We’re an engineering-first AI delivery partner. We build agents, retrieval, voice, and applied ML that are integrated, observable, evaluated, and cost-controlled — AI that holds up in production, not just in a demo.
Trusted by teams shipping AI into production
Representative projects, abstracted where needed. Every one was built to run: integrated, observable, and evaluated.
Four scoped, low-risk offers. Start small, prove value, then scale — and keep it running.
1–3 weeks · fixed scope
You have an AI idea and a deadline, but no shared definition of what “working” means.
It turns an uncertain, open-ended bet into a lower-risk first step — and tells you honestly whether to build at all.
Deliverables
4–8 weeks
You need to prove one workflow or model path works on real data before you commit to scale.
It de-risks the build by validating the hardest path first — on your data, against a real evaluation harness.
Deliverables
8–16 weeks
You’re ready to ship AI into a real product and it has to hold up with real users.
Most AI dies between demo and deployment. This is the engineering that gets it across — integrated, observable, and measured.
Deliverables
Ongoing · monthly
Your AI is live and now has to stay reliable, accurate, and affordable as it evolves.
LLM systems drift. Models change, data shifts, costs creep. This keeps quality and unit economics under control over time.
Deliverables
The market is full of teams that can build a demo. The difference shows up in production — and it’s where we focus everything.
We design for load, failure, and operation from the first commit — not after a demo gets attention.
Gold-standard datasets and regression tracking keep quality stable as prompts, models, and logic change.
Hybrid search and citation-backed answers, instrumented so you can see and tune what the system retrieves.
Model routing, caching, and per-feature instrumentation keep unit economics under control at scale.
Structured logging, tracing, and feature-flagged rollouts make behavior visible and changes safe.
We build into the products, data, and workflows you already run — not isolated prototypes.
A delivery model built to de-risk AI: define success early, ship working software every sprint, and make it reliable before it scales.
Align on the problem before touching the model.
We align on workflows, acceptance criteria, quality targets, and the cost envelope — then produce a reference architecture and an evaluation plan. Discovery ends with a defensible go / no-go, not a backlog of assumptions.
Ship working software every sprint — demos, not slideware.
We deliver incrementally against the evaluation plan. Each cycle produces something you can run and measure, with the hardest path tackled first so risk falls early rather than late.
Make it reliable, observable, and cheap to run.
Before launch we wire in observability, evaluation and regression checks, cost routing, and staged rollout. After launch, we keep quality and unit economics under control as the system evolves.
Domains where reliability, integration, and measurable outcomes aren’t optional — they’re the point.
Credit scoring, risk, bond-default prediction, and wealth-management recommenders — where accuracy and auditability are non-negotiable.
Multi-tenant assistants, documentation and troubleshooting copilots, and retrieval over messy internal knowledge.
Demand and checkout forecasting, recommendation, and operational planning that feeds real downstream systems.
High-volume ingestion, metadata extraction from images and video, clustering, and structured content pipelines.
Large-scale ML infrastructure, lifecycle management, and perception for platforms operating at massive scale.
Agentic automation for workflows that demand repeatability, auditability, and integration into existing systems.
Technical depth without the theater. This is what production-grade AI is actually made of.
We treat agents as software, not prompts. Control flow is explicit, tools are typed, and humans stay in the loop where it matters — so behavior is predictable and every run is explainable.
Retrieval is engineered as its own system. Hybrid search balances keyword precision and semantic recall, chunking and indexing are deliberate, and responses are grounded with citations you can trace.
We benchmark agent and LLM outputs against gold standards, track regressions across prompt, model, and logic changes, and flag unsafe or incorrect outputs before they reach users.
What we’ve learned getting AI from demo to production — written for engineers and the people who fund them.
Tell us what you’re trying to ship. We’ll give you an honest read on whether AI is the right tool — and how we’d build it to last.