AI that earns its keep.
LLM-powered features built the way we build the rest of the product — scoped to a metric, evaluated against a real dataset, observable in production, and cheap enough to run at the volume your business needs.
The problem we solve
Most AI features look great in demos and fall apart in production. Hallucinations slip through QA. Costs balloon. Latency makes the feature unusable. The team can't tell if changes made things better or worse. We treat AI like an engineering discipline: prompt versioning, eval suites, cost dashboards, fallback paths and human-in-the-loop where stakes demand.
What we ship
- 01Use-case scoping — what AI actually buys you here, in writing
- 02Model selection: Claude, GPT, Gemini, open-weights — chosen on eval
- 03Prompt engineering with versioning, A/B testing and rollback
- 04Eval harness: regression tests for prompts and chains, in CI
- 05Cost, latency and quality dashboards
- 06Structured outputs and validation against schemas
- 07Fallback paths when the model is wrong, slow or unavailable
- 08Human-in-the-loop where stakes demand it
- 09Streaming responses, tool use and function calling
- 10Cost optimization: caching, model routing, prompt compression
What you receive
- A working AI feature integrated into your product
- Eval dataset and dashboards your team owns
- Prompt library with version history
- Cost, latency and accuracy report at launch
Stack we reach for
Ideal for
- → Teams shipping their first real AI feature beyond a chat box
- → Operations teams replacing repetitive review work with assisted workflows
- → Products with text content that needs to be summarized, classified or extracted
- → Companies wanting AI in a workflow without rebuilding the workflow
How an engagement runs
- 01
Scoping
We define the specific outcome AI is improving, the metric, the budget per call. Written down before any code.
- 02
Eval first
We build the eval dataset and harness before the feature. If we can't measure better, we can't ship better.
- 03
Implementation
Feature built, integrated, instrumented. Prompts versioned. Costs tracked from the first call.
- 04
Launch with guardrails
Canary rollout, human review on a sample, dashboards live before a single end-user sees output.
How to engage
AI Feasibility Sprint
Honest assessment of whether AI is right for your use case, with a written go / no-go recommendation.
AI Feature Build
End-to-end AI feature shipped with evals, observability and cost discipline in place.
AI Embedded Team
Senior AI engineering inside your team for ongoing feature development and operation.
Frequently asked.
01Which models do you use?
Whichever wins on the eval for your task — usually Claude or GPT-class, sometimes open-weights when cost or data residency demands it. We test, we don't bet.
02How do you keep costs under control?
Cost modelling before the first prompt. Per-feature budgets, caching, smaller models where they suffice, and dashboards so you see spend in real time.
03What about hallucinations?
We treat them as a first-class engineering problem: grounded retrieval, structured outputs, validation, eval suites that flag regressions before they ship.
Have a problem worth solving well?
Tell us the outcome you want. We'll tell you what it takes — honestly, within a week, in writing.
Start a conversation