The problem
Every product is racing to add AI. Most are adding chatbots that nobody uses, summaries that aren’t accurate, and "AI features" that cost more in tokens than they earn in value.
The good ones picked a workflow where AI removes a real, measurable amount of friction — and built evals around it so the team can iterate without flying blind. That’s the work.
What we actually build.
No demos. Production AI features your users will use weekly.
Retrieval (RAG) done well
Chunking, embeddings, hybrid search, re-ranking, citations. Vector store of your choice — Pinecone, pgvector, Turbopuffer.
Drafting & summarization
Write-the-first-draft features for emails, tickets, reports. Streaming, edit-in-place, undo.
Structured extraction
JSON-schema constrained outputs. Forms that fill themselves, classifiers that don’t hallucinate.
Tool-using agents
Multi-step workflows with tool calls, with hard guardrails on what they can do.
Evals & observability
Braintrust, Langsmith, or rolled-our-own. Regressions caught before users see them.
Cost & latency budgets
Token tracking per feature, fallback chains, prompt caching, model routing. AI you can afford.
What you get, shipped.
Concrete artifacts, not slide decks. Every engagement ends with these in your repo, your cloud, your hands.
Production AI feature
Live in your app, on your domain, with your users. Not a Streamlit demo.
Eval suite
A test set you control, with pass/fail criteria. CI runs it on every prompt change.
Observability dashboard
Per-feature cost, latency, error rate, satisfaction signals.
Prompt + tool registry
Versioned, reviewable, rollback-able. No prompts hidden in YAML.
Safety guardrails
Input/output filtering, jailbreak protection, PII redaction where needed.
Knowledge transfer
A working session for your team to own the system after we leave.
Four to eight weeks per feature.
Pick the friction
A workshop to find the workflow worth automating, with measurable wins.
Prototype
Eval set first, then the feature. We iterate against the evals, not against vibes.
Productionize
Streaming UI, error states, fallbacks, observability, cost guardrails.
Ship & tune
Roll out behind a flag, watch the dashboards, tighten prompts and routing.
Tools we reach for, by default.
Not religious about any of these — we'll use what your team can maintain after we leave.
Other things we build.
Most engagements blend two or three of these. If you're not sure where your project fits, send us a brief and we'll suggest the right slice.
Web platforms
Marketing sites, dashboards, portals, content systems. Built for speed, accessibility, and edit-ability by your team.
Product engineering
SaaS, MVPs, internal tools — typed APIs, real-time data, auth, billing, observability.
Design systems
Tokens, components, Figma kits — versioned, themable, generated from one source of truth.
What’s the workflow worth saving?
Tell us about the workflow your team would pay to remove. We’ll tell you whether AI is the right tool — even if it isn’t.