AI Solutions & Automation
AI applied to specific work you can measure, not a chatbot on your homepage.
Most AI projects fail because they start from the technology instead of the task. We start from a process that is currently slow, repetitive or expensive, and ask whether a model actually makes it cheaper — sometimes the honest answer is no, and we will say so. Where it does, we build it properly: retrieval over your own documents, guardrails on what the model may act on, human review where the cost of being wrong is high, and logging so you can audit every decision.
Start with the process, not the model
The projects that pay back are unglamorous: reading invoices into a system, qualifying inbound leads, answering the same forty support questions, extracting data out of PDFs that arrive by email. Each has a number attached — hours, or error rate — and that number is how you know whether it worked.
Guardrails, review and an audit trail
A model that can act on your data needs limits on what it may do, a person in the loop where a mistake is costly, and a record of every input and output. We build those in from the start, because retrofitting oversight onto an autonomous system is how organisations discover what it has been doing.
Frequently asked questions
- Can AI actually help my business, or is it hype?
- Both are true depending on the task. It is genuinely good at reading unstructured text, classifying, drafting and answering from a known body of documents. It is a poor fit where an answer must be exactly right every time and cannot be checked. We will tell you which one you have before quoting.
- Will our data be sent to a third party?
- Only if you accept that trade-off. Hosted models are cheaper and stronger; self-hosted open models keep everything on infrastructure you control. For sensitive data we usually recommend the second, and we can run it on your own servers.
- How do you stop the AI from making things up?
- By constraining what it is allowed to answer from — retrieval over your documents rather than open-ended generation — and by measuring accuracy against a fixed test set before launch and continuously after. Where being wrong is expensive, a person approves the output.