AI Quotation Generator
An AI MVP that turns product images into accurate price quotations using extraction and business pricing rules.
Project overview
Quotations stall when a salesperson has to type what they can already see in a photo. Attributes get mistyped, prices drift from the rulebook, and the customer waits.
This AI quotation generator extracts product details from images, applies pricing logic, and produces a professional quote draft. Sales can still edit and approve — the model does the data entry, not the customer relationship.
It was delivered as a practical MVP: useful in the sales workflow, not a research demo that never reaches a PDF.
The challenge
Sales teams were creating quotations by hand from product images, which slowed turnaround and introduced pricing mistakes. The photo already had the information; the quote process ignored it.
The solution
We built an AI quotation generator that extracts product details from images and produces professional quotes with less manual data entry. Pricing rules and an approval step keep sales in control of what the customer receives.
What we built
How we approached it
Start from the photo the sales team already has
The input is a product image, because that is what arrives in the WhatsApp chat or email thread.
Extract attributes, don't guess price
Vision handles description. Business rules handle price, so the model is not inventing discounts.
Draft, then let sales edit
The quote is a starting point. Approval is a person.
Ship an MVP that sales will open
The interface and output format were chosen for the existing quoting habit, not a new science project.
Examples from the workflow
These are the kinds of moments the system was designed to handle — not hypothetical product slides.
A photo from the warehouse floor
Sales uploads the image, gets attributes filled, and reviews a draft quote instead of typing SKU fields from memory.
A repeat product with a pricing rule
Once rules exist, the same item does not get a different price because someone rounded in a spreadsheet.
A quote that needs a human tweak
The salesperson adjusts terms, then sends. AI did not skip the relationship; it skipped the data entry.
Stack and delivery pieces
Outcomes
Questions this case study usually raises
Does the AI set the final price?
No. It extracts product details and applies your pricing rules. Sales can edit and approve before the customer sees the quote. For engagements like A quote that needs a human tweak, we confirm fit during discovery against your stack, volume, and compliance constraints rather than assuming every environment matches the case study one-to-one. Integrations, permissions, and review gates are planned explicitly. A pilot usually proves the highest-value path first, then expands once quality and adoption are clear. That keeps delivery practical and measurable.
What is the input — only images?
This MVP is built around product images because that was the bottleneck. The same pattern can take structured fields when they already exist. That distinction matters for scoping A quote that needs a human tweak: we align on users, boundaries, and non-goals during discovery so the build does not drift into unrelated platform work. Deliverables and success metrics follow that definition. If a lighter or different approach fits better, we say so before heavy engineering begins. Related screens and workflows in the case study show how the scope played out in practice.
Is this a full CPQ suite?
It is a focused image-to-quote MVP. The goal was faster, more accurate drafts in the current sales process, not replacing an entire CPQ stack on day one. That distinction matters for scoping A quote that needs a human tweak: we align on users, boundaries, and non-goals during discovery so the build does not drift into unrelated platform work. Deliverables and success metrics follow that definition. If a lighter or different approach fits better, we say so before heavy engineering begins. Related screens and workflows in the case study show how the scope played out in practice.
Can this be extended to other product lines?
Yes. Extraction, pricing rules, and the approval step are the reusable pieces. For engagements like A quote that needs a human tweak, we confirm fit during discovery against your stack, volume, and compliance constraints rather than assuming every environment matches the case study one-to-one. Integrations, permissions, and review gates are planned explicitly. A pilot usually proves the highest-value path first, then expands once quality and adoption are clear. That keeps delivery practical and measurable.
AI Quotation Generator
We build quotation systems that can read product information, extract attributes, and assemble accurate quote drafts much faster than manual workflows.
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