FlatClaw, Private AI Platform
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Estimating & QuotingData ConsolidationAnalytics & ReportingConstruction

Flooring and concrete contractor

≈ $25M revenue (approx.) · 12 years of job history · Zoho CRM · Azure tenant

Estimation benchmarks from twelve years of costing sheets

Twelve years of costing sheets and work orders become a governed knowledge base; every new estimate is benchmarked by technology and size band against what past jobs actually cost, QA'd before approval, and driven from inside the CRM the sales team already lives in.

Organization
Specialty flooring and concrete contractor
History
About twelve years of costing sheets, work orders and job outcomes in spreadsheets
Runs on
The contractor's own Azure tenant, driven from Zoho CRM
Scope
Historical benchmarks, estimate calculation, QA/QC before approval, learning loop
The situation

Where they started.

The company had years of project history and no way to use it. Costing sheets, work orders and estimating files lived in individual spreadsheets, so nobody could quickly say what an epoxy job really costs per square foot, which technologies are most profitable, how estimates compared with actuals, or whether a new number was too low, too high or missing a cost line. The brief was explicit: not another calculator, an estimation support system that combines the company's costing logic with its own performance history.

What FlatClaw does

What was built.

  • Historical costing sheets and work orders are normalized into a governed knowledge base: technology, square footage, price per square foot, labor hours, material, transport, equipment, consumables, per diem, estimated versus actual value, profitability, crew size, duration, change orders.
  • Benchmarks by technology and project-size band: epoxy coatings, urethane cement, polished concrete, self-leveling systems, sealers, with the realistic ranges for every cost category.
  • A new estimate is calculated on the company's own cost logic, then compared with the closest historical jobs and their outcomes, and the gaps are flagged before an estimation manager approves it.
  • The whole loop runs from inside the CRM the sales team already uses, and completed jobs feed their actuals back into the benchmarks.
Results

What changed.

  • Estimates grounded in what the company's own jobs actually cost, by technology.
  • Too-low, too-high and missing-cost estimates caught before they leave the building.
  • Profitability visible by technology and market segment for the first time.
  • Spreadsheet dependence replaced with a knowledge base that improves with every job.
Why private

A contractor's cost structure and margins by technology are the business. Benchmarking against them on private inference keeps that history inside the company's own tenant.

Runs on

Microsoft Azure

The stack

Components involved.

Spreadsheet and work-order ingestionBenchmark and estimate agentsCRM MCP connectorFlatClaw PortalPrivate inference on a dedicated GPU
Your workflow

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