LLM applications, retrieval systems, agents and applied ML built to a standard you can put in front of a regulator — with evaluation harnesses, cost controls and failure behaviour designed in rather than discovered in production.
Most AI projects do not fail on model quality. They fail because the team had no way to tell whether a change made the system better or worse, so every release was a judgement call and confidence decayed until the project was quietly shelved.
What we build
Production RAG over your own corpora — chunking and indexing strategy, hybrid retrieval, reranking, citation and grounding — built so that answers can be traced back to a source document rather than asserted.
Turning documents, transcripts and unstructured feeds into typed, validated records — with schema enforcement, confidence handling and a defined path for the cases the model should refuse rather than guess. Agentic systems are a capability of their own.
The piece almost everyone skips. Golden sets, rubric grading, regression suites that run in CI, and offline-versus-online agreement checks — so a prompt or model change is a measurable event rather than a vibe.
Model routing, caching, batching, context budgeting and fallback chains. Unit economics per request, tracked, so the system does not become unaffordable at the exact moment it becomes popular.
Forecasting, classification, ranking and scoring models with the feature pipelines, training cadence, drift monitoring and rollback path that keep them honest after launch.
The hard parts
A vendor who cannot tell you where their own discipline is difficult has either not done it or is not going to tell you the truth about your project either.
The same input can produce different output. Assertion-based testing breaks down, so the test strategy has to shift to distributional and rubric-based evaluation — which most teams have never built and most vendors quietly skip.
A prototype that impresses in a meeting is perhaps a tenth of the work. Latency budgets, cost ceilings, abuse handling, evaluation, observability and graceful degradation are the other nine tenths, and none of them show in the demo.
Inference cost is variable cost. A feature that is cheap in a pilot can be ruinous at real volume, and the architectural decisions that control it — caching, routing, context discipline — are hard to retrofit.
Stack
The full stackThis is what we reach for when the decision is ours. When you already have an estate, we work in it — the stack is part of the engagement, not a precondition of it.
Roles we staff
The reasoning, published
Every capability has a matching domain in the knowledge library. Read it before you talk to us — it is the fastest way to tell whether we know what we are doing.
The piece almost every team skips. How to build a golden set, design a rubric, use LLM-as-judge without being fooled by it, and turn "is this better?" from an argument into a measurement.
Practitioner · 9 minModel-backed features carry a variable cost that rises with success. Context budgeting, caching, routing and cascades, batching, and the unit economics you need before the architecture hardens around you.
Advanced · 10 minRetrieval quality sets the ceiling on answer quality, and chunking decides retrieval quality. Hybrid search, reranking, grounding and citation, re-indexing, and how to tell which half of the system broke.
Practitioner · 10 minAssertion-based testing assumes one correct output. Model-backed systems do not have one. What replaces equality checks, where deterministic tests still belong, and what the test pyramid becomes.
Practitioner · 9 minA convincing prototype is a small fraction of the work. What sits between a demo that worked and a system you can operate, and how to reset executive expectations after they have seen the demo.
Foundational · 9 minHow to buy it
Capability answers who works on it. The engagement model answers what we are accountable for, how it is priced and how it ends.
Other capabilities
Software that acts
Systems that choose their own next step — tool-using agents, orchestrated workflows and automation that spans your existing systems — designed so that what they are permitted to do, and what happens when they are wrong, are decided up front.
Irreversible deployment
Protocol and application development where a deployment cannot be rolled back and a defect is a withdrawal. Contract engineering, security review, upgradeability design and the on-chain boundary decision.
Gated releases
iOS, Android and cross-platform applications delivered continuously despite a third party sitting between your merge and your users — staged rollouts, feature flags, release trains and a native-versus-cross-platform decision made on economics.
The paved road
The substrate the other three stand on: cloud infrastructure, deployment pipelines, observability, data platforms and the internal paved road that determines how fast every team above it can move.