A logistics company was drowning in repetitive tasks. Here’s the ecosystem we built to fix it.
Category: Tech | Read time: 5 min
The operations manager sent us a spreadsheet. Every task her team did in a typical week. Data entry. Email parsing. Document processing.
Forty hours a week on work that needed a brain but not creativity.
She didn’t want a new system. She didn’t want to fire anyone. She wanted to take the boring stuff off her team’s plate so they could do the work that actually requires judgment.
Six weeks later, 60% of those hours were automated.
The mess
Mid-sized freight forwarder. Hundreds of shipments per week. The workflow:
Customers email shipment requests — each in a different format, with different details, different attachments. Someone reads every email, extracts the data, types it into the TMS. Repeat 200 times a week.
Shipping documents — bills of lading, proof of delivery, customs forms — arrive as PDFs, scans, photos. Someone reads each one, verifies it, updates the system.
The team spent most of their week on copy-paste. Not problem-solving. Not client relationships. Copy-paste.
What we built
Not one giant system. Three specialized AI agents, orchestrated through an automation.
- Document processor — OCR + AI reads bills of lading, customs declarations, invoices. Understands document structure — knows where to find a shipper name on a BOL versus a customs form, even when layouts differ completely.
- TMS data entry — Takes extracted data from the first agent, populates system fields, cross-references existing records, creates audit trails.
- Exception router — Unusual requests, conflicting data, incomplete info get flagged and routed to the right person with context.
Timeline
No six-month planning phase. Six weeks from kickoff to production.
- Weeks 1-2: Process audit. Shadow the team. Document every manual step. Score automation candidates by effort and ROI.
- Weeks 3-4: Build and test. Core agents developed, tested against real historical data. Working prototype in hand by day 14.
- Weeks 5-6: Integration, training, monitoring setup. Production deployment.
- Week 7+: Optimize. Refine rules, expand scope, track performance.
Key point: a working prototype in two weeks. Not a presentation. Not a roadmap. A system processing real data.
The numbers
| Metric | Result |
|---|---|
| Manual data entry reduction | 60% |
| System accuracy | 99% |
| Weekly hours recovered | 30+ |
| Time to working prototype | 2 weeks |
The agents are conservative by design: escalate when uncertain, never guess.
The math
Payback: under 4 months. After that, net savings every month. Unlike a new hire, it doesn’t call in sick and scales linearly with volume.
What we learned
- Audit first, build second. Two weeks mapping workflows shaped every decision. Without it, you automate the wrong things.
- The team gets upgraded, not replaced. They stopped doing copy-paste. They started doing exception handling, route optimization, client relationships. The agents handle volume; humans handle judgment.
- You don’t need enterprise tools. Open-source automation, standard APIs, custom scripts. No six-figure licenses. No 18-month timelines.
