Development
AI products for auditable AI-assisted software work and accelerated physical or systems engineering.
AI with control, evidence and impact
Use AI without surrendering your data or your engineering rigour.
CognitiveSand builds secure AI products, trains teams and helps organisations turn AI ambition into measurable outcomes.
Why CognitiveSand
- ✓ Expertise forged at Airbus on demanding technology projects
- ✓ Based in Toulouse, data handled under French and EU constraints
- ✓ Every engagement starts with a measured pilot before any rollout
Three business lines. One method.
Development, consulting and training share the same practical discipline: test early, keep control, prove value, then scale.
Consulting
AI consulting for engineering: surrogates, optimisation, MBSE and traceable engineering software.
Training
Role-specific AI literacy, practical workshops, structuring and securing AI use for employees, developers, engineers and project teams.
Designed for teams that need trust, not just demos
Sensitive data under control
Workflows compatible with French or EU jurisdiction constraints, deployed in the cloud or on-premises depending on your IP and compliance constraints.
Traceability by design
AI-assisted work remains auditable from business need to decision, specification, implementation and review.
Measured business value
Each engagement is framed around useful pilots, clear KPIs and decisions grounded in evidence.
Start with a pilot, not a rollout.
A pragmatic path for adopting AI without losing control of data, quality or governance.
Secure AI adoption for practical organisations
CognitiveSand is based in Toulouse and works in French and English. The company helps organisations adopt AI through concrete products, clear training and hands-on consulting.
The offer is especially suited to SMEs and teams in larger organisations that want to validate the value of AI on focused, low-risk use cases before deploying more broadly.
The operating principle
Test early, test small, test smart, physics always wins at the end.
This principle keeps the work grounded: first understand the workflow, then build a controlled experiment, measure the impact, and only then decide whether to scale.