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Two practices,
one team that ships.

We design, build, and operate AI products — voice, chat, multi-step agents — and the platforms that keep them honest in production. Below: what we do, how we work, and the stack we use to do it.

How we work

From kickoff to production
in six weeks.

We don't do demos. Every engagement targets a live, evaluated system in your stack — owned by your team when we walk away.

01Week 1Discovery

Map the workflow we're improving

We sit with the people doing the work today — your product team, your end users, your operators. What does the workflow look like? Where does it break? Outputs: agent spec, success metrics, and a clear build/buy decision before any code is written.

02Week 2–3Prototype

A working agent in your stack

An evaluated v0 against your real data — not a demo on slide deck. Run live queries, see real outputs, pressure-test before we commit to production hardening.

03Week 4–6Productionize

Hardening for production traffic

Function calls, retrieval pipelines, evaluation harness, fallbacks, audit logs, cost ceilings, role-based access. The system your platform can run in production from day one.

04Ongoing or finalHand-off

Hand-off and optional retainer

Documented system, trained team, full source code. From here: clean exit, or 3-month retainer at £8-12K/month for continuous AI development and weekly evals.

Engagement
Week 1
Week 2–3
Week 4–6
Ongoing or final
Live system
Tech stack

No tool dogma.
We pick what ships.

We hold opinions about agent design, not vendors. Below is what we've put in production in the last 18 months — frameworks come and go, the engineering doesn't.

AI Platforms
OpenAIAnthropicGoogle GeminiMistralLiveKitElevenLabs
Frameworks
LangChainLangGraphLlamaIndexVercel AI SDKPydantic AI
Languages
PythonTypeScriptJavaScriptGoSwiftKotlin
Frontend
ReactNext.jsFlutterReact NativeTailwind
Cloud
AWSGCPAzureCloudflareVercel
Data
PostgresPineconeWeaviateSnowflakeRedis