ARCHITECTURE & ENGINEERING
Engineers deserve engineering.
A&E firms run on utilization math and win rates, and increasingly on how well they use computation in the design work itself. We build both layers: the operational software that runs the firm, and the machine learning tooling that changes what the firm can produce. We were ML engineers before the boom renamed the field; this is home turf.
The way it works now
Proposals are rebuilt from the last proposal. Utilization reports arrive two weeks after the month they describe. Project financials live in a system the PMs avoid. And on the design side, your best people are doing iteration work by hand (option studies, load cases, layout variants) that a well-built computational pipeline could explore a hundred times faster.
What custom looks like
Firm operations: live utilization and project-profitability dashboards, proposal generation from a structured past-performance library, and resourcing that sees three months ahead.
ML-assisted engineering: models trained on your firm's own project history to inform early-stage estimates, feasibility screens, and design decisions.
Agentic design pipelines around your CAD and analysis stack: parameterized option generation, automated iteration against constraints, and result triage, so your engineers review a ranked set of candidates instead of producing them one at a time. Prototyping faster than any human could alone, with a human deciding what ships.
Where agents fit
Proposal assembly: past performance, resumes, and scope language drafted from your library for a human to edit.
Spec and submittal review support: flagging deviations against the spec sections that matter.
RFP monitoring across the public sources your sector bids, triaged by fit before a person spends an hour reading.
Sound like your shop?
Tell us what your week actually looks like. First conversation is free, and it is with an engineer.