AI that works where
mistakes are expensive.
In medicine development, engineering and healthcare, an AI that is usually right is not good enough. Xantage takes AI from strategy to running systems, and makes it dependable enough to rely on.
- Alfa Laval
- HOMA Pumps
- Streamline Hospital Services
- Uppsala University
- RLC Institute
- Yonsei University
- Dankook University
- NextAgents
- Quantale Intelligence
- Boroka
Decide. Build. Accelerate.
Decide
Where AI pays off, what to build or buy, and what the AI Act, CRA and MDR require of it.
Learn more →Build
Custom software, AI systems and data platforms, validated and taken all the way into production.
Learn more →Accelerate
Faster learning where the work happens: R&D, product management, sales and operations.
Learn more →A demo is easy.
Dependable is hard.
Most AI is right most of the time. That is enough for a demo, not for a process that patients, products or plants depend on. Xantage works the whole path, from the first strategy session to a system validated and live in production.
- 01
Scope
Find where AI pays off, and what it must prove before anyone relies on it.
- 02
Engineer
The system and the data foundation it depends on, built in short cycles with working software early.
- 03
Validate
Test it against the real thing: reference measurements, expert judgement and the rules that apply.
- 04
Run
Put it into production, help your people adopt it, and keep it dependable: we run it, or your team does.
Where it had to be right.
AI that turns a non-invasive sensor into a number a clinician can trust, validated in hospitals in Sweden and Korea.
Read the story → Decide · Build · Accelerate Alfa LavalThree engagements: product data behind customer self-service, field research that found the root causes, and AI enablement now underway.
Read the story → Accelerate · Spark RLC InstituteSpark, an AI coaching platform built with the Rapid Learning Cycles Institute, helps teams make R&D decisions on evidence.
Read the story →
