Service 03 — London
AI Integration
LLM features, retrieval over your own data, and agents with tool use — added to the product you already have, with evaluation and guardrails so you can trust the output.
What we do
What’s included.
LLM feature integration
Adding language-model features to a product that already exists: drafting, summarising, classification, extraction, search and support. Built provider-agnostic across Claude, OpenAI and open models so you are not locked to one vendor.
Retrieval over your own data
Retrieval-augmented generation grounded in your documents, database or knowledge base — with the ingestion, chunking, indexing and permission model that decides which user can retrieve what.
AI agents and workflow automation
Agents that call your tools and APIs to complete real work, with explicit scoping of what each tool may do, approval steps where the action is irreversible, and full traces of what was run.
Evaluation, guardrails and monitoring
A test set and scoring for your actual task, so a prompt or model change can be judged rather than guessed at. Plus output validation, refusal handling, and monitoring of quality, latency and spend in production.
AI security review
Prompt injection through user content and retrieved documents, data leakage across tenants and into logs, over-permissioned tools, and cost or abuse controls on model endpoints. Reviewed the same way we review any other attack surface.
Build versus buy advice and model selection
An honest read on whether an off-the-shelf product already does this, and if not, which model tier fits the task and budget. Benchmarked on your data, not on a leaderboard.
Internal AI tooling
AI for your own team rather than your customers: internal assistants over company knowledge, automation of manual back-office processes, and tooling that removes repeated work.
Packages
Two ways to start.
AI Feasibility & Architecture Discovery
2-week sprint, fixed priceMid-market businesses looking to automate core operations
A two-week sprint to find the AI use case worth doing first, prove it with a working prototype on your own data, and design the architecture for the build that follows.
- Use-case workshops with the teams doing the work
- Review of your data and connected systems
- Working prototype on your data
- Model and provider recommendation
Custom LLM & Agentic Workflow Integration
Fixed-price build, typically 4–10 weeksMid-market businesses automating core operations
Building and shipping LLM features or tool-using agents into the systems you already run, with the evaluation, guardrails and monitoring needed to trust them in production.
- Provider-agnostic integration layer
- Retrieval over your own data where it is needed
- Tool-using agent workflows with approval steps
- Evaluation suite scored on your real task
How it works
Start to handover.
- 01
Define the task
We pin down exactly what the AI should do, what a correct output looks like, and how wrong it is allowed to be. Most failed AI projects fail here rather than in the model.
- 02
Prototype and evaluate
A narrow prototype on your real data with an evaluation set attached, so we can compare models and approaches on evidence and tell you early if it will not work.
- 03
Build and harden
Production integration inside your app: data boundaries, permissions, guardrails, rate and spend limits, fallbacks when the provider is down, and traces for every call.
- 04
Measure and iterate
Monitoring of quality, latency and cost once real users arrive, with a clear path to improve prompts, retrieval or model choice without regressing what already works.
Who it’s for
Built for teams like these.
FAQ
Questions.
Which AI models do you work with?
Claude, OpenAI models and open-weight models that can be self-hosted. We build the integration behind an interface so the provider can be changed without rewriting your product, and we pick the model per task based on how it performs on your data.
Will our data be used to train models?
Under the major providers' business and API terms, data sent through the API is not used for training by default — but the terms differ per provider and per plan, so we confirm the specifics for whichever you use and write it down. If the data is too sensitive to leave your infrastructure, we use a self-hosted open model instead.
How do you stop the AI making things up?
You reduce it rather than eliminate it. Grounding answers in retrieved source material, showing the citation, constraining output to a validated structure, and having the system say it does not know instead of guessing. Then you measure it: an evaluation set tells you the actual error rate rather than a feeling about it.
How is an AI project scoped and priced?
Usually as a short paid discovery and prototype — enough to prove the task is achievable on your data — followed by a fixed-price build. That order exists so you find out cheaply if the idea does not work. Ongoing model usage is billed to you directly by the provider.
Can you add AI to our existing app rather than rebuilding it?
Yes, and that is the usual case. We work inside your existing codebase and infrastructure. A rebuild is almost never justified by adding AI features.
Talk to us about ai integration.
A senior engineer replies within one business day.