AI Document Processing Pipeline

PDF → Claude vision → structured extraction → CRM record creation. Processing 2,847 docs/month at 98.7% accuracy.

2026 AI Pipeline Engineer AI & Bots

Claude APIPDF Processingn8nDocument ExtractionCRM Integration

taimoorakhtar.com/projects/ai-document-processing

Introduction

An insurance broker was processing claim documents manually — 3 staff members spending half their day reading PDFs and transferring data into the CRM. Errors were common, throughput was capped at ~100 docs/day, and scaling the team felt like the only growth path. We built the alternative.

The Challenge

Document AI in 2024 was hit-or-miss. OCR-only solutions struggled with scanned PDFs, tables, and handwriting. LLM-only solutions confidently hallucinated values when they couldn't read the source. The challenge: build something that knew when to trust itself and when to escalate to a human.

The Solution

Built an n8n pipeline using Claude's vision capability for direct PDF reading (no separate OCR step). Structured extraction defined by JSON schema per document type. Confidence scoring on each extracted field. Below-threshold extractions routed to human review queue. Above-threshold extractions auto-create CRM records.

Technical Deep Dive

Document classification. First Claude call: classify the document type from PDF content (claim form, medical report, ID verification, billing statement). Routes to type-specific extraction prompt.

Structured extraction with JSON schema. Type-specific extraction prompt enforces JSON schema output with explicit fields, types, and required-vs-optional. Schema includes a confidence field per extracted value.

Confidence-based routing. Fields with confidence below 0.85 flag the entire document for human review. Above 0.85 across all fields → auto-process. Tunable threshold per document type.

Human review queue. Flagged documents queue in a Retool interface with the source PDF + AI's tentative extraction + confidence scores. Human corrects, confirms, the system learns from corrections.

CRM record creation. Approved extractions create GHL contact + opportunity records with all extracted fields mapped to custom fields. Document attached to the contact. Audit trail per record.

Key Features

Direct PDF reading via Claude vision (no separate OCR)
Per-document-type structured extraction schemas
Confidence-based human review routing
Retool interface for queue management
Full audit trail per processed document

Results & Impact

2,847 documents per month processed at 98.7% extraction accuracy
Staff time on document processing reduced 75%
Throughput capacity 10x without team expansion
Zero data-entry errors in auto-processed documents (manual processing baseline: ~3%)

Lessons Learned

"Confidence-based routing is the unlock for production document AI. Pure automation is too risky."

"Don't separate OCR from understanding. Modern vision LLMs handle both in one pass with better results."

"Always build the human review queue first. The AI gets better when the reviewers see and correct edge cases."

Related Work

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