Expertise
Applied AI
Language models are useful for a specific set of problems: reading unstructured documents, classifying requests, drafting, searching and summarising. We integrate them into real workflows, with measurable quality, clear fallbacks and humans in the loop where it matters.
01 — Typical situations
- Staff spend hours copying data from emails, PDFs and scanned documents into other systems.
- Incoming requests need to be sorted, routed or answered, and the rules are hard to write down.
- Knowledge is spread across manuals, tickets and shared drives, and nobody can find anything.
- An AI pilot looked impressive in a demo and never made it into production.
02 — What we do
- Document and email processing
- Extraction of structured data from invoices, orders, delivery notes and correspondence — with validation and review queues.
- Classification and routing
- Sorting requests, tickets or leads by intent, urgency or responsibility.
- Internal search and assistants
- Retrieval over your own documents with sources, permissions and honest uncertainty.
- Evaluation
- Test sets and metrics so you know whether a model is good enough before it touches production data.
03 — How we work
We start with the workflow, not the model. Every AI component gets an evaluation set built from your real cases, a defined fallback when it is unsure, and logging that respects data protection. Where a simple rule does the job, we use the rule.
- Claude & other LLM APIs
- Structured outputs
- Retrieval (pgvector)
- Evaluation pipelines
- Python
- Workflow automation
04 — When not to hire us
We do not sell AI as a strategy. If the problem is a missing process or poor data, a model will not fix it — and we will say so.
Have a problem worth solving?