Logistics and food distribution still run on documents — bills of lading, purchase orders, manifests, proof-of-delivery slips — and most of that paperwork is still processed by hand. A SaaS company set out to change that for a set of large distribution operators, and brought us in to build the production system behind it.
The challenge
The operating reality was thousands of semi-structured documents flowing through email, EDI, and scanned PDFs every day. Staff re-keyed line items, reconciled mismatches, and chased exceptions manually. The cost wasn't just labor — it was latency and errors that rippled downstream into routing, billing, and customer commitments.
The goal was an AI-native platform that could ingest that document flow, understand it, and drive the downstream workflow automatically — while staying trustworthy enough to operate against real inventory and real money.
The approach
We built the system around three layers, in the order that lets each one be verified before the next depends on it:
- Ingestion & extraction. A pipeline that normalizes documents from multiple channels and uses AI to extract structured fields, with confidence scoring so low-confidence extractions route to human review instead of silently flowing through.
- Workflow automation. The extracted data drives the previously manual steps — matching, reconciliation, and exception handling — with clear escalation paths where a human decision is required.
- Optimization. On top of clean, structured data, the platform could begin optimizing routing and selection decisions that were previously made on instinct.
Why it held in production
The difference between a document-AI demo and a production system is everything around the model: how you measure extraction quality before you trust it, where human review is mandatory, and how failures are caught and rolled back. We invested there first — a real evaluation set for extraction accuracy, observability on every step, and permission boundaries on what the system could act on automatically.
The outcome
Workflows that had been fully manual became automated, with humans moved from data entry to exception handling. The platform replaced outdated, paper-based processes with a system that scales with volume instead of headcount — and gave the operators a structured-data foundation they never had before.