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Solution: Automated quoting

Writing quotes takes too long, and slow quotes lose jobs

From a request to a draft quote in minutes: your price rules calculate the numbers, AI drafts the wording from similar past work, and a person approves before it goes out.

Sounds familiar?

Signs this is costing you time

For many service and trade businesses, the quote is the sale. The customer compares a few providers, and the one who replies first with a clear, professional quote often wins. Yet quotes are usually built by hand from old documents, spreadsheets and memory, by the same senior people who are busiest.

  • Customers wait days for a quote while competitors reply the same day.

  • Only one or two senior people know how to price complex jobs.

  • Quotes are copied from old documents, and old details slip through.

  • Pricing mistakes are found after the job is won, when they cost money.

  • Nobody can easily see which quotes were won or lost, and why.

Before and after

How it works today, and how it works after

Before

How it works today

  1. A request arrives by email or phone with incomplete details.

  2. Someone asks follow-up questions over several days.

  3. The quote is assembled from an old document and a spreadsheet.

  4. A senior person checks the numbers when they find time.

  5. The quote goes out as a PDF and is tracked in someone's inbox.

After

How it works after

  1. A structured request form captures what pricing needs.

  2. Your price rules calculate the numbers automatically.

  3. AI drafts the scope and wording from similar past quotes.

  4. A person reviews, adjusts and approves in one screen.

  5. The quote is sent, tracked and linked to the CRM.

What we build

What we build

Smart request form

Asks the questions your pricing needs, adapting to the type of job, so fewer follow-ups are needed.

Pricing engine

Your rates, materials, margins and discounts encoded as tested rules, not guesses by AI.

AI drafting

Scope descriptions and cover letters drafted by an AI copilot from the request and similar past quotes.

Past work library

Previous quotes and proposals searchable by meaning with retrieval, so good wording is reused.

Review and approval

One screen to check numbers, edit text and approve, with limits on who can approve what.

Delivery and tracking

Branded PDFs or online quotes, e-signature, reminders and win or loss tracking.

In practice

What it looks like in practice

An illustrative walk-through, not a client story.

A commercial cleaning company receives a request for nightly cleaning of a three-floor office. The website form asks for floor area, number of restrooms, frequency and special requirements. The pricing engine calculates hours, labor and supplies from the company's own rates.

AI drafts the scope of work, drawing on wording from similar past office contracts, and a cover letter that mentions the client's request for eco-friendly products. The operations manager reviews the draft, adjusts one line item for a difficult access requirement and approves it. The client receives a professional quote the same afternoon, and the CRM tracks when it is opened.

Models and tools

AI models and MCP servers that usually fit

We choose models by task, data sensitivity and cost, and test them on your real examples before we commit. These are common starting points, not a fixed recipe.

How we build it

The services behind this solution

Most solutions combine two or three of our services. These are the ones this one usually needs.

AI copilots

AI helpers built into your product or internal tools that draft, summarize and suggest while people decide.

Document automation

Read invoices, forms, contracts and IDs, pull out the right fields and route them for review.

Internal tools

Custom software for your own team: trackers, approval flows and back-office systems that replace spreadsheets.

Industries where it fits best

Honest limits

Limits and human checks

AI should not set prices. Numbers come from your rules, which are written and tested like any other software, so they are predictable and auditable. AI drafts the words around those numbers, and a person approves every quote before it is sent.

The quality of drafts depends on the quality of past quotes. If old documents contain outdated terms or mistakes, the AI may repeat them, so we clean up the library first and mark which documents are good examples. Complex or unusual jobs still need expert judgment; the system saves time on the routine parts so experts can focus on those.

Keep exploring

FAQ

Questions about automated quoting

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