AI for Engineering
AI working
for your projects.
Where most AI only handles isolated tasks, our agents reason, analyze and act across all of your technical data: test reports, specifications, simulations, drawings and knowledge bases.
At VSOLUTION, we built in metrics, systematic human oversight and a clear chain of accountability from the very start. AI does not replace human expertise, it multiplies it.
- 40% of engineering time lost to repetitive tasks, which we automate.
- Autonomous agents that reason, not just isolated prompts.
- Fully on-premise deployment available (confidentiality) or an optimized hybrid setup.
- Production stack: Claude Fable 5 & Claude Opus (Anthropic) · Gemini (Google) · orchestrated agents (n8n) · domain RAG · REST APIs · on-premise, SSO LDAP/AD.
AI in production, not in demos
- A project delivered end-to-end by AI: vehicle geometry analysis, from CAD to leak verdict, with no manual step
- Test report processing: quantified summary in 30 minutes instead of 4 hours
- Large-scale extraction of test and simulation data: stress, fatigue, thermal, crash
- Regulatory and standards checks: ISO 26262, MISRA, EuroNCAP requirements
- Embedded C/C++ code generated, then verified against the standards
- Your data stays with you: on-premise available, zero installation, VSOLUTION expertise included
What is on-premise AI?
AI installed on your own servers, on your premises or in your data center. It works without sending your documents to an outside service.
Does our technical data leave the company?
No. In an on-premise deployment, the model runs on your servers: your documents never leave your network and are not used to train a public model.
What is AI used for in an engineering department?
To find information within seconds across thousands of test reports, specifications and standards, to check a document's compliance and to prepare drafts of technical documentation.
Are the AI's results reliable?
Every answer cites its source documents and an engineer validates the result before use. Quality metrics are tracked to measure accuracy over time.
What hardware is required?
A server with graphics cards, sized according to the volume of documents and the number of users. We carry out this sizing with your IT department.
How do we get started?
With a pilot on a limited set of documents, for example your test reports, to measure the benefit before extending the deployment.