AI development services that run in production.
Agents, generative AI, LLM integration, chatbots and AI consulting from a UK engineering company. We build the model work and everything around it: your data, your integrations, evaluation, security and the cost per request.
What we build with AI.
Five kinds of AI work, each with its own page. Most projects combine two: an assistant grounded in your data, and an agent that acts on what it finds.
AI agent development
Agents that plan, call your tools and hand off to people when unsure.
Generative AI development
Text, image and document generation built into your product.
LLM integration and RAG
Language models wired into your systems and answering from your documents.
AI chatbot development
Customer and internal assistants that answer from your own data.
AI consulting
Use-case selection, data readiness and governance before you build.
How we take AI to production.
The model is the easy part. These six practices are what make an AI feature safe to put in front of customers and cheap enough to keep running.
Evals before launch.
We build a test set from real tasks first, then measure every prompt, model and retrieval change against it. Nothing ships on a demo that happened to work once.
Guardrails on every output.
Inputs and outputs are checked for policy, personal data and answers outside the system's scope. Uncertain answers go to a person instead of the user.
Costs capped per request.
Each feature carries a token and hosting budget, with model routing so routine requests use cheaper models. You see the cost per answer before go-live.
Your data stays yours.
Private model endpoints, no training on your data, UK or EU data residency where you need it, and access rules that follow your existing permissions.
Observable from day one.
Every request is traced: what was retrieved, which tools ran, what it cost and how long it took. Quality drifts get caught on a dashboard, not by a customer.
People where judgement matters.
Review steps for anything with legal, financial or clinical weight, and a clear handover path. The system does the volume; your team keeps the decisions.
AI in the sectors we serve.
Where AI earns its place differs by industry. These are the systems we build most often in each.
Financial services
Document review, KYC checks and analyst assistants over policy and filings.
Healthcare & life sciences
Triage support, clinical letter drafting and patient-facing assistants with human sign-off.
Retail & commerce
Product search and recommendations, catalogue copy at scale and support assistants.
Logistics & mobility
Document extraction for shipping paperwork and exception-handling agents.
Telecom & media
Content tagging and summarisation, subscriber support and churn signals.
Energy & climate
Field-report extraction, asset-data assistants and regulatory reporting drafts.
Public sector
Casework assistants, correspondence drafting and accessible citizen services.
Education
Tutoring assistants, marking support and course content generation.
Manufacturing & industrial
Maintenance assistants over manuals, quality inspection and procurement automation.
Real estate & PropTech
Listing generation, lease abstraction and tenant assistants.
Travel & hospitality
Booking assistants, itinerary generation and guest messaging in several languages.
Legal & LegalTech
Contract review, clause extraction and research over your own precedents.
HR & workforce
CV screening support, policy assistants and job description drafting.
AI work we've shipped.
Case studies where a model does part of the job. We anonymise clients by default and share details under NDA.
Manufacturing · North AmericaProcuraForge: AI procurement automation.
A single source of truth for POs, commitments and supplier conversations.
React · Django REST Framework · PostgreSQL
Retail · North AmericaShed Happens: photo-score shed antlers.
A collector photographs a find and gets a measured, scored result instead of working a paper card by hand.
React Native · Computer vision · Django · Django REST Framework
AI development, answered plainly.
Still wondering something? Ask an engineer
Software where a model does part of the work: assistants that answer from your documents, agents that complete multi-step tasks across your systems, generated drafts and content inside your product, and chatbots for customers or staff. We build the surrounding engineering too: data pipelines, integrations, evaluation, security and monitoring.
Discovery runs two to four weeks and ends with a written scope, an evaluation plan and a cost estimate. A focused first feature usually follows within a few months, released behind a flag to a small group before it reaches everyone. Timelines depend mostly on how ready your data and integrations are.
OpenAI, Anthropic Claude, Google Gemini and open models such as Llama and Mistral, through direct APIs or private endpoints on Azure, AWS Bedrock and Google Vertex AI. We choose per task against your evaluation set and often route between models to balance quality and cost.
No. We use enterprise endpoints that do not train on your inputs, keep data in the UK or EU where required, and apply UK GDPR controls such as data minimisation, retention limits and access logging. Where a third-party model is not acceptable, we deploy an open model in your own cloud.
By grounding answers in your own data with citations, constraining what the system is allowed to answer, and testing against an evaluation set before every release. When the system is not confident, it says so or hands over to a person instead of guessing.
Book a call or send a short brief. If the use case is still open, we start with an AI consulting engagement that picks the use case worth building. If it is already clear, we go straight to discovery and a scoped first release.
Let's build from here.
Thirty minutes with an engineer who builds. No sales, no drip campaign. If we're the wrong fit we'll tell you and point you somewhere better.