Document Decision Engine
The Document Decision Engine turns document-heavy workflows into structured, explainable decisions. It ingests unstructured inputs (PDFs, scans, emails), extracts validated case data, applies configurable decision logic, and produces auditable outputs ready for integration into your systems.
Built for regulated, high-volume environments where transparency, traceability, and operational control matter.
Core capabilities:- OCR and structured data extraction
- Canonical case schema
- Configurable decision logic
- Rule-level explanations and audit trails
- API-based integration or review GUI
- Human-in-the-loop workflows
Deployment status
- Production deployment: Construction estimate processing
- Proof of concept: Insurance application processing, Claims document reviews
- Internal demonstration: Logistics document extraction
The examples below illustrate typical deployments. Where applicable, results are labeled as production, PoC, or internal demonstration.
Example deployments
Production: Construction estimate processing
Context:
Engine in action:
Operational results:
- Manual review of construction estimates and cost documents
- High variability in document formats
- Slow validation and approval cycles
Engine in action:
- Automated extraction of line items, totals, and metadata
- Structured validation against predefined rules
- Standardized outputs for review and reporting
Operational results:
- Reduced manual review effort in daily operations
- More consistent validation and documentation
- Faster turnaround for estimate checks and approvals
Proof of concept: Insurance application processing
Context:
Engine in action:
Observed during PoC:
- High volume of incoming application forms
- Manual data entry and validation
- Seasonal peaks causing operational strain
Engine in action:
- Automatic parsing of application forms and attachments
- Segmentation of bundled PDFs into individual case documents
- Extraction into structured, validated case objects
- Delivery via API to underwriting systems or via review GUI
Observed during PoC:
- Estimated annual efficiency potential (~60 kCHF) for a medium-sized insurer
- Improved data consistency for underwriting
- Validated scalability during seasonal peaks
Proof of concept: Claims document review
Context:
Engine in action:
Observed during PoC:
- Manual review of claim documentation
- Inconsistent validation across reviewers
- High processing effort during peak periods
Engine in action:
- Extraction of key claim attributes from structured and unstructured documents
- Validation of completeness and required documentation
- Structured outputs ready for downstream systems
- Traceability of extracted fields and confidence levels
Observed during PoC:
- Reduced manual review workload for selected document types
- Improved data consistency for downstream processing
- Scalable processing during peak volumes
Internal demonstration: Logistics document extraction
Context:
Engine in action:
Demonstrated capability:
- Manual extraction of data from invoices and bills of lading
- Incomplete processing of incoming documents
- Data quality variability
Engine in action:
- Automated extraction of shipment details (carrier, receiver, delivery date, package data)
- Structured reporting on processed documents
- API-ready outputs for ERP or tracking integrations
Demonstrated capability:
- Consistent structured extraction across document layouts
- Estimated annual efficiency potential (~40 kCHF) in a medium-sized logistics scenario
- Foundation for downstream analytics and services
Validation & Data Enrichment
Typical challenges:
How we address it:
Implementation notes:
- Missing or inconsistent reference data (names, addresses, legal entities)
- Manual validation steps that slow down processing
- Higher risk of errors and inconsistent decisions across teams
- Limited traceability of “why” a value was accepted or rejected
How we address it:
- Validation: verify extracted fields against trusted reference data (e.g., entity name/address checks)
- Enrichment: add missing attributes used in decision logic (e.g., country risk, entity identifiers)
- Governed outputs: keep a traceable record of what was validated/enriched and which source was used
- Delivery: via API to your systems or visible in the review GUI
Implementation notes:
- Initial PoC: immediate (depending on data sources)
- Tailored validation/enrichment adapters: ~1-2 months
- Secure API setup: ~1-2 months
- Optional deep integration to your IT systems: depends on target systems
- Demo available