Case studies / QuoteWeaver
Vertical SaaSOur product · Furnishings tradeMay – Aug 2026

QuoteWeaver

Measure, price and sign a curtain and blinds order in a single home visit.

Built for
Curtain, blind, wallpaper and flooring businesses and their field estimators
Platform
Web app for office and tablet, customer quote portal
Status
In pilot
The live QuoteWeaver landing page pitches quoting curtains, blinds and upholstery in minutes to soft-furnishing businesses.
6 weeksto build the full platform
1 visitmeasure, quote and sign
98.6%of price-list rows imported cleanly by AI
₹3,000per month for 5 seats
Overview

The short version.

QuoteWeaver runs a soft-furnishings business end to end: leads, site measurements, quotes, orders, supplier purchase orders, work orders for the workshop, and delivery. Its centrepiece is a pricing studio where the shop owner sets up their own pricing logic once, without code, so an estimator can price any window on site in seconds.

The first customer was a window-furnishings company in Kochi that ran quoting from a large Excel workbook. The product grew from two small tools we built for them, a measurement form and a quoting sheet, into a multi-tenant SaaS that any furnishing business can sign up for.

The challenge

What was broken.

In soft furnishings, one quote depends on fabric width, fullness, pattern repeat, heading and hem allowances, supplier size charts and square-foot slabs. Our first customer ran it all in a spreadsheet, and when we audited it we found totals that only calculated on the first row, a fixing charge left out of every blind quote, and a broken reference in the summary. Estimators measured on paper, priced back at the office, and quoted days later.

  • Quotes took days, so customers compared prices and cooled off.
  • Spreadsheet errors quietly under-charged jobs.
  • Supplier price lists arrived as PDFs and phone photos and were typed in by hand.
How it came together

From idea to production.

  1. May 2026

    First tools, first tenant

    Measurement form and quoting sheet replaced the spreadsheet, then became a multi-tenant web app with clients, projects and branded printable quotes.

  2. Jul 2026

    Pricing engine designed

    A three-layer pricing model (catalogue, recipes, quote rules) prototyped and tested against real quotes to the rupee.

  3. Jul 2026

    Full platform build

    Orders, purchase orders, work orders, delivery, reports, team roles and billing built in six weeks. A full acceptance run passed with hand-checked maths.

  4. Aug 2026

    AI price-list import

    Supplier PDFs and scans read straight into the catalogue.

The solution

What we built.

The owner describes their pricing once in the pricing studio: fabrics with their width, rate and repeat, product styles with fullness and making charges, supplier size charts, and quote-level rules for discounts and bundles. In the field, the estimator fills one form per window (room, furnishing, measurements, options) and sees the live price of every line. The customer accepts on a link before the estimator leaves, and the order, purchase orders and work orders follow automatically.

How it works
  1. Step 1Estimator measures each window
  2. Step 2Pricing recipe works out the fabric
  3. Step 3Live price per line with tax
  4. Step 4Customer accepts on a link
  5. Step 5Order, PO and work order created
Key features

What we delivered.

Pricing studio

Owners build pricing from blocks and formulas, referencing measurements, fabric rate, width and repeat, with size-chart lookups and if/then rules. Every formula shows a live preview as you type.

One-form site visit

Room, furnishing, measurement, options and live price, all on one screen. No wizard to click through while the customer watches.

Window measurement diagram

A drawn window with tap-to-edit dimensions for header, footer, sides, depth, height and width, matching the paper template estimators already use.

AI price-list import

Upload a supplier's PDF or a photo of a printed list. AI reads the table, maps the columns, and the catalogue fills in, without duplicates on re-import.

Customer quote portal

Customers review and accept quotes online, with order terms and a payment schedule.

The whole order chain

Quote to order to supplier purchase order to workshop work order to delivery order.

Reports

Profit and loss, pipeline and order reports for each business.

Built for more than one country

INR with GST, AED with VAT, and billing by region.

Inside the product

A closer look.

Where the AI does the work

Intelligent, and trustworthy.

Read, then map

AI first copies the supplier's table exactly as printed, then a second small step works out which column is which from a few sample rows. Everything after that is ordinary, predictable code.

Maths is never left to AI

Prices, fabric consumption and taxes are calculated by a tested formula engine, never by a language model.

Any model, same result

The import works with a range of AI models and can switch between them without code changes. It was tested across models with a 100× difference in price.

Errors in plain language

If a formula can't be calculated yet, the studio says what is missing instead of showing an error code.

Built with

The stack.

Platform
Next.js 16 on Cloudflare Workers
Data
Neon Postgres with per-organisation isolation
AI
Gemini via OpenRouter for document reading
Business
Dodo Payments billing, PDF quotes, Resend email, Google sign-in
Results

The numbers.

Build proof

98.6%
of 783 price-list rows transcribed defect-free (internal check)
Verified
3,300+
automated test cases
Verified
49
screens across the platform
Verified
593
commits in the main build
Verified

Expected impact for users

1 visit
from measurement to signed quote
Projected
3 days → 20 min
quote turnaround
Projected
0
formula errors reaching customers
Projected
+30%
close rate on the first visit
Projected

Verified from project repositories, test runs and release records. Projected modelled estimates of user impact, not measured client results.

Your product next

Got an idea? Let's ship it.

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