AI doing useful work for you.
AI for the boring jobs: sorting emails, drafting replies, reading information out of PDFs, answering questions from your team's docs. We build it with proper testing and a safety net, so it won't say something silly to a customer.
What we actually build.
We don't just plug an AI into your business and walk away. Every project comes with a tested setup, a way to handle the cases where the AI isn't sure (it asks a human), and clear visibility into how it's performing.
What you get
AI built for the job, with the right model for each step. A test suite that catches mistakes before they go live. A system that hands tricky cases to a human, with the AI's reasoning attached. Dashboards showing how it's doing. Written instructions for your team. A 30-day window where we fix anything that comes up.
What you don't get
A "deploy ChatGPT inside your business" pitch. A custom-trained model when prompting works fine. A demo that breaks the moment a customer phrases their question oddly. Lock-in to one model vendor. Anything you can't tune yourself once we're done.
How we work is different.
The old way
- 😩Six-month roadmaps before any code ships
- 😟One enterprise platform pretending to do everything
- 😞Hand off the spec, disappear, invoice
- 😔Demos that don't survive contact with reality
- 🙁You depend on the agency, forever
The Intellilabs way
- ✨Real production AI with proper testing
- 🔁Swap one AI for another with a setting
- 🤝A human checks the awkward edge cases
- 📊Dashboards for accuracy, cost and speed
- 🔑No lock-in to one AI provider
Common questions on AI builds.
How do you stop hallucinations in production?
Three things. First, we give the AI real information about your business so it isn't making things up. Second, when it's not confident, it passes the question to a human (with its reasoning shown). Third, we have a test suite that catches mistakes before they go live. We never put a raw AI in front of a customer.confidence thresholds that route low-confidence cases to humans with the model's reasoning attached; and an eval harness with golden examples that catches regressions before they ship. We never put a raw LLM in front of a customer.
Which model do you use?
Whichever fits. Claude and ChatGPT for most jobs that need careful thinking. Smaller, cheaper models for high-volume sorting jobs. We pick on what works, not which company we like. Most projects use a couple of different ones for different steps.GPT where its strengths suit. Small specialised open models (Llama, Qwen, etc.) for high-volume classification where latency and cost matter. We pick on capability, cost and latency — not loyalty. Most builds use 2–3 models for different steps.
What does an AI build cost to run?
AI costs depend a lot on the job. Typical projects cost £100 to £800 a month in AI usage. High-volume jobs can be more, in which case we use cheaper models where we can. We tell you the costs up front and itemise them on your bill.Typical builds run £100–£800/month in token spend; high-volume jobs can be more, in which case we route to smaller cheaper models where possible. Infrastructure on top is usually £40–£200/month.
Can you train a custom model?
We can — but we rarely should. In most cases, a well-set-up prompt with good context beats a custom model at a fraction of the cost. We'll be honest about which side of the line your problem sits on. If a custom model genuinely earns its keep, we'll build one. If not, we won't sell you one.
How do you measure if it's working?
Every project comes with a dashboard showing how accurate the AI is, how much it costs per use, how fast it is, and how often a human had to step in. Each week we also flag the cases where the AI and the human disagreed — so we can keep making it better.
What if a model improves later?
We build things so you can swap one AI for another by changing a single setting. Our test suite then runs against the new model — and we only go live when it's as good or better than what you had before. No re-building from scratch.
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Lots of helpful web pages, written by AI.
A software company had tons of product information but only a couple of writers. We built a system that uses AI to draft helpful pages, then fact-checks them automatically before they go live. No AI slop, no embarrassing mistakes.
4,800 helpful pages, live in six weeks.
We collected the company's real product info into one place. Claude wrote the pages following a strict template. Another AI graded the drafts. Code checks ran on every example. Six months later their Google traffic was 3x higher.
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Got an AI idea you want to actually ship?
Most AI projects stall before they go live. Tell us what you're trying to do, and we'll come back within a working day with a rough plan.