AI development — LLM integration, chatbots, document processing, RAG
AI that does a specific job, not a demo
We build AI features that solve a named problem and can be measured: assistants trained on your own documentation, document processing that removes manual data entry, and LLM features inside your existing product.
The problem
AI is easy to buy and hard to get value from. The gap is rarely the model — it is that nobody defined what success looks like, so there is no way to tell whether the thing works. Projects stall in an endless prototype phase, or worse, ship without evaluation and quietly give customers wrong answers with total confidence.
What's included
Internal knowledge assistants
A chatbot that answers from your own documentation, policies, and past tickets rather than from the open internet — retrieval-augmented so answers cite their source and can be checked.
Document processing
Extract structured data from invoices, contracts, forms, and PDFs, with a confidence threshold and a human review queue for anything below it.
LLM features in your product
Summarisation, classification, drafting, and search built into software you already run, with cost controls and fallbacks so a provider outage does not take your product down.
Customer support automation
Triage and first-line responses that hand off cleanly to a person, with the handoff point tuned so customers are not trapped in a loop.
AI readiness assessment
A fixed-price review of where AI would genuinely pay for itself in your business, and — as often as not — where it would not.
How we approach it
- 01
Start from the process, not the technology
We look at the actual manual work you want removed and how often it happens. If the honest answer is that a database query or a form would solve it better, that is what we will tell you.
- 02
Define what good looks like before building
An evaluation set of real examples with known correct answers. Without it, "it seems to work" is the only quality bar available, and it is not one you can defend to a customer.
- 03
Build the narrow version first
One use case, deployed, measured, in front of real users. Breadth comes after something is demonstrably working, not before.
- 04
Design for being wrong
Confidence thresholds, human review queues, graceful degradation when a provider is down. The failure path matters more than the happy path in anything customer-facing.
- 05
Control the cost before launch
Per-request cost modelled against realistic volume, with caching and limits in place. AI features that get expensive at scale are a business problem, not a technical one.
Our position
"Most AI projects fail on evaluation, not on modelling."
Getting a plausible answer out of a language model takes an afternoon. Knowing whether it is right often enough to put in front of customers takes an evaluation set, a threshold, and a fallback. Teams that skip that part ship something impressive in a demo and unusable in production.
Free discovery call
Got a problem that keeps sliding to next sprint?
A free 30-minute call. Tell us what you're working on and we'll outline the best approach, a realistic timeline and an estimated budget.