How we work
Measurement first,
then code
Every engagement is built backwards from an agreed definition of correct. This is what that looks like week to week.
Nobody can prove it's working
The failure mode we are most often called in to fix is not bad code. It is a system nobody can prove is working — an agent that is right “most of the time”, a retrieval layer with no benchmark, a pipeline whose incidents cannot be reproduced.
So we invert the usual order. The evaluation comes before the architecture, and the architecture is chosen because it is the one that scores.
What every engagement has in common
Evaluation before architecture. We do not choose a model, a framework or a vector store until there is an agreed benchmark to choose it against. The architecture is whichever one scores.
Everything traced. Every agent action, every retrieval, every decision writes a trace that can be replayed exactly. That is what makes a system auditable after the fact, and it is not optional.
Budgets are contracts. A latency target or an accuracy floor is written down, measured continuously, and a regression fails the build. Not an aspiration in a slide.
Week by week
Each phase ends with something you keep — a document, a benchmark, a running system — whether or not the next phase happens.
Discovery
A short paid engagement. We map the problem, interrogate the data, and agree what 'good' is measured against before anyone writes production code.
The evaluation
We build the eval set from your real cases. It becomes the contract: the number that decides whether the system ships, and the regression gate afterwards.
The plan
Architecture, milestones and a fixed first deliverable, priced. Yours to take elsewhere, whether or not we continue.
Build
Embedded with your engineers or as a dedicated pod owning a surface end to end. Shipping continuously, with traces, evals and docs alongside the code.
Handover
Runbooks, dashboards and an on-call story. We are not finished when it works; we are finished when your team can change it without us.
Questions
we're asked most
What does “AI-native engineering” actually mean?
Architecture decisions start from the model: what it does reliably, what it doesn't, and what evaluation proves it. The product, data and infrastructure are engineered around that answer.
Do you build financial products or give financial advice?
No. We build infrastructure — data pipelines, execution plumbing, risk tooling and monitoring. We do not provide investment advice, manage assets, or make performance claims.
How do engagements start?
A short paid discovery: we map the problem, define an evaluation, and return a plan with a fixed first milestone. You keep everything produced, whether or not we continue.
Can you work with our existing team?
Yes — embedded alongside your engineers, or as a dedicated pod that owns a surface end to end. Either way you get the traces, evals and docs, not just the code.
Let's build
the impossible.
Tell us the system you can't get built. We come back with a short, paid discovery — a clear plan and a fixed first milestone — usually within two working days.
Discovery
A working session to map the problem and define what "good" is measured against.
The plan
Architecture, milestones and a fixed first deliverable — yours to keep, either way.
We build
Embedded with your team or as a dedicated pod, shipping with traces, evals and docs.