We build AI systems
built to run in production.
Not a chatbot agency. Not a prompt-engineering shop. We build production-grade AI systems — reliable, observable, cost-efficient — whether that's something new or hardening a feature that's already live.
Get a Scoping CallREPRESENTATIVE OUTCOMES
LLM bill on a 30K-user product
Hallucination rate on customer-facing flows
p95 latency on the critical user path
Retrieval@5 after RAG rebuild
WHAT WE DO
Six things, done well.
LLM Cost Engineering
We attribute spend per feature, per model, per prompt — then re-architect to slash it.
- Token-level cost attribution and dashboards
- Model routing (GPT-4 → smaller models where safe)
- Prompt-aware caching and batching
- Context-window pruning without quality loss
RAG Hardening
We rebuild retrieval pipelines that actually retrieve the right thing.
- Chunking strategy and metadata design
- Embedding model selection + reranking
- Eval harness so quality stops regressing
- Hybrid search (BM25 + vector) where it helps
Reliability Engineering
Guardrails, structured outputs, and fallbacks so AI features stay up under load.
- Structured-output validation (Pydantic / JSON schema)
- Retries, timeouts, circuit breakers per call
- Fallback model chains for outages
- Hallucination eval pipelines
Observability & Tracing
When something breaks, you can trace it back to the prompt, model, and chunk.
- OpenTelemetry / Langfuse / Helicone integration
- Per-feature cost and latency dashboards
- Quality scorecards on production traffic
- Alerting on cost, latency, and error spikes
On-call AI Ops
Optional retainer: we own dashboards, on-call rotation, and ongoing tuning.
- Monthly cost and reliability review
- On-call response for AI-specific incidents
- Prompt and retrieval tuning as data shifts
- Quarterly model upgrade evaluation
Inference Infra
Self-hosted inference, GPU sizing, and request routing for scale and cost.
- vLLM / TGI deployment and tuning
- GPU instance sizing and autoscaling
- Request batching and continuous batching
- Multi-region routing where latency matters
HOW WE WORK
Engagements, not retainers-for-the-sake-of-it.
Scope (week 1)
We define the build with you and set the baseline — what ships, the success criteria, and the evals that prove it. For an existing system we instrument first to find where it falls short of production-grade. You get a fixed-scope plan.
Build (weeks 2–4)
We build to our six standards — evals, cost controls, tracing, and reliability math designed in. Every change ships behind a flag with a rollback plan.
Deploy (weeks 4–6)
Shadow mode first, then gradual rollout. We measure against the baseline we set at the start. No big-bang releases.
Hand-off or stay (week 6+)
We hand off dashboards and runbooks to your team — documented and observable — or stay on a monthly retainer for on-call response and ongoing tuning. Your call.
PRICING
Quoted per engagement.
Every system is different. We quote after the scoping call, when we can give you a fixed price tied to a specific build.
Scoping call
Free. Fixed-scope build plan + quote.
Build sprint
Fixed-fee, scoped on the call. Typically 4–8 weeks.
On-call retainer
Monthly. Owned dashboards + incident response + tuning.
Infra pass-through
Cloud, GPUs, and API costs billed at cost.
Get a quote you can defend to your CFO.
We quote against a specific build scope and outcome — not a deck of feature bullets.
Get a Scoping CallGet your AI built right the first time.
Free 30-min scoping call. We map the build — new or hardening — and send back a fixed-scope plan.