Practical AI, not demos. Answers enquiries. Reads documents.
LLM-driven workflows, conversational agents, OCR and document processing that do real, boring, valuable work.
The symptoms, as clients describe them.
- A chatbot demo impressed everyone and then never shipped.
- Someone still reads every invoice by eye to work out which template it uses.
- Enquiries outside office hours just wait until morning.
- Documents get assembled by copying the last one and hoping nothing changed.
Concrete deliverables, not a capability list.
Conversational agents
Chat, email or voice agents that qualify, log and route — not just answer.
OCR & document intelligence
Extraction and matching against a master record, with mismatches flagged, not guessed.
RAG & knowledge bases
Answers grounded in your own documents, not the model’s memory.
Document generation
Assembled from conditional logic across parties and clauses, versioned automatically.
A disciplined process. No fixed opinion about the tool.
- 1
Discover
We sit with the people doing the work and map the process as it actually runs — not as the org chart says it does.
Process map
- 2
Diagnose
Root causes, volumes and the cost of the status quo, so the business case is arithmetic rather than opinion.
Business case
- 3
Build
Right tool for the job, built with error handling, logging and handover documentation from day one.
Working system
- 4
Launch
Piloted against real data and rolled out with your team, with training so adoption isn’t left to chance.
Handover pack
- 5
Operate
Monitoring, fixes and iteration — the system keeps earning after go-live, or we hear about it first.
Support line
Work we have already shipped.
Enquiries answered after hours
A conversational agent that books appointments end to end over SMS, so nothing waits for the morning.
Legal & adminDocuments that assemble themselves
Full contract sets generated from conditional logic across parties, clauses and jurisdictions.
RecruitmentShortlists without the reading
Resumes parsed via OCR and ranked by a custom algorithm against live requisitions.
- Claude
- OpenAI
- ChromaDB
- Google OCR
Python
- FastAPI
Before you ask us.
No — Claude, OpenAI or another model, chosen on merit for the task and swapped without re-architecting the whole workflow.
Every agent ships with logging and a human-review path for anything below a confidence threshold — it's built to fail safely, not silently.
No. Retrieval and generation run against your own documents; nothing is sent for training.
A plugin answers questions. What we build books appointments, updates records and hands off to a person when it should — the outcome is the point, not the chat window.