How we work
One team, first question to production.
The applied AI lifecycle, run as an engagement. The people on your first call are the people who design, build, and run the system. Nothing gets handed to another firm halfway through.
The engagement
From first call to running in production. Governed the whole way.
Each phase ends with something you can use, whether or not you continue. Most clients start with Advise; some arrive with the use case already chosen and start building.
- 00
First call
- What happens
- You walk us through the workflow that costs the most: how it runs today, where it breaks, which systems it touches.
- Who’s involved
- An AI engineer and a solutions architect. No sales hand-off.
- You leave with
- A straight answer on whether today’s AI can change it, and where we’d look first. If the answer is no, we say no.
- Govern
- We ask about data boundaries, security, and compliance on day one.
- 01
Advise
- What happens
- We map the workflow, the data, and the systems around it, then size the opportunities and rank them by value, feasibility, and risk.
- Who’s involved
- Solutions architect, AI engineer, and product lead, with your process owners and an executive sponsor.
- You leave with
- A prioritized roadmap tied to operating metrics, and a first build scoped well enough to start.
- Govern
- Risk and compliance requirements shape the roadmap instead of arriving after it.
- 02
Engineer
- What happens
- A dedicated squad builds inside your environment: integrations, context, agents, evaluation, and the interface people use. We ship in increments and run beside the old process until your team trusts it.
- Who’s involved
- Architect, AI and full-stack engineers, product design, and DevOps. Forward-deployed engineers when the work needs watching up close.
- You leave with
- A production system, documented architecture, an evaluation suite, and baseline metrics.
- Govern
- Identity, permissions, and audit logging are designed in. HIPAA, SOC 2, and ISO 27001 practices throughout.
- 03
Operate
- What happens
- We run and improve the system: watch quality and cost, keep agents accurate as your processes change, and upgrade models without breaking what depends on them.
- Who’s involved
- The team that built it, with named owners.
- You leave with
- Service levels, monthly reporting against business metrics, and a backlog of what to take on next.
- Govern
- Every decision is traceable. Evaluations catch regressions before your users do.
How we price
We sell outcomes, not hours. A flat rate for a dedicated team.
Most engagements run on a flat monthly rate tied to a dedicated squad. No time-and-materials invoices, no surprise change orders. We use AI to move faster internally, and because the price doesn’t move, you get the benefit.
For larger programs, we scope around business impact and timeline.
Ways to work with us
Dedicated squad
Our default. A senior team of architecture, AI engineering, product, and DevOps, working your roadmap at a flat monthly rate.
Forward-deployed engineers
Our engineers sit inside your team and your queue, finding the exceptions a requirements document would miss.
Managed AI operations
For systems already in production, ours or someone else’s. We take ownership of running and improving them.
How we measure and report
Outcomes, not activity. Every engagement, the same six questions.
We report on the work, not on tokens or tickets. Here is the structure, filled in from a reimbursement engine we built for a medical device company.
- 01
The work
What people and process did before.
ExampleStaff checked each device placement against Medicare and Medicaid rules by hand, from binders and memory.
- 02
What changed
Why AI made a different approach possible.
ExampleModels can now reason over structured policy and explain which criteria a case meets.
- 03
What we built
The concrete system.
ExampleA reimbursement engine that scores every case before the device ships.
- 04
How it works
Workflow and architecture.
ExamplePolicy in a knowledge layer, live patient data over HL7 and FHIR, Claude models doing the reasoning.
- 05
Business impact
The outcome, quantified where we can.
ExampleScores in real time, and audit-ready justification tied to specific rules, against a 20–50% industry write-off rate.
- 06
What’s next
How the system compounds.
ExampleNew device types, programs, and payer rules added to the same platform.
Fit
When we’re the right partner. And when we’re not.
A strong fit
Valuable, knowledge-heavy workflows across several systems, where AI can change the economics of the work.
- High labor cost in repetitive knowledge work
- Work that spans fragmented systems and manual handoffs
- Proprietary data and institutional knowledge
- Legacy software that has to stay, or has to go carefully
- Large workflow volume, or errors that are expensive
- Real security, compliance, or integration requirements
Have an executive sponsor but no clear opportunity yet? Start with Advise.
Probably not a fit
We’d rather tell you now. If you hear yourself in these, someone else will serve you better.
- “We need a chatbot.”We start from the work, not the interface.
- “Build this exact feature, cheaply.”We’re paid to rethink the workflow, not to type faster.
- “We need an AI strategy presentation.”Our strategy ends in a first build, not a deck.
- “We want to experiment indefinitely.”Production is the finish line, and someone has to own it.
Start with one workflow. We’ll take it from there.
Bring the process that slows your business down the most. In the first call, an AI engineer will tell you whether today’s AI can change it, and what we’d do first.