Proprietary reasoning models trained on $2.3B of live freight operations — lane dynamics, carrier behavior, shipper patterns, and the messy edge cases general LLMs miss.
Freight is a language — of lane codes, service levels, BOL layouts, EDI fragments, and dispatcher shorthand. Veltrum's models speak it natively.
Models the real-time price elasticity, capacity shifts, and seasonal patterns of 42K U.S. lanes and 120 cross-border corridors.
Predicts tender acceptance, on-time risk, and fallout probability per carrier per lane per day — not just as a static score.
Flags shipments 4–6 hours before they break, with 87% precision. Time the agent has to act, not just alert.
Reads BOLs, rate cons, accessorial sheets, and POD scans — even handwritten driver notes — into structured facts.
Understands dispatcher shorthand, broker slang, and shipper urgency cues — across email, chat, and voice.
Your margin floors, carrier blacklists, and SLA commitments become constraints the agent physically cannot violate.
Freight-tuned foundation model. 34B parameters. Trained on operational logs, EDI corpuses, and dispatcher language.
Specialized models for ETAs, carrier acceptance, exception risk, and lane price. Updated hourly from live telemetry.
Your org's constraints and objectives compiled into a verifiable policy layer. The agent plans within it — always.
Autonomy without observability is unacceptable for enterprise ops. Veltrum ships reasoning traces by default.
Every input, weight, policy, and alternative — rendered in plain language and structured data.
Each decision is tagged with a confidence band. Low confidence auto-escalates; high confidence runs silent.
Any autonomous action can be reversed. The trace is attached to the rollback for retraining.
Our team will walk you through training data, evaluation benchmarks, and the exact guarantees behind every autonomous decision.