We turn AI into products that hold up
Our work spans design, machine learning, and infrastructure — held together by one rule: measure everything, ship what works.
A small set of things, done well
Product engineering
We build complete products, not demos — interface, model, API, and infrastructure designed to work together.
Computer vision
Turning images into reliable, structured understanding — with output you can act on programmatically.
Language & agents
Using LLMs where they genuinely help: structured outputs, automation, and assistants that stay on the rails.
Evaluation & reliability
Measuring model quality against labeled data, versioning what ships, and monitoring it once it's live.
Evidence in, confidence out
We treat AI like software: specified, tested, versioned, and observable. That's how a model earns its way into production — and how we keep improving it once it's there.
How an idea becomes something you can rely on
Frame
Define the problem, the data, and what 'good' actually means before writing code.
Prototype
Get a working slice in front of real inputs fast — learn what holds up.
Evaluate
Score candidates against real, labeled data. Evidence decides what moves forward.
Ship & watch
Release behind a passing pipeline, then monitor, measure, and iterate.