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 "drop an LLM into 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.
Most agencies sell hours. We sell scope. One-page proposal, fixed price, fixed timeline. Fewer surprises, less arguing about what counts.
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.
The same handful of questions come up on every AI build. Here is how we answer them.
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 the job. Frontier LLMs for most jobs that need careful thinking. Smaller 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 two or three different models for different steps.
What does an AI build cost to run?
AI usage costs depend on the job and scale with volume. We size it during design, itemise it on your proposal, and tell you up front exactly what running it will cost on the providers you choose. Infrastructure costs are itemised separately, so there are no surprises.
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.
Triaging 1,200 weekly tickets before an agent opens one.
A 60-person SaaS team was getting 1,200 tickets a week, most in five buckets. A triage layer classifies, routes, and attaches the right macro. Real bugs surface to a human inside an hour, not five.
AI doing the boring, repeatable bit.
Triage, drafting, classification, extraction. Always with evals and guardrails. Always sitting next to your team, not replacing them. The interesting work stays human, the repetitive 80 percent gets handed off.
See the case studies →Other services.
If this is not quite the shape you need, one of the other four might be.
Repetitive jobs, automated.
The eight or nine jobs your team does by hand every week, turned into software.
See workflow & ops → 03 / Internal ToolsTools your team actually uses.
Forms, queues, dashboards, admin. Built once, shared by everyone.
See internal tools → 04 / Integrations & DataConnective tissue.
Stripe to Xero to HubSpot, done idempotently, with monitoring.
See integrations → 05 / Audit SprintA focused workshop day.
From £1,500. We map your ops, score the opportunities, and a ranked, costed plan follows.
See the Audit Sprint →Not sure where to start? Take the 5-minute audit.
11 questions, your automation score, an estimated time-loss number, and three personalised recommendations. No email needed to see your results.
A focused workshop. A written report you keep.
A focused day mapping your operations and scoring the automation opportunities. The ranked plan you can act on, with us or without, follows. From £1,500.
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.