Question 1

How much can a production dispatch copilot be optimised without changing the model?

Accepted Answer

Typical reductions are 40–60% on token costs per happy path, with the best cases reaching ~60%. Same model, same product. Latency variance collapses at the same time — tail traces that used to spike 10–15 seconds stop appearing.

Question 2

Which industries does this apply to?

Accepted Answer

Dispatch, fleet, field-service, concrete, logistics, and industrial-operations SaaS. Any product where the AI copilot walks a user through a structured operational workflow — orders, dispatches, tickets, rosters, routes.

Question 3

What is the main cause of the waste?

Accepted Answer

The LLM is almost always doing work the surrounding code should own — UI emission, step ordering, data re-asking. Every one of those decisions costs tokens twice: once to instruct the model, once to retry when it gets it wrong. Removing them is structural, not prompt-engineering.

Question 4

Can you do this without access to our source code?

Accepted Answer

No. We need the orchestration layer — system prompt, tool definitions, and a representative sample of production traces. Read-only for the audit; write access comes later if we agree on the change-set.

Question 5

What is the smallest version of this engagement?

Accepted Answer

A two-to-four-week audit with a prioritised punch list and a working proof-of-concept on one item. If you like the output, we do the implementation. If not, you keep the punch list.

AI Dispatch Copilots — Cost, Reliability, and Production Delivery