Eight Mile · AI integration · London
AI integration in London.
Agents, retrieval over your own documents, and MCP servers that let Claude, ChatGPT or Copilot read your systems — built into the software you already run, for enterprises and small businesses. The engineers who ship it keep it running: the guardrails, the caching, the evaluations and the monthly bill.
Scope
Where AI earns its place in a business.
Four of these have a page of their own. Most projects combine two — an agent that answers from your documents, or an MCP server with the caching and budgets in front of it.
AI inside the products you already run
Search that understands what was meant, drafting inside the tools your team already uses, classification and routing, and summaries that save someone twenty minutes. Added to what you run, not a rebuild of it.
Documents into data
Invoices, contracts, forms and PDFs turned into fields you can query, with a review queue for anything the model is unsure about. Nothing silently guessed into your database.
How an engagement runs
01
Survey
02
Deliver
03
Support
Who it's for
The same engineering, sized to you.
A model is the easy part to buy. What changes between customers is the data it may see, the reviews it has to pass and who looks after it afterwards.
Enterprise teams
A pilot that can pass the security review.
You have data that cannot leave its region, a procurement process and a board asking about the AI plan. We start with one workflow, keep the data where your policy says, answer the questionnaire, and leave the evaluations behind that show whether it works.
Small and medium businesses
The inbox and the paperwork that eat the week.
An assistant that answers from your own policies, a form that reads the PDF so nobody retypes it, a summary of what came in overnight. Priced as one number, on a model account in your name, with a monthly spending cap.
Product teams
An AI feature your users will trust.
You own the product and the roadmap; the model is the part nobody on the team has shipped before. We build the feature inside your codebase — retrieval, tool calls, guardrails, evaluations — and hand it over with the tests that keep it honest.
Practice
How the work is done.
Chosen per project and wired the same way every time: one interface in front of the models, so the model can change without the product changing.
Models
Anthropic Claude
OpenAI
Open-weight models
Hosted in your own cloud
Frameworks
LangChain
LangGraph
Model Context Protocol
Python and TypeScript
Data
PostgreSQL with pgvector
OpenSearch
Your existing APIs
Controls
Guardrails in and out
PII masking
Evaluations
Budgets and metering
What you get
A scope, a working release, and the numbers behind it.
Every AI engagement ends with the same three things in your hands, whichever model it uses.
01
A written scope and price
Which workflow, which data, which model, what “working” means in terms you choose, and one price for the release.
02
A release with its evaluations
Running on your accounts and your model keys, with the test set that shows how well it answers and a usage ledger that shows what each call cost.
03
Handover and twelve months of cover
Documentation, a walkthrough, and twelve months of cover on everything we delivered. Models change underneath you, so the evaluations run again before any model or prompt change ships.
Proof
Work that shows how we do it.
Each of these is told in full on the case studies page: the challenge, the approach, the outcome, and the figures the project's own repository states.
Straight answers
The questions people ask first.
Next step
Tell us which workflow you want AI in.
A few sentences about the task, the data it needs and who does it today is enough to start. You will get a straight answer about whether a model is the right tool for it.