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Logistics Supply Chain for Businesses
Playbooks and case studies covering logistics supply chain for businesses.
5 Mistakes Teams Make When Digitizing a Transport Marketplace
Freight marketplace platforms rarely fail because of matching algorithms. They fail because the supply side was modeled as passive inventory. Here are the five mistakes we see mid-sized brokerages make when digitizing, and how to recover from each.
Questions to Ask Before Hiring an AI Logistics Dev Partner
Vetting an AI logistics development partner after an internal build stalled? These are the 18 questions that separate real freight-platform builders from generic app studios pitching an ML demo.
Build vs. Buy Your AI Matching Engine: A Decision Framework
Most build-vs-buy frameworks for AI matching engines treat this as a cost decision. It isn't. It's a data-density question — and the answer usually surprises the people asking it.
How Vahak Onboarded 800K Trucks Without Breaking the Marketplace
Vahak crossed 800,000 trucks on its platform without watching match quality collapse — a rare outcome for two-sided logistics marketplaces. The unlock wasn't more onboarding data. It was redesigning the matching model to learn from behavior after signup.
Questions to Ask Before Hiring an AI Logistics Dev Partner
Most AI logistics vendors can demo a dispatch dashboard. Far fewer can model lane exclusions, HOS rules, and carrier telemetry dropouts. Here are 15 questions that expose the difference before you sign.
How Vahak Onboarded 800K Trucks Without Killing Match Quality
Vahak's match rate started degrading the moment supply outpaced demand on certain lanes. Here's the engineering call that fixed it — a separate ranking pathway for unproven carriers — and the lessons that generalize to any two-sided transport marketplace.
Batch vs. Real-Time AI Inference: Pick the Right One
Most operational AI features don't need the freshest prediction — they need the most accurate one. Here's a decision framework for choosing between batch and real-time inference, written for logistics and operations teams watching their cloud bill climb.
Temporal Fusion vs. LSTM: Pick One for Demand Forecasting
Most TFT-vs-LSTM comparisons optimize for benchmark RMSE on clean data. Here's how the two architectures actually behave in production demand forecasting — covariates, retraining cadence, and serving cost at SKU scale.
How Vahak Onboarded 800K Trucks Without Breaking Its AI
When a transport marketplace adds tens of thousands of new carriers a month, the AI matching model doesn't just slow down — it gets confidently wrong. Here's the architectural call that separated cold supply from warm supply and stopped the degradation.
Questions to Ask Before Hiring an AI Logistics Partner
A field-tested set of adversarial questions to ask any AI logistics vendor before signing — designed to expose whether they've shipped at real fleet scale or just demoed on clean CSVs. Includes what good and red-flag answers actually sound like.