Document & contract intelligence

Extracting structured, checkable data from contracts, invoices and forms at volume.

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What is it?

Obligations, dates, amounts and clauses locked inside PDFs. We build extraction that turns them into structured records, with a confidence score on every field and a review queue for the ones that need a person, so accuracy does not depend on hoping the model was right.

For a technical reader

The pipeline is layout analysis, then extraction, then validation. Layout matters more than people expect: a table split across a page boundary or a two-column contract will defeat naive text extraction before any model is involved. Extraction combines positional rules where documents are consistent with model-based extraction where they are not, and every field carries a confidence score so a threshold can route low-confidence output to a human rather than letting it through silently.

Our services

Extraction pipeline buildIngestion, OCR, layout parsing and field extraction tuned to your specific document types.
Accuracy baselineA labelled sample set and measured per-field accuracy, so performance is a number rather than an impression.
Review workflowA reviewer interface for exceptions, with corrections captured as training signal.
Downstream integrationExtracted records pushed into the ERP, contract repository or workflow that uses them.

What’s covered

OCR and layout parsing
Scanned documents, tables and multi-column layouts, not just clean digital PDFs
Clause and obligation extraction
Renewal dates, notice periods, liability caps and payment terms pulled out as fields
Invoice and PO matching
Three-way matching against purchase orders and goods receipts
Confidence and review queues
Per-field scoring, with low-confidence extractions routed to a reviewer
Provenance
Every value links to its page and position in the source document

Business problems we solve

Obligations are missed because nobody reads every contractExtracting renewal dates, notice periods and caps into a queryable register makes them visible.
Invoice processing is manual and slowAutomated extraction with three-way matching handles the straightforward majority and queues the rest.
Due diligence takes weeksBulk extraction across a contract set turns a reading exercise into a review exercise.
We cannot answer what our exposure isStructured, queryable contract data answers portfolio-level questions that PDFs cannot.

Our delivery approach

  1. 01
    Start from a labelled sampleA representative set of real documents, labelled, gives a measurable accuracy baseline instead of an impression.
  2. 02
    Extract with confidence scoresEvery field carries a score. High-confidence fields flow through; the rest are queued.
  3. 03
    Keep a human in the loopReview corrections feed back as training signal, so accuracy improves with use rather than degrading.
  4. 04
    Preserve provenanceEvery extracted value links back to its location in the source document, so any figure can be verified.

Solutions & project types

Examples of the work we take on in this area. These describe project shapes, not named client engagements.

Contract obligation registerExtracting key terms across a contract portfolio into a searchable register with renewal alerting.
Accounts payable automationInvoice extraction with purchase order and goods receipt matching, exceptions queued.
Onboarding document processingExtracting and validating fields from identity, compliance and supplier documents.
Claims or case intakeTurning inbound forms and correspondence into structured case records.

Technologies & platforms

OCR & layout

  • AWS Textract
  • Azure Document Intelligence
  • Google Document AI
  • Tesseract

Extraction

  • Anthropic Claude
  • Azure OpenAI
  • Layout-aware transformers
  • Rule-based positional extraction

Storage & search

  • PostgreSQL
  • pgvector
  • Elasticsearch
  • Object storage

Workflow

  • Custom review queues
  • Camunda
  • Temporal

Integration

  • Oracle ERP
  • SAP
  • Contract lifecycle platforms
Learn this field

Where to start with document & contract intelligence.

Secondary to the work itself, but genuinely useful if you are building the skill. Every link goes to official documentation or an official channel.

Beginner

  • How PDFs store text, and why extraction sometimes returns nothing
  • Run OCR on a scanned page and inspect the output
  • Regular expressions for structured fields
  • Why confidence scores matter more than raw output

Intermediate

  • Layout analysis: tables, columns, headers and footers
  • Prompt-based extraction with a defined output schema
  • Building a labelled evaluation set and scoring per field
  • Designing a human review queue that captures corrections

Advanced

  • Active learning from reviewer corrections
  • Handling multi-page tables and cross-reference clauses
  • Provenance and audit for regulated extraction
  • Cost and throughput at high document volume

Certifications

  • Microsoft Certified: Azure AI Engineer Associate
  • Google Cloud Professional Machine Learning Engineer
  • AWS Certified Machine Learning Engineer – Associate

Practice projects

  • Extract five fields from 20 real invoices and measure per-field accuracy
  • Add confidence scoring and route anything below threshold to review
  • Handle a table that splits across two pages
  • Link every extracted value back to its page coordinates
  • Feed reviewer corrections back and measure whether accuracy improves
FAQs

Questions we get asked.

How accurate is it?

Per field, and dependent on document consistency. Clean structured invoices behave very differently from scanned handwritten forms. The labelled baseline exists so you get a real number for your documents rather than a vendor average.

Do we still need people?

Yes, for exceptions. The aim is to shift effort from reading everything to reviewing what the system is unsure about. Full automation with no review is not something we would recommend for contracts.

Can it handle scanned and handwritten documents?

Scanned, routinely. Handwriting, with materially lower accuracy and a correspondingly larger review queue. We measure both before committing to a design.

Where do the documents go?

Into your own storage, under your retention policy. Which processing happens inside your boundary and what, if anything, is sent to an external model is decided explicitly and documented.

AI & Data

Related services.

GenAI assistants & copilotsAssistants grounded in your own content, with the evaluation to show whether they are actually right.Forecasting & machine learningModels that stay in production and keep being retrained, not a notebook that was accurate once.Data platform & warehousingOne place where the numbers agree, with lineage back to the source.Data governance & guardrailsKnowing what data you hold, who can reach it, and what the AI systems are allowed to do with it.
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