AI · Documents
AI for document processing and understanding.
The system reads a document, understands it and turns it into a structured record other systems can use.
A document as data, not an attachment.
When clients usually bring us in
- staff hunt for information across documents
- documents are reviewed by hand
- support answers the same questions repeatedly
- you need classification or data extraction
- you want AI inside the business process, not beside it
AI isn't always the right answer
If plain automation solves the process, we don't add AI because it's fashionable. We'll tell you where AI pays off and where it doesn't.
What this means for you
- Less time spent looking for information.
- Less manual retyping of data.
- Faster processing of documents and requests.
- More consistent answers from your team.
- A clear trail of where each answer came from.
Problem → solution
Which documents we process
- contracts
- tender documentation
- invoices
- forms
- internal documents
- technical documentation
- letters and applications
What the AI does with a document
Reads and classifies
It recognises the document type and routes it into the right process.
Structures
It extracts the fields your systems need: parties, amounts, dates, references.
Compares and checks
It compares versions, checks against the template and flags deviations.
Finds what's missing
It flags missing data or attachments before the document moves on.
Summarises
It produces a summary highlighting risks and obligations.
Extracts deadlines and requirements
From tender or contract documents it extracts deadlines, conditions and requirements.
Use cases
Tender documentation
Conditions, deadlines and required references are extracted from a long tender.
Incoming invoices
Data reaches the accounting system without retyping.
Contracts
A review of obligations, deadlines and deviations from the standard template.
A document's path through the system
Ingestion
the document arrives by email, through a form, from a folder or from the document system
Reading
conversion to text; for scans, with text recognition
Classification
what type of document it is and where it belongs
Extraction
reading the fields the business process needs
Validation
checking against existing data, rules and code lists
Structured record
the data in a form other systems can use
Routing
the document and the data go to the right person or system
Human review
borderline cases go for approval instead of a silent guess
Archiving
storage with metadata and an audit trail
Accuracy and borderline cases
No document-reading system is perfectly accurate, so the more useful question is what happens when it is unsure. The right design makes uncertainty visible: the record is flagged and sent for review rather than written as a value someone has to chase down the chain later.
We measure accuracy on your documents, not on generic samples. That is why, before go-live, we need a sample of real documents - including the badly scanned, incomplete and unusual ones.
What we need from the client
- a sample of real documents, including the awkward ones
- the list of fields the process actually needs
- validation rules and code lists
- where the results go and who uses them
- agreement on who reviews the borderline cases
How we design the AI
Use case
We start by defining which decision or step the AI takes over, and where it must stop.
Data
We map sources, quality and access. Without organised data, AI cannot produce a dependable result.
Architecture
We choose the model, the retrieval approach and the point where AI plugs into the existing process.
RAG, agents, tools
The agent uses your systems through their APIs, within the permissions you define.
Keeping AI in control
Guardrails
What the AI may and may not do, when it must ask a person and when it stops the process.
Human in the loop
A person approves the sensitive steps. Each approval is logged together with the reason.
Evaluation
Regular checks on sample sets, so we notice when answer quality drifts.
Monitoring and cost
Tracking success rate, failures and cost per task, with alerts when something deviates.
Related work
FAQ
What about scanned documents?
We use OCR; scan quality affects accuracy, so critical fields always include human confirmation.
Related reading
Related solutions
Sounds like your project?
Send us the project description, your existing system, the tender documents or the event date.