AI infrastructure
Production AI systems
Working prototypes that need to survive real load, audit, and a second engineer reading the code. LLM apps, agent platforms, retrieval systems, fine-tuning pipelines.
Corteus · Est. 2026 · Stealth
We are a founding team.
A senior engineering collective, partnering with investors on AI companies — embedded as the founding team, from first commit to production.
01 — What we build
A narrow set of
A narrow set of
problems we take on.
Everything else is a no. The discipline is deliberate — it is how we justify the scale of commitment the team makes to a single product.
Compliance-first
Regulated-industry platforms
Banking, insurance, healthcare, telecom. Legacy modernization where a full rewrite is off the table. The work is incremental, reversible, and evidence-backed.
0 to 1
Greenfield AI products
Founding-team engagements from first commit to first production deploy, with compliance shaped into the architecture rather than bolted on afterwards.
02 — How the work goes
Three phases. One team
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.
01
We meet.
Two weeks of conversations with you and the investor. No NDA yet. We disagree on the thesis if we disagree. Both sides walk if it is wrong.
02
We immerse.
Eight weeks reading contracts, shadowing operators, stress-testing the assumptions underneath the pitch. Prototypes where they earn their weight.
03
We build.
The full team quits their day jobs. You get the authors, from first commit to first million users, with the runbook the in-house team inherits.
03 — What we believe
Five convictions.
Five convictions.
None of them clever.
Opinions with teeth about AI engineering in regulated systems. All of them load-bearing.
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.
04 — Writing
What we are
All writing →
What we are
thinking about.
Apr 25, 2026
GPT-5.5 and the moment AI stopped suggesting and started shipping
OpenAI's GPT-5.5 leans hard into agentic work. Codex jumped from 3 to 4 million weekly developers in two weeks. Here's what changes for your engineering team.
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Claude Opus 4.7 and the bet on agents that run for days
Anthropic shipped Claude Opus 4.7, Claude Design, and a hosted Managed Agents service in eight days. Together they signal where production AI is going next.
ReadApr 23, 2026
Llama 4 Scout's 10 million token window and the end of the chunking era
Meta's Llama 4 Scout ships with a 10 million token context window — the largest of any open model. Here's what changes when memory stops being the bottleneck.
Read 05 — Contact
Building an
Building an
AI company?
Partner with a senior engineering team. Write directly — every message read, reply within two business days.
hello@corteus.com