Use case
ANESLAB — our own project — currently assists doctors with medication and pharmacological interaction queries. Currently under scientific validation at a university hospital.
AI under scientific validation at a university hospital. For European healthcare reality.
We build AI solutions for healthcare centres, clinical teams and companies in the health sector. Real anonymisation of clinical data, GDPR and ENS compliance, and understanding of how a hospital actually works.
Common challenges
01
Time wasted looking up protocols in outdated systems
EHRs are slow; finding the right clinical guideline takes longer than it should.
02
Inconsistency in clinical practice across professionals
Everyone consults different sources; divergent criteria within the same centre.
03
Risk of sensitive data leaks
Generic AI tools don't comply with GDPR for clinical data.
04
Administrative load that takes time away from patients
Documentation, reports and authorisations consume hours that should go to the patient.
Services applied
Intelligent lookup
Questions about clinical protocols, therapeutic guidelines or healthcare regulation, with exact citation to the official source.
Document review
Automated analysis of medical reports, clinical histories or consent forms to detect errors or inconsistencies.
Intelligent inbox
Automatic classification of the centre's communications by administrative priority.
Predictive analytics
Anticipating demand for care, healthcare resources or clinical risks on your own historical data.
Security applied to this sector
GDPR compliance applied to special category data (article 9). Anonymisation aligned with the Spanish AEPD's criteria for healthcare research. Infrastructure with indirect ENS High (Azure).