Use
Your CRM or ERP quote tools cover it
Existing CRM quote functionality, Business Central and Copilot-assisted proposal generation already cover simple, catalogue-based quoting. If your quotes are mostly standard items at list prices, start there.
Quotes & Proposals
Get from customer request to first proposal faster.
Pull the right customer, product, pricing and previous-proposal information together automatically and give commercial teams a strong first draft — without automating judgment that should stay human.
Can AI prepare sales quotes and proposals?
Yes. AI can interpret a customer request, retrieve CRM and product information, assemble standard content and prepare a first quote or proposal. The more commercial judgment, pricing logic or product configuration involved, the more important it becomes to separate standard automation from proprietary decision logic.
The quote is urgent. Assembling it is slow.
A serious quote pulls information from half the company:
- customer context and history from CRM
- current product data and specifications
- pricing, discounts and validity rules
- text and structure from previous proposals
- input from a colleague who owns part of the answer
Meanwhile the customer is comparing you on speed as much as on content.
Use · Extend · Buy · Build
The split that matters here: assembling a draft is standard work; deciding price and configuration often isn't.
- 1
Use
Native capability
- 2
Extend
Connect the missing pieces
- 3
Buy
Specialist solution
- 4
Build
Own the differentiator
Extend
The draft needs your content and context
Connect CRM, product data, pricing and your proposal library in SharePoint, and the first draft stops being generic: right customer context, right products, your language and structure.
Buy
Configuration is the hard part
When products are configurable and pricing has real rules, specialist CPQ and proposal-automation software has already productised the hard part. Buy it rather than rebuilding it.
Build
Your pricing logic is the edge
A proprietary pricing engine, technical configuration logic, bespoke offer recommendations or a margin and discount decision model — build these only when they encode how you win deals, not to save licence fees.
In practice
From request to reviewed draft
The flow for a typical B2B quote request:
- 1
A request comes in
An email or form request is read and matched to the customer record.
- 2
The context is gathered
History, comparable previous proposals, current product data and applicable pricing.
- 3
A first draft is assembled
Your structure, your language, correct items — flagged where information is missing.
- 4
Commercial review
Price, margin and commitments stay with your team. The draft saves the hours, not the judgment.
- 5
The proposal goes out
Days earlier — and the content is logged back to CRM.
What should we measure?
We don't promise percentages we can't back up. We agree the baseline first, then measure:
- quote turnaround time
- hours per quote
- first-draft accuracy
- rework per proposal
- win rate
- margin leakage
We don't force a strategy project in front of an obvious solution.
If the route is clear
We can move directly to configuration or implementation with your existing partner, a specialist product or the right delivery team.
If the route is uncertain
We compare the options or validate the workflow with a Minimal Viable Agent before committing larger budget.
If several opportunities compete
We prioritise them through a ScopeRight Scoping Workshop.
Works with the stack you already have
We start from your current CRM, ERP, email, document management, website and specialist SaaS — not from a new platform.
Frequently asked questions
Will AI set prices in our quotes?
Only if you decide it should, and only within rules you define. The default pattern keeps pricing and commercial commitments as a human review step.
Can it reuse our previous proposals?
Yes — that's one of the biggest wins. Your best previous proposals become the raw material for structure, language and standard content.
We have complex configurable products. Is this still realistic?
Yes, but the route changes. Configuration with real rules is usually a case for specialist CPQ software, with AI handling intake and drafting around it.
How does this differ from Copilot writing a proposal?
A generic assistant writes plausible text. This workflow retrieves your actual customer, product and pricing data first — the difference between a draft you rewrite and a draft you review.
What if the request is vague?
The workflow flags what's missing and drafts the clarifying questions, instead of guessing. Vague requests are exactly where structured intake pays off.
Where this fits
Each service stands on its own — but they connect. Start with a free intake if you're not sure which one you need.
Accelerate AI on your existing enterprise platform
Get more from Microsoft, Salesforce or SAP before adding another AI stack.
Validate a use case with a Minimal Viable Agent
Evidence before scale: validate one use case end to end.
Take a validated use case into operation
Deploy, configure or build — the architecture follows the scope.
Prioritise AI use cases through a Scoping Workshop
From a fragmented list of ideas to a prioritised portfolio.
Define the right scope. Validate it. Implement it the right way.
Before you launch another pilot or commit to an implementation, let us determine what is actually worth validating. A free 30-minute intake, an honest read, a clear next step.
Not sure which service fits? Start with a free intake. We'll tell you whether you need a scoping workshop, an outside-in benchmark, an MVA sprint, a proposal review, an implementation path — or nothing yet.