Question 1

Route planning done inside the LLM instead of around it

Accepted Answer

Route optimisation is a solved deterministic problem. Asking the model to "pick the best order of stops" wastes tokens and drifts under load. The model should handle ambiguity (driver requests, natural-language constraints); a typed route solver handles structure.

Question 2

Trip status fabrication

Accepted Answer

The model emits shipment IDs, carrier IDs, or driver names in UI elements. When the ID doesn't match, the button is broken. Same pattern as concrete dispatch: source IDs from tool JSON, never from LLM memory.

Question 3

Multi-vendor routing orchestration paying twice

Accepted Answer

Fleet-ops products increasingly call multiple providers (Anthropic for reasoning, OpenAI for tool use, Gemini for vision). A tiered routing table maps request type to the cheapest model that meets quality. Built once, re-benchmarkable quarterly; typical saving is 30–50% of LLM spend with no quality drop.

Question 4

Dispatcher copilot asks questions the telematics layer already answered

Accepted Answer

Live truck position, current shift status, recent jobs — all in the telematics API. A prefill layer replaces three-to-five Q&A turns per interaction.

AI-копилоты для fleet- и логистических SaaS