Project Cornerstone Be a design partner

AI production intelligence for architecture, engineering, and construction

Claude Code–level production intelligence for architecture, engineering, and construction.

Run prebuilt work packs that draft common deliverables and route them to the right reviewer.

Less rework Faster reviews Fewer field surprises

What we're building

Project Cornerstone is building a private, permissioned AI production layer for architecture, engineering, and construction.

Built on the tools the industry already uses: Revit, ACC, and PDFs stay the source of truth.

One permissioned context built from your past projects, scoped to each party, from developer to trades.

Run prebuilt work packs that generate first-pass deliverables, routed to the right reviewer, grounded in your project sources.

The whole value chain

Each party works from the same permissioned context.

  1. Developers. Fewer change orders and schedule surprises.

  2. Architects. More projects with the same team. Better QA.

  3. Engineers. Assumptions verified early. Cleaner first passes for review.

  4. GCs. Design intent on demand. Faster RFI cycles.

  5. Subs and trades. Scoped field answers and takeoffs, tied to the right sources.

Designed so the value grows as more of the chain joins.

Platform

On top of your tools

It sits on the tools the industry already uses. Revit, ACC, and PDFs stay the source of truth.

Generic AI tools don't have your project context. Project Cornerstone answers from it, with citations back to source.

  • Retrieval: start from what the firm already built
  • Quality: catch issues before a set goes out
  • Constraints: surface code and standards while change is cheap
  • Checks: confirm consultant assumptions before they harden
  • Handoff: downstream teams see only what they need

Self-serve by design: start with a few work packs and expand from there.

Work packsrepeatable workflows your team runs on demand
Harnesscontrolled execution · permissions · approvals · audit
Context layerfirm and project knowledge, permissioned at the source
Your existing toolsRevit · ACC · PDFs stay the source of truth
Fig. 02 · The Cornerstone stack. One shared context, above your existing tools.

The goal is not to replace architects, engineers, contractors, or tradespeople. The goal is to give them a trusted AI layer that preserves context, accelerates repeated work, and supports human-reviewed decisions across complex projects.

Demand swings. Payroll doesn't have to.

In the AEC industry, work arrives in waves, and teams can't staff up and down fast enough in response.

Every new project rebuilds context someone in the chain already has.

With elastic inference, teams handle peak workload without permanently staffing for the peak.

Some labor spend shifts to usage-based AI spend, closer to a variable cost.

Demand swings against payroll held at a lower step Illustrative chart. A dashed line of project demand rises and falls in waves. A solid stepped line shows payroll held at a lower level, and a dotted reference line shows payroll sized to the peak. A red bracket marks a demand peak above the payroll step, labeled as running on agents and inference. MONTHLY COST / CAPACITY TIME PAYROLL SIZED TO THE PEAK (OLD MODEL) PROJECT DEMAND PAYROLL HELD AT THE LOWER STEP PEAKS RUN ON AGENTS + INFERENCE
Fig. 01 · Payroll held at the lower step. Illustrative, not to scale.

"We would not have to staff up and cut back every time the market turns. For a firm our size, that is the game changer."

CFO, US multifamily architecture firm

Workflow packs

Up to 30% of a phase's spend is work that repeats from past projects. Packs turn it into repeatable, reviewable workflows.

  • CD-to-DD pull-forward. Prior detail work drafted into the current set.$52.5K to $125K modeled savings per project
  • Engineering draft outputs. First-pass deliverables routed to engineer review.$75K to $160K modeled savings per project
  • GC and trade estimates. Quantities and first passes for estimator review.$87.5K to $250K modeled savings per won project
  • Drawing QA. Small errors caught before the set goes out.being measured in production trials

Modeled on a benchmark composite project · assumptions available on request

Outputs route draft → reviewer → approved deliverable, with permissions and audit.

  1. Define. Configured around your standards and review gates.

  2. Run. On live project work, as often as needed.

  3. Review. Every output reaches the right reviewer first.

Packs are designed to run self-serve, with expert support when you want it.

Trust

Your knowledge stays yours

Each party's data and outputs stay that party's property. Nothing trains public models without permission.

Access is scoped by source, role, and project. Sharing across the chain is permissioned down to each source.

Answers include citations back to the underlying drawings and documents.

Permission scopesPRJ 2026-041
SourceArchitectEngineerGCTrade
Firm standardsFull within role scopeNot sharedNot sharedNot shared
Model and drawingsFull within role scopeScoped viewScoped viewScoped view
Consultant inputsScoped viewFull within role scopeScoped viewNot shared
Review historyFull within role scopeScoped viewNot sharedNot shared
Construction evidenceScoped viewScoped viewFull within role scopeScoped view
Full within role scope Scoped view Not shared
Fig. 03 · Illustrative permission model. Roles and scopes are invented.

Where the work stands

  1. Now. Completing a paid assessment with a design-partner architecture firm.

  2. Next. A pilot on a live project, architecture side first.

  3. Goal. The full value chain on the project, with developer buy-in.

Work with us

We want to hear from:

  • Firms, developers, GCs, and trades with repeated production work

  • Investors in vertical AI and AEC automation

Also hiring (builders): AI + full-stack engineers, computational designers, BIM experts.

An email gets you a working session on one of your live projects.