Engineering & Technology Consultancy

From first principles
to first product.

We design and build complete systems — mechanical, electronic, and software — from the ground up. No handoffs. No committees. Just engineering that ships.

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Services

Feasibility Sprint

1–2 weeks

Rapid technical assessment of your idea. Can it be built? What will it take? Clear answers, fast.

Concept Development

4–8 weeks

From validated concept to working prototype. Mechanical, electronic, and software design in parallel.

Product Development

8–24 weeks

Full product development from concept to production-ready. Design, build, test, iterate.

Fractional CTO

Ongoing

Embedded technical leadership. Strategy, architecture, team mentoring. 1–2 days per week.

AI-Driven Prototype Design

Materia Forge

We use AI to explore more of the design space than any traditional approach. You tell us what a part needs to do — our AI generates and physics-validates multiple design candidates in parallel, including solutions your engineers wouldn't have tried.

  • • Requirements-first design exploration — not constrained by starting geometry
  • • Automated physics verification (FEA) before you see a single concept
  • • Three times as many validated concepts for the same budget and timeline
  • • AI-generated concepts refined into production-ready parametric CAD models by our engineers
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Capabilities

Robotics & MechatronicsEmbedded SystemsPCB Design & ECADControl SystemsMachine LearningComputer VisionMathematical ModellingRapid PrototypingAI-Driven Design

About

Materia Lab is founded by Dr Nicole Martin — a physicist and engineer with 12+ years across Formula One, aerospace, robotics, academia, and product development.

Nicole designs and builds complete embedded robotic systems from the ground up: mechanical engineering, PCB and electronics design, firmware, control systems, and application software. Most engineering teams need five people to cover what she does alone — which means faster iteration, less waste, and someone who understands how every decision ripples through the entire system.

Previous work includes machine learning for Formula One aerodynamics (Williams and Toyota), continuum robots for Rolls-Royce jet engine inspection (Tharsus), and force-feedback robotic guidance systems (SquareFace).

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