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Quote-prep assistant

Writing quotes is rarely hard – but it's the same work over and over: read the inquiry, dig out the building blocks, look up prices, phrase it. And because that takes time, it gets left.

Quote-prep assistant

01Quote-prep assistant

The assistant takes the preparation off you, not the decision: it reads the inquiry or brief, pulls the fitting text and service building blocks from your stock, proposes a draft – and lists clearly what's still open before anyone can name a price. Instead of starting from zero, you start at eighty per cent. The last twenty – check, adjust, decide, send – stay yours. That's exactly the order that makes it an assistance system rather than a quote machine you can't trust.

Straight up: the quality stands or falls with your quoting logic. If prices and standards live only in people's heads, the first step is writing them down – that's part of the project, not a precondition for it. And binding price and expert decisions are only prepared by the system as a draft: checking and sending is done by people.

02Demo

simulation · curated examples

On the left an inquiry brief, as the inquiry qualifier delivers it. On the right, what the assistant prepares from it – and what it deliberately leaves open. Two examples to click through.

// brief (incoming)
// prepared draft (internal)

The draft never leaves the building automatically: check, adjust, decide, send – that stays reserved for humans.

03How it works

  1. 01

    Make the quoting logic visible

    Which packages, prices, building blocks and limits are there – and where is that written down? Often this step is the most valuable of the whole project: the quoting knowledge gets structured in writing for the first time.

  2. 02

    Curate the text building blocks

    Your best quote copy becomes a library: introductions, service descriptions, terms – in your tone, not in AI-speak.

  3. 03

    Build the draft flow

    Inquiry or brief in, draft plus open-points list out – as an internal tool, as lean as possible.

  4. 04

    Test on a real case

    The last ten real inquiries run through the assistant: where does it hit, where does it miss? Building blocks and rules get sharpened.

  5. 05

    Take it into the day-to-day

    The team learns the flow: read the draft, clear the open points, adjust, send. The assistant becomes routine – and the quoting time shrinks measurably.

04Stages

The entry point needs no software of its own – just your logic in good shape.

Tier 1

Entry: building blocks + templates

A curated text-building-block library plus prompt templates for your existing AI tool – the work aid that helps right away.

Tier 2

Extended: the internal mini tool

A qualified inquiry in, draft and open-questions list out – a small tool of your own for the quoting process.

Tier 3

Full build: connected

Connection to a quote database, CRM and email drafts – the assistant sits right in the middle of your toolchain.

05Technical implementation

  • Input: a qualified inquiry, brief or call notes – the more structured the input (see the inquiry qualifier), the better the draft.
  • Knowledge base: service building blocks, text building blocks, pricing logic, sample quotes and tone – curated, not thrown together.
  • The language model generates the draft, open questions and building-block suggestions; it invents no prices where the logic yields none.
  • Output to suit your process: an email draft, Google Doc/Word, CRM note or internal dashboard.
  • The safe start is export rather than send – a direct email connection only comes once the flow has proven itself.
  • Hosting: as a small internal tool or a function in the existing website/portal stack.

for context · This shows how something like this usually gets built – not how it has to be built for you. What actually fits is worked out in the project: around your existing setup, your team, and what stays easy to maintain.

06Related building blocks

See all use cases