QuoteFlow Agent

For

Sales leads at trade suppliers and B2B service firms

Solves

Quotes sit in inboxes, forgotten, while you chase them manually, and a single missed follow-up loses a job.

Delivers

Drafted quotes, sent quotes, follow-up reminders, booked jobs

Built in

about 3 weeks of creation time, MVP in 3 days

Investment

$6,500 for the MVP, $21,500 for the full product

Run it

Inside your business, or as part of your offer to clients

For sales leads at trade suppliers and B2B service firms, turn call recordings and email threads into drafted quotes, scheduled follow-ups, and booked jobs.

1. Ingest call recordings and email threads.
2. Generate quote with line items and pricing.
3. Send quote via email.
4. Schedule personalised follow-ups.
5. Detect client acceptance or rejection.
6. Book job into calendar and notify team.

What goes in, what comes out

The workflow

1. In
   Start with

   Call recordings, email threads, pricing rules

2. 1

   Forward call recording or email thread

3. 2

   Generate draft quote

4. 3

   Review and edit draft

5. 4

   Approve for sending

6. 5

   Send quote

7. 6

   Schedule follow-ups

8. 7

   Book job on acceptance

9. Out
   Finish with

   Drafted quotes, sent quotes, follow-up reminders, booked jobs

AI does the heavy lifting, people stay in charge

Use language models to transcribe calls and extract quote details, and to draft line items and cover notes. Keep pricing and terms in structured fields. Validate against pricing rules through deterministic checks. A human reviews the first draft and sets pricing rules before anything is used.

What your team sees

Key screens: Quote inbox, draft review, follow-up schedule. Use a central dashboard for incoming quotes, a draft editor with line items and pricing, and a timeline view for follow-ups. Let users approve or edit drafts before sending. Display sent, pending, and booked states. Provide a client-facing link for acceptance. In this product, the first view is quote inbox, followed by draft review and follow-up schedule.

Accounts and administration

User roles, quote versions, approval steps, follow-up logs, audit trail, pricing rule overrides, and client communication history.

Integrations and data access

Starts with email (Gmail, Outlook), then CRM (Salesforce, HubSpot), calendar (Google, Outlook), and invoicing tools (QuickBooks, Xero).

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   3 days

   One buyer segment, one recurring use case. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   4 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   7 days

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will sales leads at trade suppliers and B2B service firms use it to solve "quotes sit in inboxes, forgotten, while you chase them manually, and a single missed follow-up loses a job"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Run a paid pilot with one trade supplier; baseline is current quote-to-booking time and follow-up rate, decision is whether the agent reduces time by 50% and increases bookings by 20%.
4. Measure, then decide. Track two measurable outcomes: average time from enquiry to booking and and percentage of quotes that result in booked jobs. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. One buyer type, one use case (quote drafting from email threads), first two modules (draft generation and follow-up scheduling), manual review of every draft before sending.

After the MVP. Automate follow-up personalisation based on client behaviour, integrate with calendar and invoicing, and add voice call transcription for real-time drafting.

What the build depends on. Needs reliable voice transcription, language model API access, and CRM or spreadsheet integration for quote storage.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test pricing at $199 per user per month, with a free trial for the first 10 quotes, as a hypothesis.

Message to test

Stop chasing quotes; let your quote draft, send, and follow up on its own.

Where to find buyers

LinkedIn ads targeting sales managers, trade industry forums, and partnerships with CRM resellers.

Lead magnet

Free demo showing how a call recording becomes a booked job in under 10 minutes.

The first 30 days

1. Week 1: Build quote ingestion from email and call transcripts.
2. Week 2: Develop draft generation with pricing rules.
3. Week 3: Implement follow-up scheduling and client acceptance detection.
4. Week 4: Pilot with one trade supplier, refine based on feedback.

Paid pilot

Run a paid pilot with one trade supplier; baseline is current quote-to-booking time and follow-up rate, decision is whether the agent reduces time by 50% and increases bookings by 20%.

Success metrics

Two measurable outcomes: average time from enquiry to booking, and percentage of quotes that result in booked jobs.

Retention and expansion

Keeps earning through monthly subscription, with expansion as the agent learns pricing rules and integrates deeper into CRM and invoicing workflows.

Why clients would pick it

The agent learns pricing rules and client preferences over time, making it harder to replicate as it accumulates historical quote and booking data.

Alternatives and positioning

Manual quoting in spreadsheets or CRMs, email templates, and human follow-up calls; this differs by automating the entire quote-to-booking loop.

Main delivery costs

Main delivery costs are cloud transcription, language model API usage, and CRM integration maintenance.

Limits on who can approve quotes, permissions for editing pricing, audit log of all actions, and a safety check that the agent never sends a quote without human approval. It must not alter pricing outside approved rules or send follow-ups after a client has declined.