Make data and AI useful
Turn incoming documents into structured, checked data
We build workflows that extract, classify, validate, summarize and route information from invoices, contracts, reports and forms, with human review where it matters.
In short
Document intelligence reads business documents and turns them into structured data. It extracts and classifies fields, checks them against business rules, summarizes where useful and routes the result to the right system. Uncertain or high-impact cases go to a person for review and approval.
Last updated
01 The problem
What problem does this solve?
Invoices, contracts, forms and reports arrive in different layouts, and someone has to read each one and key the details into another system. The work is repetitive, and the details need checking before anyone relies on them.
Automating it is not only a matter of reading text. The extracted values have to be validated against business rules, exceptions need a person, and sensitive documents need retention and access rules.
02 What we build
What do we build?
- Intake for files and documents from your storage and systems
- Parsing, classification and field extraction
- Structured outputs in a schema you define
- Validation against your business rules
- A human review queue for exceptions and approvals
- Summaries and routing, with updates to business systems
- Monitoring of accuracy, exceptions and cost per document
03 Architecture
What does a typical architecture look like?
04 How it goes
How does the work usually go?
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Gather real documents and rules
We collect representative documents, including the awkward ones, and agree which fields matter, what valid means and which cases need a person.
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Build intake and extraction
We set up ingestion, parsing and classification, and extract the agreed fields into a structured format with defined outputs.
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Add validation and review
Business-rule checks and a human review queue catch what automation should not decide alone, and approvals are recorded.
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Test and pilot
We measure extraction accuracy on representative documents and run a time-boxed pilot against agreed success criteria. It ends in a decision to proceed, revise or stop.
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Connect and monitor
We connect the results to your business systems and monitor accuracy, exceptions and cost per document as the document mix changes.
05 Capabilities and stack
Which capabilities and technologies are involved?
Capabilities
- Python
- FastAPI
- LangGraph
- LlamaIndex
- Amazon Bedrock
- Google Vertex AI
- Microsoft Foundry
- PostgreSQL
The final stack follows your requirements and what you already run. How we choose technology
06 A typical situation
What does this usually look like?
A finance team receives supplier invoices and contracts in many formats, and its people spend their time keying details into another system and chasing mismatches.
We pilot extraction on one document type using the team's own samples, add business-rule checks and a review queue, and measure accuracy before widening the scope.
07 Questions
Questions people ask
Where it matters, yes. Human review and approval are part of the design. Uncertain or consequential cases go to a person, and we define with you which cases those are.
Not always. For fixed, predictable forms, conventional extraction or automation may be more reliable. We assess your documents first and recommend the simplest approach that meets the accuracy you need.
Access and retention rules apply to every document, and what they require depends on sensitivity and your obligations. In financial services and healthcare, privacy, access control and auditability are key considerations, and we design for them.
09 Terms
Which terms come up here?
If this sounds like you: Stop keying invoices into another system by hand