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AI Agent & AutomationTechnical Brief

Multi-Modal Document Extraction & Triage Engine

Autonomous ingestion and structured parsing for financial and legal paperwork.

Technologies Deployed
PythonFastAPIOpenAI / Claude APIPostgreSQLDockerRedis Queue

01. Context & Operational Problem

Client Context: Mid-sized operational consultancy processing hundreds of client declarations weekly.

Specific Challenge: Manual document intake resulted in 48-hour processing queues, frequent manual entry errors, and lost billable client hours spent on repetitive classification.

02. Engineering Architecture & Pipeline

Designed an asynchronous multi-stage pipeline utilizing OCR, deterministic schema validation, and structured LLM extraction with validation checkpoints to push clean data into internal ERP APIs.

Step-by-Step Data Flow

  1. Document ingestion via secure webhook
  2. Preprocessing & layout-aware chunking
  3. Structured JSON schema enforcement via instructor/Pydantic
  4. Confidence scoring & human-in-the-loop review fallback
  5. Direct webhook sync into relational client database

03. Measured Production Outcomes

  • ✔Reduced average document intake turnaround from 48 hours to under 3 minutes
  • ✔Zero manual data re-entry for 84% of standard structured forms
  • ✔Deterministic audit trail for all extraction confidence scores

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