Products for simulation and engineering teams. Infrastructure for enterprises that need AI on their own terms. Custom agents for everything in between.
Products and Services
AI platforms purpose-built for engineering and simulation teams. Deploy agents that prep CAD, mesh, and set up solvers — and surrogate models that turn weeks of simulation into minutes.
Explore ProductsOwn your AI stack
Fine-tuned models, air-gapped LLM deployments, and document intelligence — for enterprises where data privacy, cost, or control rules out off-the-shelf AI.
Explore InfrastructureProduction-grade agents, built to spec
Support, sales, research, and voice agents that connect to your databases, APIs, and knowledge bases — deployed and monitored in production.
Explore Custom AgentsWe're not a simulation consultancy adding a sprinkle of AI, and we're not a software reseller with an implementation arm.
We're an AI engineering company that speaks simulation, CAD, PLM, and manufacturing natively. We train and deploy ML models, build data pipelines and scalable AI systems in production, and we understand boundary conditions, mesh quality, and why your BOM never matches your as-built. We don't resell software, so our only incentive is what works on your stack.
Seven years inside ANSYS shaped how we build. We know where CAE workflows break because we lived them before we automated them.
Runs on your infrastructure, so your geometry, BOMs, and field data stay in your environment. IP ownership of what the pipeline produces is yours, written into the engagement.
In our own propeller study, a verification agent caught a sign-convention error before it reached the report, and the winning design was cross-checked against a full CFD run.
Read what the agent caughtWe sit over your existing stack instead of asking you to rip it out and start again. AgentCrew's connectors span CAD, meshing, and solver tools across both commercial and open-source suites.
From the Lab
Method-first technical writeups of studies run on our CAE/CFD and surrogate-model pipelines: what we ran, how it was verified, and where each method reaches its limits.
A genetic algorithm drives a fast BEMT evaluator to a Pareto front. A blade-resolved RANS run then verifies the winner, with review agents running the whole loop.
A post-processing sign error slipped into an automated study. A verification agent flagged it as physically implausible before it reached the report.
A surrogate trained on high-fidelity thermal runs predicts component temperatures in milliseconds. The hard part is knowing exactly where to trust it.
How we engage
It starts with a free consultation, then a fixed-price sprint, so you can prove the approach before committing to a build. No open-ended retainers to start, no rip-and-replace.
Free · 1 day
Where every engagement starts. We spend a focused day on your workflows and your bottlenecks, then tell you what we would do about them, whether or not you work with us.
Deliverable: concrete, prioritized recommendations, yours to keep either way.
Fixed price · 2–4 weeks
We scope your problem against your real data and systems, prove feasibility, and hand back a plan you can act on, with us or without us.
Deliverable: a technical feasibility assessment, a working proof-of-concept on your data, and a costed build plan.
Typically 8–16 weeks
We design and build the production system on your infrastructure, validated against your solvers and data, with the source code, documentation, and knowledge transfer so your team can own it.
Deliverable: a production-grade system, full source and docs, and your team trained to run it.
Ongoing, optional
Once it's live, we keep it healthy: monitoring, accuracy checks against the underlying solvers, model updates, and new use cases as your team finds them.
Deliverable: monitoring, drift and accuracy checks, and a standing line to the engineers who built it.
It starts with a free consultation and ends with a system your team owns. Paid engagements are scoped and costed upfront, so you know the number before you commit.
