The agent (the subject of our flagship case) had been designed with cost controls, but production reality had outgrown them: longer documents, longer conversations, more retrieved context per answer than answers needed. Token spend per query had crept up month over month, and finance’s question — ‘what happens to this bill at ten times the users?’ — had an uncomfortable answer.
Token consumption fell 90% with evaluation accuracy intact — and the client’s growth question got a new answer: the same budget now supports roughly ten times the usage. The per-query cost instrumentation stayed, folded into the monthly AI operations scorecard, so the next creep gets caught at 10%, not 900%.
Per-query cost tracking by use case and conversation pattern — the map of where tokens actually went.
Repeated context cached; conversation history compacted to what the current answer needs instead of the whole transcript.
Questions classified by difficulty; simple lookups routed to cheaper models, hard reasoning kept on the frontier — routing rules tuned against the evaluation set.
Retrieved content trimmed to the passages the answer uses, with citation quality checked after the cut.
Every change gated by the golden-question evaluation set: accuracy had to hold before the saving shipped. It did.
Thirty minutes. Bring the problem as it actually is — the stalled pilot, the bill, the spreadsheet everyone quietly maintains. We’ll tell you honestly whether we’re the right builder for it.
You leave with a straight answer on fit — and if it fits, a free Blueprint to follow.