AI and LLM integration.
AI applied where it genuinely helps — with the accuracy and cost controls that matter.
There is a lot of AI theatre about. The implementations that pay for themselves are usually unglamorous: extracting structured data from documents that arrive as PDFs, answering questions from your own knowledge base, classifying and routing incoming requests, or summarising long threads. Each replaces genuine repetitive human work.
What’s included
- Document processing and structured data extraction
- Retrieval over your own content, with cited sources
- Classification and routing of incoming requests
- Summarisation of long documents and threads
- Support automation with escalation to a human
- Cost controls: caching, model selection, token budgeting
- Evaluation so you can measure accuracy rather than assume it
Accuracy has to be measurable
A model that is right most of the time is fine for suggesting and unacceptable for deciding, if being wrong is expensive. Before it goes near production I want an evaluation set and a measured accuracy figure, plus a defined path for what happens when confidence is low.
Costs surprise people
Per-token pricing looks trivial in testing and becomes significant at volume. Caching repeated queries, choosing a smaller model where it suffices, and capping runaway usage are the difference between a feature that pays for itself and a line item nobody predicted.
Where AI is the wrong tool
If a rule, a lookup or a regular expression solves it, use that — it is cheaper, faster and deterministic. I will say so rather than adding a model because it is fashionable.
Common questions
Which providers do you work with?
The major API providers. I keep the integration abstracted so switching models or providers later does not mean rebuilding.
Can it work with our own documents?
Yes — retrieval over your content, with answers citing their source so users can verify.
What about data privacy?
Important and often the deciding factor. Anything with personal data needs a lawful basis, a processor agreement and awareness of where the data goes. I will raise it early rather than after the build.
Have a project in mind?
Tell me what you're building and roughly when you need it. I reply to every genuine enquiry within one working day.