The pipelines, evals, and tooling that make AI systems reliable in production.
The problem
AI prototypes tend to work in a demo and fall apart under real traffic, real cost pressure, and real edge cases.
Our approach
We build the infrastructure layer underneath AI features — data pipelines, evaluation harnesses, and monitoring built for production, not a demo.
Capabilities
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Model serving & inference infrastructure
Inference that stays fast and available under real traffic, with the right model routing and caching decisions made deliberately, not by default.
What this looks like when it's working
AI features that stay fast and available under real production load.
Regressions caught in a test suite, not in a customer complaint.
Cost and quality you can actually see, not guess at.
Deliverables
Technology
Every engagement follows the same five-stage process, regardless of service.
See how we work →Related concept work
Common questions
Because working in a notebook and working reliably at scale are different problems. This is the layer that closes that gap.
Usually, yes. Cost overruns are almost always a routing, caching, or model-selection problem, and are one of the fastest wins we find in an infrastructure audit.
Yes — this is provider-agnostic infrastructure work. We build around whatever you're using, or help you evaluate a change if that's actually the right call.
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Talk AI infrastructure
Tell us where your AI systems are breaking down at scale.