Turn document chaos into structured part data.
Manufacturing operations run on documents — engineering drawings, spec sheets, inspection reports, and supplier files. The Document Intelligence Agent transforms unstructured bundles into validated, confidence-scored, system-ready data.
Live deployments to date are in rubber manufacturing; other sub-sectors are scoped per engagement.
- Text documents and scanned PDFs
- 2D engineering drawings (3D model file support on the roadmap)
- Image-based and mixed-format document packages
- Part specifications, dimensions, and tolerances
- Materials, surface finishes, and annotations
- Structured data fields ready for downstream use
How it operates
Confidence-scored, human-reviewed, system-ready.
- 01 / Ingest
Document bundle
Mixed PDFs, scans, drawings loaded into the workflow.
- 02 / Extract
Field extraction
Specs, dimensions, materials parsed into structured fields.
- 03 / Score
Confidence gate
Every field scored. Low-confidence routed to human review.
- 04 / Route
Downstream
Validated output flows into ERP and downstream workflows.
≥98% OCR accuracy on clean scans · ≥95% dimensional extraction accuracy · Processing in under 30–60 seconds per document.
Unstructured document bundle
PDFs, scans, and mixed formats scattered across email and file shares. Estimators and engineers manually re-key part data — slow, inconsistent, and error-prone.
Structured part-spec dataset
A validated, confidence-scored dataset — every field traceable to its source document and ready to feed quoting, procurement, or ERP systems automatically.
| Material | Al 6061-T6 | 0.98 |
|---|---|---|
| Overall width | 148.0 mm | 0.99 |
| Bore diameter | ⌀12.0 mm H7 | 0.97 |
| Tolerance | ±0.05 mm | 0.93 ⚑ |
| Quantity | 250 | 0.99 |
Worked example · Smart Manufacturing AP
Invoice-to-PO matching against a legacy SAP instance (illustrative example).
The finance team manually reconciles paper and PDF invoices against purchase orders. Part-number mismatches on specialized machinery cause late-payment penalties. Here's how the workflow assembles.
Agent roles shown here (Data Engineer, Architect, Policy, Evaluator) are the team that scopes and builds your workflow during onboarding.
Builds the OCR pipeline that extracts line items and table structure from 2D PDF invoices.
Maps the API flow between the invoice data and the ERP, recommending a Secure Gateway connector for the legacy SAP system.
Ensures tax compliance is calculated correctly and flags invoices with bank details that must be encrypted at rest (PII detection).
Sets confidence-score thresholds — if the AI is under 95% sure of a match, it flags the invoice for human review.
Processing 5,000 invoices per month is estimated at $140 in model tokens — against an estimated $4,500 saved in manual labor hours.
Source · Scoping docStructured data feeds every downstream workflow.
Document Intelligence is the foundation the Quotes Generation Workflow is built on. See how extracted specs become an ERP-logged draft quote.
- Confidence scoring on every field
- Human review before progressing
- PII masked before external LLM calls
- Full audit log per document