INDUSTRY · LOGISTICS
1,000-stop routing computed in 30 seconds. Stockouts down 35% in 90 days.
We build route optimization engines, demand forecasting models, and carrier integration layers that handle the real-world fragility of logistics at operational scale.
WHY
Logistics systems fail at the edges: carrier API outages, address validation edge cases, weight and zone discrepancy handling, multi-leg shipment reconstruction. We've built carrier integration layers across FedEx, UPS, USPS, and regional carriers with normalization, retry logic, and fallback routing that survives real-world fragility.
Route optimization is a constraint satisfaction problem at scale. Time windows, vehicle capacity, driver hours-of-service, customer priority tiers, and fuel cost all interact. We've shipped optimization engines using both exact solvers for small fleets and metaheuristic approaches for 1,000+ stop problems, with solutions computed in under 30 seconds.
Inventory forecasting in supply chains requires models that handle seasonality, promotions, supplier lead time variability, and SKU proliferation simultaneously. We built demand forecasting systems that reduced stockouts by 35% and cut excess inventory holding costs by 20% in the first 90 days.
WHAT WE BUILD
Relevant capabilities
CAPABILITY · 01
Algorithms & Optimization
Route optimization engines, load planning algorithms, and carrier selection models for cost minimization.
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CAPABILITY · 02
AI & Machine Learning
Demand forecasting models, shipment delay prediction, and warehouse slotting optimization.
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CAPABILITY · 03
Data Engineering
Carrier event pipelines, inventory data warehouses, and supply chain visibility data aggregation.
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CAPABILITY · 04
Automation & Integration
Carrier API integrations, WMS connectors, and order management system synchronization.
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CAPABILITY · 05
Custom Platforms
Fleet management dashboards, shipment tracking portals, and warehouse operations tooling.
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CAPABILITY · 06
Real-Time Systems
Live shipment tracking feeds, driver location updates, and exception alerting pipelines.
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ETA ACCURACY
Route optimization and ETA accuracy
Point-estimate ETAs lie. We ship windowed predictions with explicit confidence bands and track accuracy per route, per driver, per time of day. Inputs: historical leg times, current traffic snapshot, weather, driver speed profile, dwell-time distribution per stop class. The optimizer respects hours-of-service, time-window constraints, capacity, and a customer-priority weight. Re-optimization triggers on any 15-minute slip versus plan. ETA accuracy measured as the percentage of stops where the actual arrival falls inside the predicted window, not a single-second match. Production target: 88%+ within window for 1,000-stop urban routes, 92%+ for regional line-haul.
Optimization horizon
1,000+ stops in under 30 seconds
ETA window
Predicted band, not point estimate
Re-optimization trigger
15-minute slip vs plan
Constraints
HOS, time-window, capacity, priority
Tracked accuracy
Per route, driver, time-of-day
Target window-hit rate
88%+ urban, 92%+ regional
SAMPLE WORK
What we've shipped
Route optimization engine solving 1,000+ stop delivery problems in under 30 seconds using adaptive metaheuristics.
Demand forecasting model across 50K SKUs that reduced stockouts by 35% and cut excess inventory holding costs by 20%.
Carrier integration layer connecting 8 carriers with normalization, retry logic, and automatic fallback routing.
Shipment visibility platform aggregating tracking events from 15 sources into a unified timeline per order.
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