Unstructured Data
Contracts, invoices, and claims are data. Treat them that way.
A large share of enterprise knowledge sits in documents no system reads: contracts, invoices, claims, forms, statements, and correspondence. Modern AI can extract, classify, validate, and route that information — if governance comes first.
Every document workflow hides the same three costs: manual reading, manual keying, and missed terms.
Teams re-read the same agreements to answer basic questions, re-key data that already exists, and discover obligations after they've been breached. The information was always there. It was never engineered.
Documents live outside governance
Shared drives and inboxes hold contractual facts no catalog governs and no analytics can reach.
Extraction is a human job
Operations and finance staff spend hours keying invoice, claim, and contract data into systems.
Terms are discovered too late
Renewal dates, price escalators, and obligations surface as surprises because nothing reads the corpus.
Outcomes
Documents as governed data products
Extracted entities, terms, and classifications in Unity Catalog with lineage to the source file.
Validated automation
Extraction pipelines with confidence thresholds, human review queues, and audit trails.
Searchable institutional knowledge
Authorized teams can query the document corpus conversationally — within their permissions.
How the architecture works
01
Ingestion and classification
Document feeds landed, classified, and versioned on the lakehouse with explicit retention rules.
02
Extraction and validation
Model-driven extraction with quality gates, review workflows, and exception handling.
03
Consumption
Extracted facts joined to operational data; governed retrieval for AI assistants where appropriate.
What we implement
- Document classification pipelines
- Entity and term extraction
- Validation and review workflow design
- Contract analytics
- Invoice and claims automation foundations
- Governed retrieval for document AI
Where this shows up
Contract term visibility
Renewals, escalators, and obligations extracted for one agreement type and joined to customer records.
Invoice processing relief
High-volume invoice extraction with confidence-based routing to a smaller human review queue.
What should be measured
The business case is built on a baseline, not a promise. These are the numbers this solution is accountable to.
- Manual processing hours per document type
- Extraction accuracy against human review
- Cycle time from receipt to system entry
- Missed-term incidents
The first sensible pilot
One document type, end to end
Pick one economically meaningful document type — one contract family or one invoice stream — and run extraction, validation, and routing in production with human review.
Questions
Is this OCR?
OCR is one ingredient. The value is in classification, extraction, validation, governance, and joining document facts to the rest of the enterprise data estate.
What about sensitive documents?
Document pipelines inherit Unity Catalog permissions, and access is scoped by identity. Sensitive corpora can be isolated to restricted catalogs with their own review rules.
Related
AI & ML
How to Prepare Enterprise Data for AI Agents
Agents inherit whatever you give them: permissions, definitions, and quality. Preparing data for agents is lakehouse work, not prompt work.
10 min
Start with the business case
Find the first data or AI opportunity worth proving.
We evaluate the business problem, systems, data, architecture, and economics behind it—then identify the smallest production engagement capable of proving whether the opportunity is real.
Business case first · Architecture-led · Production-focused