Practical AI that removes work, not just adds features.
AI & Automation
- Document and email data extraction
- Knowledge assistants over your own content (retrieval-augmented generation)
- Request classification and routing
- Workflow agents with human approval steps
Problems this solves
Skilled people spend hours on repetitive reading
Emails, PDFs and forms are read, interpreted and re-typed before real work can begin.
Answers are buried in documents
Staff and customers struggle to find information in manuals, policies and past correspondence.
AI pilots never reach production
Experiments look promising but lack the integration, security and oversight needed for daily use.
What we deliver
- Document and email data extraction
- Knowledge assistants over your own content (retrieval-augmented generation)
- Request classification and routing
- Workflow agents with human approval steps
- LLM integration into existing software
- Evaluation, monitoring and cost control
Example use cases
Automated quotation intake
Extract requirements from customer emails and attachments into a structured draft quote for review.
Internal knowledge search
Staff ask questions in plain language and get answers with links to the source documents.
Support request triage
Incoming requests are categorised, prioritised and routed, with suggested replies for agents.
Invoice and form processing
Key fields are extracted, checked against your data and passed on for approval.
How we approach it
- Step 1
Find the right problem
We identify tasks where AI is reliable enough, and where errors can be caught before they matter.
- Step 2
Prototype with your data
A small proof of concept on real, anonymised examples shows what accuracy is realistic.
- Step 3
Design the human checkpoint
People review, correct and approve AI output wherever the stakes require it.
- Step 4
Integrate and monitor
The solution runs inside your existing tools, with logging, evaluation and cost tracking.
Relevant technologies
- LLM APIs
- Retrieval pipelines
- Vector search
- Python
- FastAPI
- Workflow orchestration
- PostgreSQL + pgvector
AI & Automation: common questions
Is our data used to train AI models?
We select providers and configurations based on your data-protection requirements, including options where data is not used for training and is processed in specific regions. Data handling is agreed and documented before any real data is used.
How accurate will the AI be?
It depends on the task and the quality of your data. That is why we test on your real examples early and design review steps for cases where the model is uncertain. We will not promise accuracy we have not measured.
Can you add AI to software we already use?
Often, yes. If your system has an API or can exchange data, we can add AI-powered steps around it without replacing it.
What does it cost to run an AI solution?
Running costs mainly depend on volume and the models used. We estimate them during the proof of concept and build in monitoring so costs stay predictable.
Have a task that feels like it should be automated?
Describe the process and share a few anonymised examples. We will assess whether AI is a good fit, and say so if it isn’t.