We help technical organisations design, prototype, and deploy agentic AI workflows for simulation-heavy, data-rich, and uncertainty-driven engineering environments.
End-to-end advisory and build services for engineering teams adopting agentic AI in simulation-heavy, uncertainty-driven environments
We design and build physics-aware multi-agent workflows tailored to your engineering environment — from simulation interpretation to uncertainty-aware decision support and traceable reporting.
Rapid prototyping of agents that interpret simulation outputs, compare scenarios, evaluate physical constraints, and generate engineering-ready recommendations grounded in domain logic.
Build specialist agents tuned to your domain — subsurface engineering, power systems, critical minerals, or any simulation-driven technical field. Includes SLM fine-tuning for domain precision.
Transform raw simulation outputs, engineering data, and technical documentation into structured, AI-ready datasets — with ingestion, cleaning, labelling, and governance built in.
A four-step approach to delivering engineering AI workflows that are explainable, auditable, and fit for production
We map your engineering environment — simulations, tools, data, decision points, and uncertainty sources — to identify where agentic AI adds the most value.
We architect a multi-agent reasoning workflow: specialist agents, tool connections, human-in-the-loop gates, and traceability requirements — before writing a line of code.
We build, test, and validate the agent workflow against real engineering data. Every agent step is inspectable, every output is traceable.
We deploy on your infrastructure — cloud, on-premise, or hybrid — and continue refining agents as your engineering requirements evolve.
Our consultancy draws on hands-on experience in engineering domains, simulation workflows, and production AI systems.
Engineering domain knowledge. We understand subsurface, energy, and power systems — not just AI. Our workflows are designed around engineering constraints, not generic data pipelines.
Production agentic AI experience. We have built multi-agent systems for real engineering environments — including simulation interpretation, uncertainty analysis, and human-in-the-loop validation.
Transparency by design. Every workflow we build is inspectable, auditable, and traceable. We prioritise explainability because engineering teams — and regulators — need to understand why an agent made a recommendation.