Scientific response drafting, field insight synthesis, publication and congress monitoring.
AI models for the pharmaceutical industry
AI models fully trained for the work of a pharmaceutical company.
Pharmaccelerator trains domain models for medical affairs, regulatory, market access, supply chain and manufacturing, commercial and marketing, and strategy teams. Each model is implemented on-premises or in a private cloud, where the company's information stays fully under its control, with governance and industry policies built in.
Submission authoring support across CTD modules, agency query responses, labelling consistency checks.
HTA appraisal analysis, value dossiers, pricing and reimbursement scenarios by market.
Demand and shortage signals, batch record and deviation review, supplier and quality documentation.
Account and territory intelligence, campaign and content planning with compliant review, launch readiness.
Portfolio and asset assessment, competitive intelligence, scenario planning.
Under your control, by design
Every model is delivered as a product the company owns, implemented where its information already lives, and operated under the governance and industry policies the organisation is held to.
On-premises or private cloud
Implemented in your own data centre or in a private cloud of your choice. Prompts, documents, outputs and weights stay inside your environment.
Information fully under your control
Access, retention and monitoring follow your rules. No data leaves the boundary, and nothing is pooled with other companies.
Governance and industry policies
Role-based access, audit trails and model documentation, with the policies your industry works under applied: GxP and computer system validation, pharmacovigilance and promotional-code rules, GDPR and the EU AI Act.
A product you own
The trained model is yours, not a subscription to us. A specialised partner packages it into an optimised infrastructure for the hardware and setup agreed with you.
Get in touch
Tell us which team, and which decisions the model should support.
We will come back with a view on the data you already hold, what a fully trained model could add, and what it would take to implement one inside your environment.