Throughput per operator
Structured work queues replaced ad hoc email chains.
Logistics/ops5 screens
Logistics AI
Cut manual shipment and document handling time by roughly 80% with extraction, validation rules, and human QA checkpoints.
Screen walkthrough
A fuller gallery for the product story.
This gallery shows progression, not just a single hero frame. Use it to talk through navigation depth, records, analytics, and workflow context during a call.
/ops
Chapter 01
Brand-aligned visual — client interfaces are anonymized for this launch story.
Overview
Operations teams were retyping shipment data from PDFs, scans, and carrier emails. We shipped extraction pipelines with validation, exception queues, and measurable throughput per operator hour.
Strongest story angle
Use when buyers want document-heavy logistics workflows automated without losing human control at the edge cases.
Observable modules
OCR + extractionBusiness rulesException queuesOperator consoleExports to TMS
Client story
Problem, approach, and outcomes
Context
A logistics operator processed thousands of shipment documents weekly across carriers and lanes.
Problem
Staff manually copied consignment data from inconsistent PDFs and emails into internal systems, creating delays and mis-keys at peak season.
Approach
Built extraction models with domain-specific parsers, added deterministic validation rules, and required human sign-off on exceptions only.
Architecture
Ingestion service → OCR/text pipeline → schema validation → rules engine → Postgres staging → approved writes to TMS APIs → operator UI for exceptions.
Tech stack
PythonFastAPIPostgreSQLLangChainAWS S3Docker
Results
- ~80% reduction in time spent on repetitive document entry (measured on pilot lanes)
- Exception queue kept human oversight on ambiguous shipments
- Fewer downstream billing disputes from bad master data
Timeline
Pilot lane: 6 weeks. Expanded rollout: 8 additional weeks with change management support.
“We stopped hiring temps every peak season just to retype PDFs.”
Why this one works
Three angles worth carrying into the final write-up.
Accuracy gates
Confidence scoring routed low-confidence extractions to QA.
Integration discipline
Clean handoff to existing TMS fields without duplicate records.
Motion outline
This sequence can still become a short teaser.
- 01
Open on the manual email-and-spreadsheet pain.
- 02
Show extraction → validation → TMS push.
- 03
Close on operator hours saved and error rate drop.
Next publishing pass
The structure is now cleaner: better screenshots, stronger conversion paths, and shared page chrome that behaves correctly. The next layer is adding repository-backed build notes and verified outcome data.
Still worth adding
- 1. Verified repository context for stack and architecture notes.
- 2. Approved proof points to replace generic performance language.
- 3. Short teaser renders once the repository evidence is in place.