Logistics & shipping,
built for visibility.

Why logistics IT is hard right now

Most logistics operations we walk into run on five to seven disconnected systems. A TMS for transportation planning. A WMS for warehouse operations. An ERP for finance and inventory. A telematics provider for GPS pings. Customs and trade compliance somewhere else. Customer portals stitched on top. Each one is fine in isolation. Together they create reconciliation overhead, latency, and a single question — where is this shipment right now, and is it on time? — that nobody can answer without checking three dashboards.

The pattern that works: treat every logistics event — pickup, scan, dispatch, transit hop, exception, delivery, settlement — as an event in a unified log. Tied to a shipment identity, a carrier identity, and a customer identity. The screens, KPIs, automated workflows, and AI-driven decisions are all just queries over that log.

What we typically deliver

A production logistics engagement covers some combination of:

• Supply chain analytics — real-time dashboards on shipment status, lead time, OTIF, fill rate, inventory turns, carrier performance, warehouse productivity. Sub-second queries over millions of events.
• Fleet tracking — consolidated view of vehicles across your telematics providers, with predictive ETAs, exception alerts (geofence breach, idle time, fuel drop), and driver performance scoring.
• Warehouse automation — pick-path optimization, slot management, inventory accuracy reconciliation, labor scheduling. AI-assisted where the data supports it; rule-based where it doesn't.
• Route optimization — daily route planning that respects time windows, vehicle constraints, driver hours, and real-time traffic. Solver-based, integrated with the TMS.
• Shipment visibility — customer-facing portals and APIs that surface real-time status from the consolidated event log, not from polling five systems per query.
• AI-assisted operations — demand forecasting for warehouse staffing, anomaly detection on transit times, intelligent triage of exceptions.

The data stack underneath

For most logistics operators we deploy:

• Kafka or a managed queue for streaming events from TMS, WMS, ERP, and telematics into the canonical log.
• Postgres for transactional writes (shipments, orders, exceptions).
• ClickHouse or BigQuery for analytical reads — lead-time distributions, carrier scorecards, warehouse productivity. Sub-second queries over hundreds of millions of events.
• Airflow for orchestration — daily route planning runs, overnight settlements, EDI processing, customer reporting.
• dbt for the model layer with tests on every transformation.
• FastAPI or similar for the customer-facing visibility APIs.

This is the same operational engine pattern we use across our data engineering work — adapted for logistics entities and event types.

Integrating what you already have

Most logistics operators don't want to replace their TMS or WMS — they want them to talk to each other and to the rest of the business. The pattern we use:

• API-first integration where the source system supports it (modern TMS / WMS platforms, telematics).
• Database-level CDC (Debezium against Postgres/MySQL, or polling for older systems) where APIs are limited.
• EDI handling for carrier and customer integrations — ANSI X12, EDIFACT, and the modern REST equivalents.
• File-based bridges for legacy customs and trade-compliance systems.

Each pattern adds latency and complexity, so we pick the simplest one that meets the SLA for the use case.

Implementation timeline

A typical first engagement runs 8–16 weeks:

• Weeks 1–2: Data audit, current-state architecture, target architecture, integration plan.
• Weeks 3–10: Phased build — canonical event log first, then fleet tracking or warehouse automation, then customer-facing visibility, then optimization and AI layers. Each phase ends with a working deployment.
• Weeks 11–16: Migration from any systems being retired, operations training, runbook handover.

Multi-region or multi-warehouse rollouts add 2–4 weeks per additional site once the platform is stabilised.

Pricing

We scope logistics engagements on a fixed-fee basis for the build phase, with optional monthly retainer for ongoing support and feature work. Pricing depends on integration count, geographic scope, and module selection. Most operators find that the consolidated platform costs less to run than the licensing fees they're currently paying across disconnected systems — with meaningfully more flexibility.