Turn document backlogs into automated workflows.
Classify, extract, and route documents with human review for exceptions—integrated into the systems your teams already use.
Overview
We build document processing pipelines that extract, classify, and route information from unstructured documents—invoices, contracts, medical records, regulatory filings—with accuracy metrics and human review for edge cases.
Who this is for
Ideal buyer / team
Operations, claims, records, or back-office teams processing high-volume documents
Best-fit workflow
Intake, classification, extraction, and routing of forms, applications, claims, or regulatory filings
Typical environment
Organizations where manual document review is a bottleneck and accuracy requirements demand human-AI collaboration
Strong fit when…
When you process high volumes of unstructured documents and need structured output with audit trails and exception handling
When to use this
- Manual document review is your bottleneck
- You process high volumes of forms, claims, or applications
- Accuracy requirements demand human-AI collaboration, not full automation
- You need audit trails for document processing decisions
What you get
Reference architecture
Document intelligence pipeline: intake, classify, extract, review, integrate
- Intake layer — file ingestion from email, portals, APIs, and file shares
- Classification engine — document type, priority, and routing logic
- Extraction pipeline — OCR, layout analysis, field extraction with confidence scores
- QA + validation — rule-based checks, cross-field validation, anomaly detection
- Human review UI — exception queue with pre-populated corrections
- Integration layer — push extracted data to downstream systems via API
- Monitoring — throughput, accuracy, exception rate, and SLA dashboards
Typical timeline
Document analysis, taxonomy design, sample annotation, and integration mapping.
Extraction pipeline, classification logic, and QA layer development.
Human review UI, downstream integration, and accuracy optimization.
Controlled production deployment, monitoring, and team training.
Extended scope: additional document types, edge case tuning, and SLA validation.
Success metrics
- Extraction accuracy — % of fields correctly extracted vs. human baseline (target ≥92%)
- Throughput — documents processed per hour vs. manual baseline
- Cycle time — end-to-end time from intake to downstream delivery
- Exception rate — % of documents routed to human review (target <15%)
- Audit trail for processing decisions
Security & governance included
- Document encryption at rest and in transit
- PII/PHI detection and handling
- Access-controlled document storage
- Retention policy enforcement
- Audit logging for all processing steps
Integration-first
We connect to your stack
Frequently asked questions
Want to talk through your use case?
Book a 30-minute discovery call. We'll scope the workflow, map integrations, and outline security controls—no commitment required.