Document Processing Agent
Autonomous pipeline utilizing Claude OCR and structured extraction to classify, parse, and route contracts, proposals, and operational documents.
System Architecture Flow
Compact, event-driven flow. Each step is horizontally scalable and instrumented for failure recovery.
- Resilience: Circuit breakers and fallbacks for external services.
- Observability: Structured logs, metrics and alerting on queue depth/latency.
- Permissions: Role-based access at tool and data level.
Problem
Operations and legal teams lose thousands of hours manually reading PDFs, extracting key clauses, and copy-pasting data into CRMs or practice management software. Standard OCR templates fail when a document format changes slightly. Human data entry is slow, expensive, and prone to transcription errors.
Without an intelligent extraction layer, documents become a physical bottleneck that halts digital workflows.
- Manual Bottlenecks: Staff spend 4+ hours a day just reading and categorizing incoming PDFs
- Brittle Templates: Traditional OCR breaks if a vendor moves the "Total Amount" field one inch to the left
- Transcription Errors: Copy-pasting complex IDs, dates, and financial figures leads to costly downstream mistakes
- Missing Context: Documents are stored in Google Drive without extracting the metadata needed for search or reporting
- Delayed Execution: Workflows pause until a human can read the document and click "Approve"
Architecture
A resilient extraction pipeline utilizing Vision-Language Models (like Claude 3.5 Sonnet) to read documents contextually. It handles varied formats, extracts structured JSON payloads, validates the data against business rules, and routes the document to the correct system or human reviewer.
Intake Webhook
Receives documents via email parsing, form uploads, or API endpoints. Handles PDF, DOCX, and image formats.
IngestionVision-LLM Extractor
Uses Claude/GPT-4V to read the document contextually. Bypasses rigid OCR templates. Understands tables, messy handwriting, and varied layouts.
ProcessingSchema Enforcer
Forces the LLM output into a strict JSON schema (e.g., extracting exactly: ClientName, ContractValue, ExpirationDate, LiabilityClause).
ValidationConfidence Scorer
Evaluates the extraction quality. If the document is blurry or the LLM is uncertain, the agent flags it for human review.
QualitySystem Sync
Pushes the extracted structured data into the CRM (HubSpot/Salesforce) or ERP, and uploads the original file to secure storage with metadata tags.
IntegrationHuman-in-the-Loop UI
Provides a side-by-side dashboard for human reviewers to quickly verify flagged documents and correct extraction errors, training the system.
Interface