What is hybrid AI for pharma and life sciences?
Hybrid AI uses different models for different tasks. In Axenivo, anything involving trial data, safety cases, compound data or unpublished research stays on private, open-source models in your own cloud, with small models answering first and larger models when needed. Tasks with no confidential data, such as summarising published literature or tracking public guidance, can use managed models such as Claude, GPT or Grok.
Why not private AI for everything, or a managed tool for everything?
Private-only means your scientists miss out on the most capable models for public literature. Managed-only sends confidential data to a vendor and bills every case and document by the token. Hybrid gives each task the right model: confidentiality where it matters, frontier capability where it helps, and the lower cost for each workload.
Can confidential data ever reach a managed model?
No. Every request passes a policy check before any model sees it. Anything involving trial, patient, safety, compound or submission data, or where the check is unsure, stays private. You decide which task types may use managed AI at all, and you can switch it off entirely.
Does Axenivo replace our safety database, LIMS or document management system?
No. Axenivo works on top of the systems you already run. Your safety database, LIMS, electronic lab notebook and document management system stay the systems of record; Axenivo helps your teams read, summarise and draft, grounded in your own SOPs.
Does Axenivo make safety, quality or regulatory decisions automatically?
No. Axenivo is decision support with human oversight. A qualified expert reviews every output and makes the decision, and nothing is submitted to a health authority or reported automatically. Your organisation defines the intended use of each workflow.
What is private AI for pharma and biotech?
In Axenivo, private AI is the part that handles confidential data. The models run inside your own cloud account or data centre, under your control, instead of on a vendor’s servers. Axenivo delivers it as an operated platform, so companies get the benefits without building an AI team.
How is Axenivo different from building pharma AI ourselves?
Running AI on confidential data is far more than provisioning a server with a GPU. It needs infrastructure provisioning as code, model serving, retrieval over your own SOPs, staging environments, release engineering with evaluation gates and rollback, observability, autoscaling, backup and security policy, all kept running every day. Axenivo delivers that complete stack, built on XePlatform, inside your own cloud account and operated for you, so a supervised pilot can start within weeks without new hires.
How does Axenivo support GxP, GDPR, HIPAA and the EU AI Act?
Axenivo is designed to support GDPR, HIPAA and EU AI Act obligations, as well as GxP expectations such as EU GMP Annex 11 and FDA 21 CFR Part 11. Confidential data is processed only inside your own account, every request and approval is logged, and we provide documentation for your impact assessments, supplier qualification and computerised system validation. Compliance itself depends on how your organisation uses the platform.
Which AI models does Axenivo use?
Open-source general and domain models run inside your account for all confidential work, alongside your own scientific models. New models are adopted only after they pass evaluation on your own cases. Managed models such as Claude, GPT or Grok are used only for tasks with no confidential data.
Can Axenivo run on-premise or in a private cloud?
Yes. Axenivo runs on the major public clouds in the region you choose, in Europe or North America, or in your private cloud or data centre, built on open standards so you can move without rebuilding.
How much does hybrid AI for pharma cost?
A fixed platform subscription plus your own cloud costs, with no per-token fees for confidential work. Managed AI is billed per token only for the tasks you route to it, under a monthly cap. Small models first, capacity that follows your working hours and batch runs, and shared hardware keep the cost of each answer low, which matters when case and document volumes grow with every study.
How long does a pilot take?
The environment is set up in hours and your SOPs loaded within days. A supervised pilot with your experts typically runs about four weeks, while your QA, IT and data-protection teams complete their review in parallel.
