How PreBLD learns from your estimates.

PreBLD's AI gets sharper every time anyone finalizes an estimate. This page explains exactly what data feeds the learning pool, what stays private to your firm, and why we built it this way.

The short version

  • When you click Finalize, an anonymized copy of your estimate joins a shared pool that every firm in PreBLD can learn from.
  • The shared pool contains scope and pricing only — never your project name, client name, or address.
  • Your full project record (with names + clients) stays inside your firm's tenant. No other firm can see it. Ever.
  • In return, every estimate you make is anchored against real bid data from across the whole user base — not just static benchmarks.

What's shared

On Finalize, we extract just enough information to make the AI's pricing smarter for similar future projects. Specifically:

  • • Project type (Commercial / Residential)
  • • Scope type (New Build, Addition, Interior Reno, Exterior Reno)
  • • Building use (Warehouse, Office, Multi-Family, etc.)
  • • Square footage
  • • City + province / state — never street address
  • • Year of estimate (so the AI knows whether it's recent or stale)
  • • Structural system, wall assembly, roof, exterior finish, foundation, HVAC
  • • Division-level pricing breakdown ($ per CSI division)
  • • Construction subtotal, CM fee, contingency, project total, $/SF

That's it. The shape of the building and what it cost.

What's NOT shared

The shared pool's database schema literally cannot hold these fields — the columns don't exist. This isn't a policy promise we hope our code follows; it's enforced by the database itself.

  • ✗ Project name
  • ✗ Client name
  • ✗ Street address
  • ✗ Tenant ID (no link back to your firm)
  • ✗ User ID (no link back to who created the estimate)
  • ✗ Free-text notes
  • ✗ Line-item descriptions that mention identifying details

When another firm's estimate references one of your finalized projects, they see a label like “Commercial Addition · 18,300 SF · Winnipeg, MB”. They never see who built it, for whom, or where on the street it sits.

Why we built it this way

Construction estimating is a category where the data network effect compounds: more real bids means tighter $/SF benchmarks, better division-level cost intelligence, and faster onboarding for new firms (who otherwise start cold).

But a bid total is also competitively sensitive — it's the firm's pricing strategy on a specific job. So we split the data:

  • Your firm's private record keeps everything — project name, client, address, notes. It powers your dashboard, your finalize history, and the CRM-style features built on top of it. Only your tenant can see it.
  • The shared learning pool keeps only scope + pricing. It feeds the AI's anchor data so estimates across the entire user base get smarter over time. No identifying data, by schema design.

This gives every firm a real network-effect advantage without exposing anyone's specific bid strategy on a specific job to a specific competitor.

If you re-edit an estimate

Re-opening a finalized estimate clears the lock so you can edit. When you re-finalize, the shared pool is updated with the new numbers — the latest version is what's anchored against. Re-edits don't create new anchor entries; they overwrite the existing one for that project.

If you delete a project

Deleting a project from your dashboard removes it from your firm's tenant. The anonymized anchor entry stays in the shared pool — it's already anonymous and can't be traced back to you, your firm, or your client. This is standard for anonymized contributions: once data has been stripped of identifying information and entered the pool, it remains there as part of the collective baseline.

Questions?

If anything about this isn't clear, or you have specific concerns about how a project's data flows through PreBLD, email hello@preblt.com. We'll answer directly.

Last updated: June 16, 2026. This page describes the cross-firm learning pool architecture as of today and may be updated as PreBLD's data model evolves. Material changes will be communicated to existing users.