MLOps & AgentOps engineering
We build the operational layer for AI systems for US companies: evals, monitoring, CI/CD, guardrails, and cost control that keep LLM and agent deployments reliable. The unglamorous infrastructure that decides whether your AI survives in production.
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 across US time zones, 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.
Evals & regression
Automated evaluation so prompt and model changes don't silently degrade quality.
Monitoring & tracing
See latency, cost, errors, and quality drift in production at a glance.
CI/CD for prompts
Version, test, and roll back prompts and models like real code.
Guardrails & cost
Safety checks and budget controls so production stays safe and affordable.
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
We shipped an AI feature and quality is drifting. Can you help?
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Yes, this is the core of AgentOps. We add evals, tracing, and monitoring so you can see what's degrading and why, then fix it with confidence instead of guessing.
How do you test something non-deterministic?
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With evaluation sets and rubric-based scoring rather than exact-match tests. You measure quality as a distribution and watch the trend across changes.
Can you control our runaway AI costs?
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Yes. We instrument spend per feature, add caching and model routing, and set budget guardrails so cost stops being a monthly surprise.
Do you work with our existing observability stack?
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Yes. We integrate with Datadog, Grafana, LangSmith, and similar tools rather than forcing a new platform on you.
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.
Start the conversation