I build AI agents that automate import logistics operations.
I work inside the Import VIP desk of one of the world's largest container carriers. That's where you learn where freight operations bleed time: invoice matching, container exception hunting, carrier chasing. I build the agents that do that work — grounded in how ops actually run, not how software vendors imagine they run.
| Project | What it does | Try it |
|---|---|---|
| Middle Watch 🆕⭐ | On-water exception monitoring — routing changes, vessel swaps, dwell, ETA drift, transit vs routing guide. Agentic tool-calling + MCP, field-level evidence on every line. 40k containers in 2.3s, 100% recall / 0% false positives | Demo · Code |
| Import Ops Agent | Full daily verification — carrier sites, terminal holds, invoice audit, prioritized exceptions | Interactive demo |
| Freight Document Intelligence | RAG Q&A over full shipment doc packs — page-level citations, cross-doc reconciliation, compliance audit (44/44 tests) | Demo · Code |
| Import Desk Automation | Invoice↔booking matching (97%), overcharge detection, AR audit | Dashboard · Code |
| Container Visibility | Vessel delay detection, 48h detention-risk alerts | Dashboard · Code |
| Carrier Scorecard | Carrier reliability rankings by lane, switching-savings estimates | Dashboard · Code |
All demos run on synthetic data. Deterministic pipelines with a Claude reasoning layer on top — the math never depends on the LLM.
Python · Claude API (tool use) · MCP · RAG · Pandas · GitHub Pages — agents designed for adoption: they output what ops teams already use, flag rather than auto-act, and measure hours saved, not model metrics.
🚢 Import VIP desk @ one of the world's largest container carriers · 📍 Miami · 🇫🇷 French
