IN DEVELOPMENTExploring design partnershipsExplore our approach
ENGINEERING R&D INTELLIGENCE · IN DEVELOPMENT

Your intelligent layerfor engineering R&D.

Describe the engineering outcome you need. Renra is being developed to plan, execute and iterate the investigation using your organization’s knowledge and compatible engineering tools—with engineers supervising the decisions that matter.

THE APPROACH

One research objective.
Multiple tools.
A continuous thread of evidence.

Engineering context is difficult to carry across applications, files and people. AI Research Engineer is being developed as a workspace that carries an investigation from intent through design iteration and review, using compatible engineering tools and your organization’s R&D expertise.

Plan the investigation

Text, voice, images and existing research.

Explore planning

Coordinate the tools

Connect compatible tools through one investigation.

Explore execution

Preserve your expertise

Keep methods, evidence and approved reasoning.

Explore R&D memory
THE IMPACT

More time for
engineering decisions.

Renra is being developed to coordinate routine engineering work, bring evidence into design decisions and carry your team’s expertise into the next investigation.

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UP TO · TARGET

Faster engineering studies

Less routine setup. More time to investigate engineering decisions.

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UP TO · TARGET

Lower study cost

Targeting more efficient iterations across compatible engineering tools.

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PLATFORM DIRECTION

Connected R&D workspace

Tools, evidence and organizational expertise in one continuous investigation.

Explore the workflow

Performance targets for scoped engineering studies; not measured or guaranteed results. One connected workspace describes the platform direction.

WHY WE'RE BUILDING THIS

Research should accumulate knowledge.

Our vision is an AI engineering workspace that uses your team’s domain expertise to advance investigations across compatible tools. Each study should preserve its methods, evidence and approved reasoning, building organizational knowledge that future work can draw on.

CONNECTED ENGINEERING KNOWLEDGECONCEPTUAL WORKFLOW
01 / KNOWLEDGE SOURCES

Prior studies

Findings, assumptions and decisions

Domain methods

Established engineering expertise

Design rules

Constraints and acceptance criteria

Experimental data

Measurements, tests and observations

02 / ENGINEERING CONTEXT

Organizational
memory

Expertise, connected
to its evidence.

Methods & reasoningEvidence & provenanceApproved decisions
03 / DESIGN INVESTIGATION

Explore alternative designs

Design, analysis and validation across a wider design space.

DESIGN A
DESIGN B
DESIGN C
  1. Design
  2. Analyse
  3. Validate
Compare, refine and iterate
04 / CARRY IT FORWARD

Next investigation

Start with the knowledge
your team has already built.

Illustrative four-fin heat sink for a subsequent engineering study.
ENGINEER-SUPERVISED

Early product-definition and validation stage. The engineering problem stays central. Tools execute the work. Evidence and knowledge persist.

PLATFORM DIRECTIONExplore the platform

Built around your
engineering stack.

An intelligence layer designed to connect your organization’s R&D knowledge with compatible engineering tools and engineer-led review.

Organizational expertise

Bring prior studies, experimental evidence, design rules and domain methods into the investigation.

Compatible engineering tools

Coordinate supported CAD, meshing, solver and compute tools through traceable workflows.

Engineering supervision

Keep consequential assumptions, exceptions and final approval with your engineers.

Planned integrations will be selected and validated around a bounded engineering study.

INDUSTRY OVERVIEW

For demanding engineering challenges.

Target application areas for supervised engineering investigations.

Aerospace

Thermal and structural investigations for components and assemblies, with traceable assumptions and engineering review.

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Automotive

Thermal management and component design studies, connecting geometry, numerical evidence and design comparisons.

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Semiconductors

Package cooling, heat-sink design and thermal reliability studies informed by material knowledge and experimental context.

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Industrial

Thermal and mechanical component studies, with reusable methods and evidence across design iterations.

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BUILT AROUND REAL ENGINEERING WORKDESIGN-PARTNER SEGMENTS

For teams making physical-product decisions through computation.

Early conversations are focused on real engineering problems, validation requirements and potential pilot workflows.

Hardware & Deeptech Startups

Turn constrained engineering resources into more focused computational studies and clearer prototype decisions.

Computational R&D Consultancies

Explore repeatable project setup, study evidence and approved research context while preserving strict client-data boundaries.

Industrial R&D Teams

A longer-term path to coordinated studies, trusted-solver handoff, permissioned knowledge and review-ready evidence.

Explore a Design Partnership
WORK WITH US

Let's start with an
engineering problem
worth solving.

Are repetitive simulation studies, fragmented workflows or difficult-to-reuse research context slowing down your team? Tell us about a representative problem. We'll explore whether a bounded evaluation makes sense for your workflow.

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Questions & validation

Frequently asked questions

Is Renra a new CAE or simulation solver?

No. The proposed product is an intelligence and coordination layer built to work with supported existing engineering tools and solvers. Numerical computation remains with those tools.

Does it support every solver and every physics domain?

No. Initial support will be restricted to specific validated workflows and compatible interfaces. Broader solver and physics coverage is a future objective, not a launch claim.

Can it make engineering decisions autonomously?

It is being designed to plan, execute and iterate supported investigations within agreed boundaries, deriving routine setup from the objective and available knowledge. Engineers supervise consequential assumptions, exceptions, verification and final decisions.

What happens to our proprietary research data?

Data access, retention, inference location and permissions will be defined for each evaluated workflow. We will not claim a particular privacy certification or deployment mode until implemented and verified.

Is the product available today?

The product is in development. We are exploring design partnerships around concrete, reviewable engineering studies.

What would an initial design partnership involve?

A recent representative engineering problem, agreed inputs and numerical acceptance criteria, a qualified reviewer, and a scoped evaluation of workflow value and cost.

WHAT WE AIM TO IMPROVE

Less time rebuilding the workflow. More attention on the engineering decision.

Our development focus is on outcomes that can be checked, not headline AI speed claims:

We intend to evaluate these measures against existing workflows and capable general-purpose AI alternatives with design partners.