01 · Connect
Bring project evidence into one usable context without replacing the source systems.
Learn · Technical notes + field guides
How we make AI useful for AEC firms.
A closer look at the architecture, controls, and implementation methods behind AEC Hub, plus practical guidance for moving AI from an experiment into real firm workflows.
Traceable by designFirm-owned contextHuman-controlled actionMeasured deployment
The firm AI operating model
Connect the work. Govern the intelligence. Learn from approved results.
The firm's systems remain authoritative. AEC Hub assembles source-linked context, gives models bounded access through governed workflows, and turns professional feedback into measurable improvements instead of opaque conversational memory.
Firm governance
IdentityPermissionsPolicies
Firm data
Revit · ACC · PDF · email
Governed AI
Project context · models · workflows
Professional approval
Evidence attached · judgment retained
Better project work
Answers · drafts · checks · actions
Traceability across every stepSource → decision → approval → outcome
Approved feedback improves the next workflow version
Detailed platform view
How the governed learning loop works.
Sources → context → workflow → review → improvement
01Systems of record
Revit + ACCProject mailDrawings + PDFFirm standards
Connected in place. Source identity and permissions remain visible.
02 · Firm scopedProject context
Evidence · relationships · workflow state
03 · Replaceable intelligenceFoundation models
Selected for the task
04 · AEC Hub control planeGoverned skills + workflows
Typed tools · versioned definitions · bounded autonomy
05 · Review gateProfessional oversight
Interpret exceptions, approve the output, authorize material action.
06Reviewable outcomes
Project answer Attention brief Proposal draft Model check
Approved learning ledgerWhat professionals correct and accept improves the next run.
Reviewer corrections→Evaluation cases→New skill + workflow version↻
02 · Govern
Bind foundation models to typed tools, firm permissions, and versioned workflow definitions.
03 · Prove
Keep evidence and professional review beside every answer, artifact, and proposed action.
04 · Improve
Turn approved corrections and outcomes into evaluations and better workflow versions.
How the platform works
Technical depth, connected to project outcomes.
These are implementation notes, not abstract AI commentary. Each guide explains a system choice, what is live today, and why it matters to the people delivering projects.
01 · Platform update
Inside AEC Hub’s governed agentic runtime
A technical breakdown of the new execution foundation that connects sourced project signals, versioned skills, durable workflow state, professional approvals, and an inspectable run history.
12 minute readOutcome · Move closer to agentic work without giving up professional control
02 · Traceability
Verifiable AI architecture for project work
Why useful AEC intelligence needs a visible path from source evidence to computation, interpretation, and professional review.
8 minute readOutcome · Faster answers without losing the basis for a decision
03 · Strategy layer
The AI strategy layer belongs above the model
Models will keep changing. Durable advantage comes from the firm context, rules, tools, controls, and feedback loops that make intelligence useful.
9 minute readOutcome · Adopt better models without rebuilding how the firm works
04 · Agentic workflows
From AI chat to controlled project workflows
How bounded tools, explicit state, guardrails, evaluations, and approval points turn a useful assistant into dependable project infrastructure.
10 minute readOutcome · More work completed with clear limits and accountable review
How to leverage AI in your firm
Start with deployment, not a model demo.
Better outcomes come from choosing a valuable workflow, understanding how work actually happens, and designing the right mix of software, AI judgment, and professional review.
Find the workflow worth changing
Map the real process, its exceptions, the evidence it uses, and the business outcome before choosing a model.
Build an evaluation set
Turn real project examples, failure modes, and professional expectations into evidence that the workflow works.
Deploy with controlled autonomy
Start read-only or in draft mode, preserve human approval, and expand only when observed results support it.
Business value
Time saved, risk reduced, or revenue improved
Technical evidence
Known failure modes and measurable performance
Adoption path
A controlled route from pilot to daily use
Put the ideas to work
Prove one useful workflow on a real project.
Request pilot access
Start with a scoped workflow, explicit evidence, and an outcome your team can verify.