An AI engineering company for the hard 20%
We do the AI engineering that comes after something works: evaluation harnesses, cost per completed task, reliability when traffic triples, and the observability that catches an agent quietly producing wrong output for six weeks.
No sales script. You talk to the engineers who'd build it.
Our team works a shifted day so you get real-time standups and same-day turnarounds in your time zone, not next-morning replies.
Every line of code, model weight, and prompt is yours from day one. NDAs and clean IP assignment are standard, not an upsell.
You work directly with the engineers building your system. No account managers sitting between you and the people writing code.
We move from scoping to a working system in production in weeks. Most engagements ship something usable inside the first month.
What we build
Concrete systems we ship, tuned to your data and your stack.
Evaluation infrastructure
Scored test suites from real production traces, run on every change, in your repository and your format.
Unit economics
Cost per completed task including failed attempts, which is the only number that survives contact with a CFO.
Reliability under load
Checkpointing, idempotent steps, retries that do not double-bill, and graceful degradation when a provider has a bad hour.
Silent failure detection
Alerting on run shape, step counts, and escalation rates, because uptime and error rate both look perfect while quality collapses.
How we work
Scope & evals
We pin down what success means and build the evaluation set before writing the feature, so quality is measured, not guessed.
Build in the open
Weekly demos against real data. You see progress every week and can change direction before it gets expensive.
Ship & instrument
We deploy with logging, cost tracking, and guardrails in place, then tune against production traffic.
Hand off or stay
Take the keys with full docs, or keep us on for iteration. Either way you're never locked in.
Questions, answered
Our system works. Why would we need this?
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Because the failure you should worry about is the one that does not crash. Every quiet degradation we have investigated was visible in a random sample of output weeks before anyone noticed.
What does an engineering review find?
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Usually a large cost reduction available without quality change, missing or unusable evaluation, and an audit trail that cannot answer the question anyone will actually ask.
How do you measure quality without ground truth?
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Pairwise comparison against a reference set, objective checks for the things that are objective, and human review on a sampled basis. Imperfect measurement beats arguing about impressions.
Can you work alongside our platform team?
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That is the usual arrangement. We bring the patterns, your team keeps the context, and the work stays maintainable after we leave.
Let's scope your build.
Tell us what you're trying to ship. We'll tell you honestly whether AI is the right tool and what it would take.