Upper Kananaskis Lake in Alberta's Rocky Mountains

Workflow Automation · GPT-5.2

Quote turnaround from 31 hours to 14 minutes on GPT-5.2

A national equipment distributor

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Results

Measured against the case agreed at the start.

14 min

median quote turnaround

97.4%

line items extracted correctly

+11pts

win rate on same-day responses

Models used

Built on the current generation, chosen on evidence.

GPT-5.2

Every model is benchmarked against the client's own historical work before it is trusted with any of it, and it runs on their data inside their controls.

The automation

How the flow actually runs.

Each block is a step in the live automation, from the event that starts it to the record it writes at the end.

End-to-end automation · Inbound request to approved quote Trigger Action Decision Human Output
  1. TriggerRequest lands

    Any inbox, portal or attachment type starts the same flow.

    Email / portal
  2. ActionParse the request

    GPT-5.2 turns messy attachments into structured line items with part numbers.

    GPT-5.2
  3. ActionPrice against real data

    Cost, stock, contract terms and freight pulled live from the ERP.

    ERP
  4. DecisionAbove the approval threshold?

    Small quotes go straight out; large ones route to a named approver.

    Rules
  5. HumanApprover signs off

    One screen showing the price, the margin and the reasoning behind it.

    Human
  6. OutputQuote sent and logged

    PDF issued, CRM updated, follow-up sequence scheduled.

    CRM

Every step is visible and editable by your own team - nothing in the chain is a black box.

The challenge

What the operation looked like before.

Quote requests arrived as email attachments, spreadsheets and PDFs. An internal desk of four people rekeyed them into the ERP, checked stock by hand and priced from memory. Half of the requests were answered after the customer had already bought elsewhere.

What we did

The work, in order.

  • Benchmarked GPT-5.2 against the previous generation on 1,200 historical quote requests before committing.
  • Built the quote desk as a single visual flow that operations staff can read and change themselves.
  • Grounded every price in the client's own cost, stock and contract data rather than in the model's memory.
  • Kept a named human approver on any quote above the agreed value threshold.
“We stopped losing deals to whoever answered first, because now that is us.”
Commercial Director, a national equipment distributor

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