Architecture, Engineering & Construction
Your stamp carries your name.
Your AI should answer to the same standard.
Engineering firms live and die on methodology, professional judgment, and the reproducibility of their deliverables. Public-cloud AI vendors provide none of those guarantees — the model behind the API can change under you, and the data you send may not stay yours.
We build private AI inside engineering firms, so methodology stays proprietary, client data stays in-house, and model behavior stays stable across reporting cycles.
The Case
Why in-house AI matters for engineering firms.
Stamped work carries liability
Calculations and stamped drawings expose the PE and the firm to professional liability. The chain of responsibility for AI-assisted work should stay inside the firm, with tools the firm controls and can audit.
Methodology is competitive
Proprietary analysis methods, spreadsheets, and checklists are the firm's edge. Feeding them into public AI tools — even for "productivity" — risks losing that edge to a vendor's training pipeline.
Client NDA obligations
Many engineering engagements include NDAs that restrict where client data can be processed. A generic "the tool is HIPAA-compliant" assurance doesn't address those contractual obligations.
Consistent model behavior
Engineering reports need to be reproducible. A model whose behavior shifts between quarters introduces drift into deliverables. Private models stay stable until you choose to change them.
Capabilities
What we build for engineering firms.
Report drafting assistance
Draft and review engineering reports from structured input — grounded in your firm's templates and past deliverables, processed locally.
Code & standards lookup
Private RAG over building codes, industry standards, and your firm's interpretations. Engineers query in plain language and get sourced answers.
Spec & submittal review
Summarize submittals, flag non-compliance against spec, and surface prior engineering judgment from your firm's archive.
Every firm is different.
Discipline, project mix, and existing tools all shape what makes sense. We start with a conversation, not a proposal.