AI Development
AI features your customers actually use
Every business is being told to "add AI". We build the version that survives contact with real users: customer-facing assistants that answer from your data, document pipelines that read invoices and contracts, and automations that remove hours of manual work — engineered with evals, guardrails and cost controls, not just a demo prompt.
Anyone can wire a model to a chat box in an afternoon. The hard part is an assistant that still answers correctly on day 400 — on your real data, within a cost envelope your finance team accepts. That production gap is where we work: grounded retrieval, restricted scope, automated evals on every change, and a graceful human handover in every conversation.
We're model-pragmatic: frontier models where reasoning pays for itself, small fast models for volume, open models when data must stay on your infrastructure — with caching and cost engineering designed in from day one. The assistant on this site is our own architecture doing its job.
Why AI projects fail
Demo AI vs Production AI
The demo
Impressive for 10 minutes- Answers from the internet's general knowledge
- Improvises on pricing, policy and promises
- No tests — quality drifts silently with every tweak
- Costs unknown until the first shocking invoice
The production build
Trustworthy for years- Grounded in your catalogue, policies & documents
- Restricted scope — 'I don't know' is a first-class answer
- Automated eval suite runs on every change
- Token budgets, caching and cost dashboards built in
We build the right-hand column. It's less flashy in week one and far more valuable in year two.
Capabilities
What's included in ai development
Custom AI assistants
Chat assistants trained on your products, policies and tone that qualify leads, answer support questions and hand over to humans gracefully — on your website, WhatsApp or inside your app.
Retrieval & document intelligence
RAG pipelines over your knowledge base, invoices, contracts and reports. Ask questions in plain language, get grounded answers with sources — not hallucinations.
Workflow automation
AI that classifies incoming email, extracts data from documents, drafts responses and routes work — cutting manual back-office time by 60–90% on repetitive flows.
AI in your product
Smart search, recommendations, summarisation and generation features embedded natively into your existing web or mobile product, designed around your UX.
Model strategy & cost control
The right model for each job — frontier APIs where quality matters, small or open models where speed and cost do — with caching, batching and token budgets engineered in.
Evals, guardrails & monitoring
Automated evaluation suites, prompt-injection defences, PII handling and production monitoring, so your AI behaves the same on day 400 as it did in the demo.
Your documents, answering questions
Invoices, contracts, manuals and reports — indexed and queryable in plain language, with cited sources. Document intelligence is the least glamorous AI and the fastest payback.
How we deliver
From idea to shipped
Find the leverage
A working session to find where AI pays back fastest — usually a repetitive, high-volume flow nobody enjoys.
Pilot with metrics
A scoped pilot with explicit success numbers — deflection rate, hours saved, leads captured.
Ground & guard
Data indexed, scope restricted, eval suite built, human handover wired.
Ship & tune
Production monitoring of quality and cost; steady expansion of what the AI handles.
Industries we serve
Technology we use
FAQ
Questions we hear a lot
Which AI models do you work with?
We work with Anthropic Claude, OpenAI GPT, Google Gemini and leading open-source models. We pick per use case — and often combine a frontier model for hard reasoning with smaller, faster models for high-volume steps to keep costs down.
Can the AI answer from our own data?
Yes — that's most of what we build. We index your documents, product catalogue or knowledge base and ground the model's answers in it, with citations, so responses reflect your business rather than generic internet knowledge.
How do you prevent the AI from saying wrong or harmful things?
Layered guardrails: grounded retrieval, response validation, restricted scopes, automated evaluation suites run on every change, and human handover paths for anything sensitive. We treat AI features like production software, not experiments.
What does an AI project cost?
A focused assistant or automation typically starts around a few weeks of build. We scope a pilot with clear success metrics first, so you validate value before committing to a larger rollout.
