Automated B2B Order Processing & ERP Pipeline
A high-throughput automation system designed to eliminate manual data entry in B2B order fulfillment. The solution ingests inbound purchase order PDFs, extracts complex tabular line items with custom OCR and parsing models, validates pricing against database schemas, and issues authenticated API payloads to the central ERP system with complete audit tracking.
Business & Technical Friction
- Inbound purchase orders arrived in over 40 distinct PDF layout formats from international buyers.
- Manual data entry resulted in shipping delays, transcription errors, and inventory discrepancies.
- Legacy ERP system lacked modern webhook ingestion and required rate-limited batch sync.
Architecture & Implementation
Engineered an event-driven ingestion pipeline with a Python extraction worker. An intelligent schema normalizer converts diverse document formats into standardized JSON payloads, with an exception queue for edge cases and guaranteed idempotent delivery.
Core Technologies Deployed:
Engineered Features & Workflows
Key operational capabilities built into this platform to solve the core business challenges.
Intelligent PDF Line-Item Parser
Extracts multi-page table line items, SKUs, quantities, and delivery notes with 99.8% precision.
Automated Validation Engine
Cross-checks client pricing tiers and available warehouse quantities before ERP commit.
Operator Review Dashboard
A lightweight Next.js triage screen allowing logistics managers to review flagged anomalies in seconds.
Resilient Webhook Queue
BullMQ queue with exponential backoff to handle legacy ERP maintenance windows gracefully.
Measured Impact & Performance
Eliminated 18+ hours per week of manual clerical data entry.
Reduced order transcription error rate from 4.2% to 0.08%.
Achieved sub-4-second end-to-end extraction and validation speed.
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Software & SaaS Development
API Development & Integrations
Connect your internal applications, third-party SaaS tools, and legacy systems seamlessly.