Decision platform. Evidence engines.
The platform reasons: it structures alternatives, compares trade-offs, documents traceability and governs what can be claimed. Evidence engines feed that reasoning with domain data: physics, aerodynamics, lifecycle, materials. The value lies in the connection: decisions bounded by what is actually known.
Design Engines
The DDS decision platform for complex industrial problems. Design Engines is designed to structure the problem and its early design decisions in a single reasoning space that connects alternatives, conflicting criteria, constraints and consequences. Engineering tools produce evidence; Design Engines is designed to establish what evidence each decision needs, what that evidence supports, what remains uncertain and what a person can responsibly decide.
Aeroflow
DDS Aeroflow provides AI-assisted aerodynamic evaluation for the earliest design stages, before CAD. It helps teams evaluate alternatives earlier, identify risk zones and decide which directions deserve deeper validation. Not a replacement for high-fidelity CFD — a decision layer before it.
From requirement to responsible decision.
Engineering tools produce evidence and solve well-defined problems faster. Design Engines helps when the problem itself is not yet well defined: it is designed to establish what evidence is needed, what it supports, what remains uncertain and what a person can responsibly decide. Every decision follows the same path:
What is AI decision infrastructure? →- 01
Requirement
What the product must achieve, and under which constraints.
- 02
Claim
What an alternative is expected to deliver, stated explicitly so it can be tested.
- 03
Evidence
Simulation, data, analysis and expert review that support or challenge the claim.
- 04
Boundary
What the evidence does not cover: assumptions, uncertainty and what still requires validation.
- 05
Human decision
A person decides, with the reasoning, the evidence and its limits on record.
Application contexts.
DDS capabilities are especially relevant in sectors where physical complexity, data scarcity or the cost of error make evidence-based early decision-making determinant.
Automotive and transport
Aerodynamic optimisation, surface engineering and alternative validation in conceptual phases where traditional iteration cycles are too slow and expensive.
Aeronautics and wind engineering
Aeroelastic behaviour prediction, load analysis and early structural decision-making in environments where high-fidelity simulation arrives too late in the process.
High-performance architecture
Envelope evaluation, climate behaviour and form decisions in projects where computational evidence must enter before the design is locked.
Medical technology
Product geometry, functional validation and synthetic data generation for devices where real data is scarce, sensitive or regulated.
Applied capabilities.
DDS activates advanced capabilities through strategic projects with selected industrial partners.
Synthetic Dataset Engineering
Generation of training and validation data for industrial AI systems in contexts where real data is scarce, sensitive, fragmented or regulated. Covers image synthesis, semantic segmentation, parametric geometry variation and physical boundary conditions — producing datasets that replicate real-world distribution with full control over labelling, diversity and traceability.
Neural 3D Reconstruction & Spatial Intelligence
AI pipelines that infer, reconstruct and structure geometric product intelligence from images, views, scans and multimodal references. They generate dense, navigable spatial representations — including radiance field reconstruction and implicit neural geometry — enabling reasoning about form, structure and physical behaviour without relying on complete CAD models.
Augmented Industrial Design
Advanced industrial design services where the exploration, evaluation and justification of alternatives are supported by proprietary AI infrastructure as a foundation for predictive generation. An augmented industrial design environment directed at projects where intuition, experience and traditional tools are no longer enough.
Predictive Simulation
AI-assisted simulation workflows that anticipate aerodynamic and structural behaviour in early design phases. They reduce dependence on full CFD cycles for decisions that do not require them, connecting design intent with computational physics before the model is fully defined.
Research foundation.
Our work builds on peer-reviewed research in machine learning, computer vision, synthetic data, simulation, aeroelastic engineering and computational fluid dynamics. Selected publications by members of the DDS team and collaborators:
We do not disclose proprietary architectures, datasets, training strategies or implementation details that constitute the company's core technical advantage.
We bring intelligence to the start of the process.
For industrial partners, product teams and institutions seeking new ways to decide, validate and justify the development of complex products.