AI cost estimation
Estimates grounded in projects that actually resemble the one you're pricing.
The engine's core move is unglamorous but decisive: for every new brief, it identifies the past projects most genuinely similar - by scope, size, materials, finish, and site conditions - and uses their realised outcomes to shape the new estimate. Not template arithmetic. Not benchmark averages.
what this does
Similarity is a technical claim. The engine measures it explicitly and shows you the calculation. When two past projects come up as references, you can see why the engine picked them and how much weight each one carried in the final number.
Structured similarity, not fuzzy matching
Scope, area, finish tier, materials mix, site type - explicit fields with explicit weights. Nothing implicit.
A shortlist of references, not a single "answer"
You see the past projects the estimate is grounded in, ranked. If none of them look right, the engine tells you the confidence is low.
Grounded, not extrapolated
If there are no genuinely similar past projects, the engine says so - it doesn't manufacture a number out of a template.
a worked example
One brief. Two references. A grounded number.
A 3,000 sq ft office fit-out is priced against two past fit-outs of comparable size and finish. The estimate reflects their realised final costs, adjusted for the deltas the engine can see.
estimate-builder / live
idle
01 New project
new project
Office fit-out, 3,000 sq ft, mid-range finish - HVAC and partitions in scope.
02 Similar past projects
Office fit-out
2,800 sq ft - mid-range finish
₹ 42,10,000 final cost
Office fit-out
3,200 sq ft - mid-range finish
₹ 48,60,000 final cost
03 Cost breakdown
- Materials₹0
- Labour₹0
- Overhead₹0
- Margin18%₹0
Total₹48,25,200
Quotation ready
how it fits
Part of the loop, not a bolt-on.
- The reference pool comes from your connected project history - not a shared benchmark database.
- Every line item in the produced estimate is traceable to the reference lines that informed it.
- When the engine can't find similar-enough references, the confidence is surfaced and the estimator is prompted to review manually.
- The estimate output feeds directly into the quotation generation step - no re-keying.