production AI platforms delivered
50+

Moonshots delivered
9+

Years of applied AI work, starting in 2017

ADAPT · 2+ years in production

We ran the experiment on ourselves first.

ADAPT is our own knowledge platform, built internally across embeddings, integrations, media processing and synthesis. It ingests SME interviews, call recordings, documents and existing tools to build a working understanding of a domain. Discovery work that takes traditional consulting weeks happens in days.

The output is structured context that stays available for the rest of the engagement, and it belongs to you, not to us.

Explore ADAPT

From experiment to scale

2017–19

Before the wave

NLP and deep learning products, document intelligence, entity extraction.

2019–22

Expanding the frontier

On-device vision for insurance adjusters, real-time form tracking, early GPT-3 exploration.

2023

Foundation

ADAPT launched as an internal knowledge platform.

2024–25

Production

Client platforms, evals, operations and infrastructure as core practice.

2026

Scale

Production-grade agents across verticals and an AI-assisted SDLC.

What you keep

Your agent, your substrate, your IP.

The agent itself, encoding the IP that runs your business

The bedrock beneath it, running on your infrastructure

The SME expertise we surfaced, encoded into the system

Full portability. No platform lock-in, no migration to ours

What we bring

Method, expertise, and assets already battle-tested.

Depth at every phase, applied to your specific domain

Four internal assets refined across years of engagements

US-based product leadership with offshore AI engineering scale

CTO/CPO-led delivery. Fixed-price, outcome-aligned engagements

Most AI services require you to migrate to their platform. We don't. The agent, the substrate and the IP live with you, we bring the method, the expertise and the tooling to get there faster.

Last thing

The advantage you can't buy, you'll have to build.

Bring the problem your current stack can't solve. You'll get a shape for the agent, an honest read on feasibility, and what the first ninety days look like. No deck-ware, no discovery theater.

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