Production AI systems
Real inference, evaluation, and data pipelines engineered to hold up under production load — not demos that fall over at scale.
Founding Engineering Team
Bring the thesis.
We're the founding team.
A complete senior engineering team that builds one AI company at a time — for investors with conviction and capital, and no technical bench.
Request a build memo
One company at a time
Build Timeline Senior team
Day 0 — First commit
Architecture, repo, and delivery from the ground up.
Core systems online
Inference, evals, and pipelines that hold under load.
In production
A live product with real users — owned end to end.
What we build
From first commit to production.
One senior team, one company at a time. These are the three kinds of systems it takes on.
Regulated-industry platforms
Compliance-aware architecture for banking, insurance, and healthcare.
Greenfield AI products
From an empty repo to a product with users, owned end to end.
Senior engineers only · One company at a time · No studio on your cap table
Engineering engagements
A narrow service surface.
Corteus is built for one founding-team match. It also takes a narrow set of engineering engagements where the output is written evidence. The first is The AI Production Audit: a fixed-scope read on production AI cost, evals, provider exposure, and failure points.
Read the scope →02 — How the work goes
Three phases. One team through all of them.
Architecture, implementation, infrastructure, observability, and the runbook the in-house team inherits. Same engineers across the whole arc. No handoffs between discovery and delivery.
We meet.
Two weeks of structured diligence on the thesis: the market, the wedge, and what has to be true for it to work. Disagreements go on the table in writing before either side commits anything.
We immerse.
Eight weeks inside the domain: reading contracts, shadowing operators, and building the evaluation set before the product. Prototypes are built where they retire a risk, not where they impress a room.
We build.
At signing, this becomes our only company. The full team joins as founders, full-time, from day one — and the engineers who design the system are the engineers who run it in production.
03 — What we believe
Five convictions. All of them load-bearing.
How this team makes decisions when the benchmark, the vendor, and the deadline disagree.
01 / 05
Evals beat benchmarks.
A model that passes a benchmark has told you one thing about itself. A model running a daily evaluation suite against its real workload, with disagreements flagged for a human to read, has told you something else.
02 / 05
The cost of a wrong line.
In banking, insurance, healthcare, and telecom, the cost of a mistake compounds for years. Most of the engineering work in these systems is not what you add. It is what you do not break.
03 / 05
Senior is a shape, not a title.
Seniority in AI work is the shape of decisions you make when the thing in front of you is ambiguous. When the benchmark disagrees with the user. When the architecture the vendor pitched is right for them and wrong for you.
04 / 05
No junior layer.
Most firms ship their senior bid and deliver their junior capacity. That arbitrage is not on the menu here. Every engineer on an engagement is the engineer you met, through the life of the work.
05 / 05
Authors stay.
The people who wrote the code should be the people who maintain it. Handoffs from build to run is where the context goes to die. Engagements measured in years are cheap insurance against that cost.
05 — The build memo
Send the thesis. Get the memo.
Describe the AI company you want to exist. Within five business days you get a written build memo: the architecture we would build, the team plan, an estimate of cost to first production deploy — and the three risks most likely to kill it. No pitch and no follow-up sequence. The memo either earns the next conversation or it does not. Read a sample memo →