AI that does one specific job, not a demo.
Most AI projects fail because nobody defined the job first. We start from a task your team repeats by hand, build the narrowest thing that does it using your own data, and measure whether it holds up.
Send us a URL and we will review it within two business days.
Where AI earns its place.
Assistants that answer from your content
A support or sales assistant that answers from your own documentation, products and policies, with retrieval over your content so it quotes what you actually published instead of inventing it.
Classification & routing
Tickets, enquiries and form submissions read, sorted by topic and urgency, and sent to the right person or system without anyone triaging them by hand.
Drafting & summarising
First drafts of replies, product descriptions, meeting notes and reports, prepared for a person to approve rather than published unchecked.
Document extraction
Invoices, orders, PDFs and emails turned into structured data your systems can use, with the uncertain cases set aside for review.
Model integration
The provider and model chosen per task, API keys kept server-side, cost per request measured, caching where it pays for itself, and a fallback for when the provider is down.
Limits, logging & review
Prompts, rate limits and logging set up so you can see what was asked, what was answered and what it cost, with a human approval step wherever a mistake would be expensive.
When AI is worth building, and when it is not.
It is worth it where the same judgement is repeated many times a day over text you already have. It is not worth it where a rule, a form or a database query would do the same job more cheaply and more predictably.
- Your team answers the same questions by hand, every day.
- Enquiries are sorted manually before anyone can act on them.
- The information you need already exists, but is buried in documents and email.
- You tried a chatbot and it answered confidently with things that were not true.
- You pay for an AI tool and nobody can say what it costs per use.
- Somebody pasted company data into a public chatbot.
What you receive
- The task defined and measured before anything is built
- A working feature in your site or workflow, with keys and data kept server-side
- Logging of requests, answers and cost per use
- A documented way to change prompts, limits and the model later
AI Implementation questions
Which AI models do you use?
Whichever fits the task and the budget. We integrate the major providers through their APIs, and the choice is made per use case on quality, speed and cost per request. The integration is written so the model can be swapped later without rebuilding the feature.
Will it make things up?
That risk never reaches zero, which is why we build retrieval over your own content, show sources where it matters, and put a human approval step in front of anything that goes out in your name. Where accuracy has to be absolute, the honest answer is often a rule rather than a model.
Where does our data go?
Keys and prompts stay on the server, never in the browser. We agree up front which data may leave your systems and which provider receives it, and that is written down before anything is connected.