CASE 03 · MANUFACTURER
Quotes went from days
to minutes.
A windows-and-doors manufacturer relied on a few experienced people to interpret specifications, calculate quantities and apply pricing rules. Cyberate built the automated quoting engine that turned that knowledge into a repeatable system.
A de-identified quote take-off. Line locations and series names follow the live format. Client and brand are not shown.
- User type: Building-product manufacturer
- System: Automated Quoting
- Result, one live quoting desk, same catalogue rules: Days → minutes
- Status: Live deployment
See the system behind this case · Talk to us about your quoting
Quoting capacity was locked inside a few heads.
For many building-product manufacturers, quoting is not just administration. It is the front door to revenue. But when quoting depends on manual measurement, product-rule memory and spreadsheet calculations, capacity becomes limited by the availability of a few senior people.
- Quotes took days: Drawings and specifications had to be interpreted, measured and priced manually.
- Rules lived with individuals: Product knowledge and price logic were carried by experienced staff, not by the system.
- Errors carried commercial risk: Manual calculations created inconsistency and rework.
- Peak enquiries became lost opportunities: When demand increased, quoting capacity did not scale with it.
A quoting engine that runs the manufacturer's own rules.
The engine does not invent a price. It takes dimensions and specifications, runs them through this catalogue and the manufacturer's pricing logic, and produces a standardised draft for a person to review.
The product catalogue the engine reads: series, glazing, flyscreen and profile thickness held as editable rules. The manufacturer brand is not shown.
- Specification input: Dimensions and product requirements are entered or parsed from drawing schedules.
- Product rule engine: The manufacturer's own product logic is encoded into repeatable rules.
- Material usage calculation: Material quantities are calculated consistently against the rules.
- Price logic: Pricing is applied in a standardised way, reducing manual variation.
- Quote generation: A quote is produced for review and release by the team.
From drawing schedule to quote.
Every enquiry follows the same four steps. The estimator enters at review, not at the first measurement.
- Capture the inputs: The system receives dimensions, specifications and product requirements.
- Run the rules: Product configurations, material usage and price logic are applied automatically.
- Generate the quote: The output is shaped into a standardised quote.
- Human review: The team reviews, adjusts if required, and releases the final quote.
Quoting no longer depended on individual bottlenecks.
The manufacturer could answer enquiries in the same visit they arrived, take peaks without adding estimators, and keep senior people on exceptions instead of the same take-off.
| Case anatomy | |
|---|---|
| Business type | Building-product manufacturer |
| Before | Days per quote, rules carried by a few senior estimators |
| System built | Automated Quoting |
| Data handled | Drawings, specifications, product rules, material quantities, pricing logic |
| Workflow changed | Estimators review and release instead of measuring and recalculating |
| Result | Days → minutes |
| Status | Live deployment |
Evidence note
- What is measured
- Elapsed time to produce a priced draft quote from the supplied specification, up to the point an estimator reviews it.
- Before
- Days. Specifications were interpreted, quantities measured and prices applied by hand.
- After
- Minutes. The engine applies the manufacturer's own product configuration and pricing rules to produce the draft.
- Deployment
- Live, at an Australian windows-and-doors manufacturer.
- No sample size, measurement method or typical range has been published. This is a comparison between the manual process and the running system, not a controlled test.
- Turnaround will vary with the drawing set, the product range and the complexity of the pricing rules.
- The engine produces a draft. The estimator reviews and releases the quote, so time to a released quote depends on that review.
- Greater quoting capacity: More enquiries can be handled without simply adding more manual labour.
- More consistent outputs: Quotes follow the same product and pricing logic.
- Senior expertise preserved: Experienced people review exceptions and improvements instead of repeating the same calculations.
Common questions.
How can a quote go from days to minutes?
The manual work is reading drawings and looking up rules. The engine takes dimensions and items directly or parses them from drawing schedules, then applies the manufacturer's own product configuration and pricing logic. The estimator reviews and releases rather than recalculating.
Does the estimator still control the price?
Yes. The output is a priced draft with confidence flags per item and a review queue, not a released quote. The human stays on the decision and on the release.
Will it work with our product rules, not generic ones?
The rules are your own, encoded once in your deployment and never pooled with anyone else's. The honest test is to send a representative drawing set with your rules and a finished quote, and compare the output against it.
The system behind this case.
- Automated Quoting: The quoting engine, available to your product rules.
- Quoting Agent: The AI agent built on this engine.
- Document Intelligence: Drawings and specifications parsed into structured data.
- For Manufacturers: How we serve building-product manufacturers.
- Construction Quoting Automation: The quoting workflow this case is drawn from.