By use case: Quote generation
Quote automation, from RFQ to approved price.
Enquiries read as they arrive, items matched to your catalogue or item master, prices and margins applied by rule, and the quote approved and sent the same day.
- First workflow live
- RFQ email to queue, usually inside the first sprint
- Usually owned by
- Sales engineers and estimators
- Built on
- SAP Business One, Zoho / Tally, HubSpot
- Judged on
- Quote turnaround and Lines matched automatically
In short
Quote automation reads each RFQ from email or a form, extracts items, quantities and dates with AI, matches them to your item master, applies price lists, costs and margin rules, routes quotes outside your limits for approval, and creates the quotation in your ERP or CRM. Items it cannot match go to an estimator, not the customer.
Who this is for
The people who usually own quote generation.
- Sales engineers and estimators
- Inside sales and sales coordinators
- Sales managers who approve discounts
- Owners of distributors and manufacturers
Is this you?
If three of these sound like your week, this page is about your team.
- RFQs are retyped before they can be priced
- One or two people are the only ones who can quote
- Two people would quote the same job differently
- You do not know how many enquiries went unanswered last month
- Discount approvals happen by phone or email
What it costs today
Where quote generation loses the hours.
RFQs arrive as free text and attachments
Part numbers in a PDF, a spreadsheet, or a line in an email, retyped into the system before anyone can price them.
Matching to your catalogue is slow
Customers use their own codes and descriptions, so finding the right item depends on someone who knows the range.
Pricing lives in heads and spreadsheets
Discounts, costs and margins applied from memory, so two people quote the same job differently.
Quotes go out late, or not at all
Enquiries wait in an inbox while the team works on orders, and nobody tracks which were answered.
For estimators and sales
Quote the same day the RFQ lands.
Send us a week of RFQs. We will show how many lines would have matched on their own and where the time went.
Walk us through your RFQs
By company size
Quote generation at 10 people, and at 1,000.
- 1 to 10
The owner prices every job from memory, in the evening.
- 11 to 50
One estimator who knows the catalogue, and a queue of RFQs that grows when they are away.
- 51 to 200
Hundreds of enquiries a month, a large item master, and margins that vary by who quoted.
- 201 to 1,000
Several branches and price lists, approval chains by email, and no view of quote to order conversion.
What we build
What we build for quote generation teams.
- 01
RFQ intake and AI reading
Enquiries from email, forms and portals collected in one queue. An AI step pulls out customer, items, quantities and required dates from text and attachments.
- 02
Item matching
Each line matched to the item master by code, description and past customer references, with a confidence score. Unclear lines are flagged for an estimator.
- 03
Pricing and margin rules
Price lists, customer terms, costs and minimum margins applied by rule, so the number is consistent and explainable.
- 04
Approval and quote creation
Quotes outside discount or margin limits routed for approval. Approved quotes created in the ERP or CRM and sent, with follow-up tracked to order.
The workflows
The quote generation workflows, and what sets each one off.
- 01 RFQ email to queue
When an enquiry arrives by email or form
ThenAI extracts customer, items, quantities and dates
- 02 Item master matching
When rFQ extracted
ThenLines matched to items, unclear lines flagged
- 03 Price and margin check
When all lines matched
ThenPrice lists and margin rules applied
- 04 Discount approval
When quote outside discount or margin limits
ThenRouted to the approver with the reason
- 05 Quote follow-up
When quote sent with no response
ThenReminder scheduled for the owner, outcome logged
The numbers we move
How a quote generation build is judged.
Every build has a number written into the blueprint. These are the ones quote generation builds are measured by, and how we measure them.
| Metric | Before | After | How it is measured |
|---|---|---|---|
| Quote turnaround | Days | Same day for standard items | Time from RFQ received to quote sent |
| Lines matched automatically | All typed and searched | Most matched, the rest flagged | Share of RFQ lines matched without an estimator |
| Margin consistency | Varies by person | Within rules, exceptions approved | Quotes below minimum margin and who approved them |
| Unanswered RFQs | Unknown | Visible and chased | Open RFQs by age |
Built on
The tools you already run, where we can.
Every tool we connect has its own page in the integrations directory.
Proof
Proof from builds like this.
- Customer story
Enquiry and quotation platform on SAP
An enquiry platform for an industrial parts distributor that reads emailed RFQs with AI, matches parts against the SAP item master and tracks them from enquiry to order.
Read the story - Customer story
Part finder and product catalogue
A part finder for a cable manufacturer that decodes article numbers across the catalogue and turns enquiries into documents, the matching step behind every quote.
Read the story - Customer story
Internal operations tool replacing SmartSuite
Quotations, contracts and a pipeline dashboard in one operations tool, with win rate tracked from the same records.
Read the story
How it starts
An audit of the quote generation work your team still does by hand.
One to two weeks. We map the workflows, put an hours-a-week figure on each, and give you a costed blueprint in order of payback. The audit is credited in full to the build.
- Week 1 to 2Audit. We sit with the people who do the work and map it.
- Week 3Blueprint. Fixed scope, fixed price, the first build chosen by payback.
- Week 4 onBuild in two-week sprints, working software every second Friday.
- After go-liveSixty days of hypercare, then a care plan or run it yourself.
What this does not do
So the scope is clear before the call.
- Fully engineered, one-off projects where every quote is a design exercise. We can speed up intake, not the engineering.
- Setting your pricing strategy. We apply the rules you decide.
- Replacing your ERP.
Terms, defined
Quote generation terms, in plain English.
- RFQ
- Request for quotation: a customer asking for prices on specific items and quantities.
- Item master
- The list of every product or part you sell, with codes, descriptions and units, held in your ERP.
- Cross-reference
- A table that maps a customer’s or manufacturer’s part codes to your own items.
Questions
What people ask about quote generation automation.
Does AI set the price?
No. AI reads the enquiry and suggests item matches. Prices, discounts and margins come from your price lists and rules, and anything outside your limits goes to a person for approval. Every quote can be explained line by line.
What if the customer uses their own part numbers?
We keep a cross-reference of customer codes to your items. Each time an estimator confirms a match, it is saved, so the same code matches automatically next time.
Can it work with SAP or our ERP?
Yes. We have built RFQ intake that works with SAP from enquiry to order, and we connect to other ERPs and CRMs through their APIs. Where an older system has no clean API, we agree the approach during the audit.
What does quote automation cost?
Reading RFQs from an inbox into a structured queue is around $2,500. Item matching, pricing rules, approvals and ERP integration usually land between $5,000 and $15,000, more where the catalogue or the ERP is large. Try the ROI calculator (/tools/roi-calculator/) with your weekly RFQ volume.
Reading
- Strategy
Automation vs AI systems: what moves the number
Stacking task automations stalls at around twenty. The difference between a task and a system, the five layers that hold it up, and what to rebuild first.
- Systems
Six months of AI agents in production: what broke
Not the model. Field renames, a drifting confidence score, a new invoice format and a Slack channel nobody read. What failed in our agents, and what we changed.
Related
- Industry
Manufacturing
Production entered once at the machine. Stock that matches the shelf. Dispatch paperwork and customer messages that send themselves. All of it live this morning, not typed up tonight.
- Industry
Distribution and logistics
Collections on a schedule, proof of delivery the same day, one stock count across every channel, and customers who hear before they ask.
- Use case
Document extraction
PDFs, forms and emails read into structured records, checked, and filed where your team already works.
- Use case
Order to cash
Orders entered once, fulfilment visible to everyone, invoices raised the moment goods leave, payments matched as they land and reminders sent before anyone has to ask.
By use case: Invoice processing, Lead routing, Customer onboarding, Employee onboarding, Order to cash, Ticket triage, Document extraction, Approval workflows, Inventory sync, Weekly reporting, Client portals
Quote generation
Get the quote generation hours back.
Bring the one quote generation workflow your team still runs by hand. You leave the call with a yes or no on fit and the first thing we would build.
Book the call