We connect simulation, emulation and digital twin technology to your R&D and manufacturing pipelines, working as an extension of your engineering team, so the first workflow runs on live data rather than last week's export.
Jason, our COO, runs the call and maps your first Sim AI workflow with you before anything is scoped.
Sim AISimulation and digital twins
Semiconductors
Automated EDA flowsAgentic workflows and retrieval in EM simulation and mixed-signal emulation, aimed at power, performance and area.
Manufacturing
Live digital twinsA virtual copy of the line that responds to factory floor data as it arrives.
Supply chain
Network simulationWorld, parts and policy models that observe, orient, decide and act across the network.
Autonomy
Test, evaluation, validation, verificationTEVV for self-driving and uncrewed delivery systems.
Main risk
Adversarial machine learningEvasion, data poisoning, model poisoning and privacy attacks.
Risk categories from NIST AI 100-2 E2025, Adversarial Machine Learning.
Clients include
Why it fails without a plan
Sim AI is easy to demo and hard to trust
Sim AI brings machine learning into simulation, emulation and digital twins, so you can model and optimise physical, electronic and supply chain systems without the cost and delay of physical prototypes. The platforms are capable. Connecting them to your systems is where projects stall.
Integration debt
Each new connection to a solver, a PLM system or a data lake adds a dependency nobody owns.
Stale data
A twin fed from exports describes last week's factory, and teams stop believing it.
Attack surface
Simulation models can be attacked during training or deployment, and some of those attacks are hard to detect after the fact.
No one runs it unsupervised
Without a structured rollout, the result is an impressive tool nobody trusts enough to run on its own.
What we deploy and optimise
Four kinds of work, each scoped against the systems you already run.
Twins
Digital twin orchestration and real-time emulation
Virtual copies of your assets, processes or systems, wired to live enterprise data so the twin mirrors the factory floor or the supply network as it is now.
Design
Agentic workflows and retrieval in design automation
For semiconductor and electronics teams, agents and retrieval built into EM simulation and mixed-signal emulation flows.
Safety
Test, evaluation, validation and verification
TEVV frameworks for autonomous systems, so safety, cost and regulatory questions are answered before deployment.
Supply chain
Global supply chain simulation
Real-time simulation engines with world, parts and policy models that act across your supplier network.
A twin that reads the line as it runs
The loop we build: live data in, a simulation run, a recommendation an engineer approves, and the result fed back into the twin.
Digital twin loop
ObserveFactory floor dataSensors, MES and ERP events, streamed as they happen
OrientDigital twinUpdated from validated inputs only, with poisoned or out-of-range data rejected
DecideSimulation runScenarios scored against throughput, cost and risk
ActEngineer approvesThe recommended change goes to a named person first
The outcome is written back to the twin for the next run
Illustrative loop. Inputs, scenarios and approvers are set per workflow during scoping.
Securing your Sim AI environment
Security measures we build in from the first integration, not after the first incident.
Adversarial ML mitigationDefences against evasion, data poisoning, model poisoning and privacy attacks across the ML lifecycle.
Provenance and traceabilityHashing and digital twins that trace parts and data across semiconductor and manufacturing value chains.
Quantization safeguardsEdge deployments checked so quantization does not amplify errors or weaken resilience.
Data residencyResidency and compliance requirements mapped and enforced from the first data connection.
In production today
Agents on top of the systems a team already runs
Our clearest proof of the pattern is outside simulation: a dental care group's scheduling, built on n8n over the Odoo ERP after a year of failed native builds. The same approach applies to Sim AI. The twin and the solvers stay where they are, and we build the connections, checks and approvals around them.
A simulated agent that mimics human decisions and behaviour inside a digital environment. Enterprises use them to populate test environments, simulate customer behaviour or stress-test operational workflows before real-world deployment, under controlled, repeatable conditions.
How do you protect Sim AI against adversarial attacks?
With data validation, model sanitisation and continuous monitoring across the ML lifecycle, which cover the poisoning, evasion and model attacks NIST AI 100-2 E2025 describes.
How is an AI simulation different from a physics-based simulator?
A physics-based simulator solves deterministic equations. An AI simulation learns and predicts system behaviour, runs faster and supports agentic workflows, but it carries an ML attack surface a deterministic solver does not.
Where should we start?
With one simulation workflow that already runs by hand. If you have several candidates, the AI Audit ranks them over 4 to 8 weeks and ends in a 90-day roadmap.
Other platforms we build on
We pick the platform after we understand the workflow, so the same team can build on whichever of these fits it. Compare them all
Workflow automation
n8n
Trigger-and-steps workflows with AI inside them, billed per run and self-hostable in your own cloud.
See n8n
Enterprise AI platforms
Microsoft Foundry
Agents built in your Azure tenant under Entra ID, billed on the Azure account you already have.
See Microsoft Foundry
Workflow automation
Kestra
Open-source orchestration for data and AI pipelines, defined as code and run on your infrastructure.
See Kestra
Workplace AI
Claude Cowork
Anthropic's agent for knowledge work, set up with the plugins, connectors and approval steps your teams need.
See Claude Cowork
Workplace AI
ChatGPT for work
ChatGPT rolled out to your company with the connectors, custom GPTs and admin settings it needs.
Page coming
SAP
SAP Joule Studio
Custom Joule skills and agents on SAP BTP, connected to the SAP and non-SAP systems your teams query.
See SAP Joule Studio
Ready to move from pilot to production?
Your engineers should spend their time on the product, not on a simulation environment that was never connected to live data. Tell us what you run, and we map the first workflow with you.
Owen, Maciej, Jason, Oskar and Ben
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