AI features in production are systems that happen to call models. We build the systems first.
Hybrid retrieval, evaluated against a real corpus, not a demo PDF.
Bounded loops, tool-call validation, deterministic fallback paths.
Reviewable in CI; regressions block deploys.
Hard ceilings, not soft hopes.
Working systems that survive provider changes, model updates, and the next breakthrough that drops at midnight.
Versioned, diffable prompts with eval scores per version. Roll forward and back without code changes.
Runs on every prompt change. Blocks merges that regress beyond your tolerance band. Human review queue for ambiguous cases.
LiteLLM or custom router. Failover, retries with backoff, per-tenant budget enforcement, cost report per route.
Every run captured: inputs, tool calls, intermediate outputs, final response, cost, latency. Replayable in dev.
Jailbreak suite, PII leakage suite, prompt-injection suite — all in CI. Adversarial set grown from real production traffic.
Per-feature, per-tenant, per-model cost. Alert on burn rate. Cache-hit rate visible. No surprise invoices.
From a single POC against your data to a multi-feature AI surface area with full eval and ops.
POCs that lie are worse than no POC. Ours validate against your actual data, your actual latency budget, and your actual cost ceiling.
Before any prompt: define success. Build a 200+ item golden eval set with your domain experts. Pick a judge approach. Lock the metric.
BM25 baseline first. Single-call LLM second. Then iterate: retrieval changes, prompt changes, model changes — each measured.
Cost, latency, failure modes, provider failover, safety suite. The POC becomes the production architecture in writing.
If the evals hold, we move into Build squad. If not, we say so in writing — with what would need to change for it to.
If something isn’t answered here, ask in your intro email — we keep this list short on purpose.
Maybe not. If your feature is one API call and a small prompt, ship it yourself. We come in when you need eval harnesses, cost ceilings, agentic loops, RAG over your corpus, or compliance constraints that single-call doesn’t cover.
Fine-tune yes — when evals show that prompt engineering has plateaued and labeled data is available. Training from scratch: rarely; the bar is very high and frontier-model RAG usually wins.
Most production workloads end up frontier-by-default with a smaller open model behind specific routes. We write the rationale, build the routing, and re-evaluate quarterly.
Yes, if you have them. Most teams don’t have honest evals; we’ll build the harness with your domain experts in week 1, and it becomes a permanent asset.
Send a paragraph about the problem. We’ll come back inside 48 hours with a written take — team shape, cost envelope, riskiest assumptions.