agents
Agentic systems
Tool-using agents with the guardrails, retries, and human checkpoints that keep them dependable past the demo.
AI engineering studio
Intelligence,
engineered to run in production.
We design, build, and operate the AI systems behind real products — agents, retrieval pipelines, and the inference infrastructure underneath. From first prototype to the production pager.
stack python · fastapi · celery · supabase · openrouter
live inferencenode · iad-1
p95
382ms
tok/s
146
status
healthy
//What we build
Full-stack AI engineering, from the prompt to the pager.
retrieval
Retrieval & RAG
Grounded answers from your own data — chunking, embeddings, and ranking tuned to the questions people actually ask.
inference
Inference infrastructure
Model routing, caching, and streaming that hold latency and cost steady as traffic climbs.
evals
Evals & observability
Offline evals and live tracing so you can see what the model did, why, and whether the last change helped.
//How we ship
A path from idea to something on call.
- 01
Scope
We map the task, the data, and the failure modes before a line of code — and say plainly what AI should and shouldn't own.
- 02
Prototype
A working slice in front of real inputs within weeks, measured against an eval set, not a vibe.
- 03
Harden
Guardrails, fallbacks, cost controls, and load tests until it behaves under the messy edges of production.
- 04
Operate
We stay on the pager — monitoring drift, tracing regressions, and shipping improvements against live signal.
< 400ms
median first token
99.9%
pipeline uptime target
1.2B
tokens routed / month
40+
evals per release
// representative production targets — yours, measured against your own evals.
//Start a build
Have a system that needs to actually run?
Tell us the task and the constraints. We'll tell you what AI can do for it — honestly — and how we'd build it.