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AI that actually saves you hours every week.

Mobile apps, internal automations, and assistants your team will still use on day 30 — not abandon after the demo. Ten to twenty hours back is a realistic band, and we baseline it so progress is visible. 

Why this work, why us

AI is a tool. The hard part is shipping it as a product. 

Demos are easy. Production AI is mostly the unglamorous middle: data plumbing, evaluation, guardrails, fallback flows, and the editorial work of deciding what the assistant should and shouldn't say. That's where most projects stall — and that's where we do our best work. 

Product team mapping a feature on a whiteboard

Capabilities

What we actually build. 

Mobile apps with AI baked in, not bolted on

iOS, Android, and React Native apps where the AI features are designed into the core flow — assistants, summarisation, image generation, voice — instead of an afterthought tab.

Internal automations that actually save hours

Document parsing, lead enrichment, content generation, ticket triage. We build the boring middle so your team gets the time back, then we measure it.

Custom assistants and agents on your data

Retrieval-augmented assistants grounded in your docs, CRM, and product data — with eval suites so accuracy doesn't quietly drift after launch.

Social-media tooling and content workflows

Drafting, scheduling, image variation, brand-voice tuning. We design the human-in-the-loop step carefully so the output stays yours, not generic.

Vector search, RAG, and orchestration

We pick the boring-but-right stack: pgvector or Pinecone, structured retrieval, evals at every layer. No mystery prompts, no agent loops nobody understands.

Evaluation, guardrails, and observability

Every assistant we ship has an eval set, a regression harness, and runtime logging. AI products fail silently — we make sure yours fails loudly enough to fix.

Stack

Boring choices, modern engines. 

We pick the stack that's most likely to still be standing in 18 months. New shiny is fine for prototypes — for production, we lean on tools with real teams behind them.

OpenAI
Anthropic Claude
Google Gemini
Mistral
Cloudflare Workers AI
Vercel AI SDK
LangChain
LlamaIndex
pgvector
Pinecone
React Native
Expo

Model-agnostic when it matters. Most projects mix two or three providers behind a single internal interface so we can swap as the frontier moves.

How we operate

Eval first. Demo second. Ship third. 

Every assistant we build starts with an evaluation set — the questions, edge cases, and tone we'll grade it against. We tune until the evals pass before we put it in front of a user. That's not glamorous and it's how we avoid the AI-product graveyard. 

Code on a screen during a focused build session

Engagement model

Built carefully, owned by you, supported by us. 

You own the code, the model contracts, and the data. We build it with you, hand it over cleanly, and stay close enough that the third version is better than the first. No vendor lock-in, no rented intelligence. 

Next step

Put a number on the time your team gets back. 

We'll map the workflows that burn hours today, propose a sane first release, and give you an honest view of cost, risk, and timeline — before anyone asks for a retainer.

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