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Make your AI cheap enough to ship at scale.
AI Infrastructure & Scaling
LLM cost optimization, inference caching, eval pipelines, vector DB tuning, observability. The unsexy work that makes the difference between a demo and a production feature.
What you get
LLM cost cut without sacrificing quality, measured per request
Vector DB and retrieval tuned for latency and recall at scale
Eval pipelines and observability so regressions get caught before users do
FAQ
We already have infra. Can you improve it?
Yes. Most of our infra work is optimizing existing systems: cost, latency, evals, observability. We do not rip and replace unless it is the only option.
Do you support post-launch operations?
Yes. Monthly retainer for ongoing engineering, on-call for production AI systems, and regular cost and eval reviews.
Add the capacity
you're missing.
A 30 minute call with an engineer who would actually build it. No deck, just your roadmap and what we would ship first.