AI Infrastructure & MLOps for Pharmaceuticals
In pharma, a system that touches GxP work is not done when it works: it is done when it is validated, and it stays validated only if every change is controlled and documented. FDA expectations under 21 CFR Part 11 and computer system validation mean an AI tool supporting pharmacovigilance, evidence synthesis, or regulatory submissions needs reproducibility, audit trails, and change control built into its plumbing. MLOps is how AI earns a place in a validated environment. We build evaluation, observability, and CI/CD that produce the documented, traceable, reproducible behavior GxP demands, so your models can support submission and safety work without breaking validated state.
AI Infrastructure & MLOps, built for pharmaceuticals
We design the eval gate as a validation control: every model and prompt change runs documented tests with predefined acceptance criteria and an electronic, attributable approval before release.
We version models, prompts, datasets, and eval results so any output supporting a submission or a safety signal can be reproduced exactly, as Part 11 and CSV require.
We monitor drift, accuracy, and latency in production with audit-trailed alerting, feeding pharmacovigilance and quality teams a continuous record.
The stack runs in your validated cloud environment under your change-control and SOP framework, operated by your team.
Where it pays off in pharmaceuticals
Validated eval gate
Treat each model change as a controlled change with documented tests, acceptance criteria, and electronic sign-off, keeping the system in validated state.
Pharmacovigilance monitoring
Track case-processing and signal-detection model performance with audit-trailed drift alerts so safety coverage stays documented and defensible.
Reproducible evidence synthesis
Version every input and model behind literature review and evidence outputs so a result feeding a submission can be regenerated on demand.
Submission traceability
Maintain a complete, attributable record of model versions and outputs used in regulatory submissions, ready for inspection.
Pharma clients bring AI into validated workflows without breaking GxP state, cutting case-processing time in pharmacovigilance while keeping a Part 11 audit trail that holds up under FDA inspection.
Pharmaceuticals AI, answered
Yes, because change control is built into the pipeline. Every model or prompt change runs documented validation tests against predefined acceptance criteria and requires electronic sign-off before release, so updates happen inside your validated change-control process rather than around it.
We provide attributable, time-stamped audit trails, electronic approvals on every change, and full versioning of models, data, and outputs. That means any AI-supported result can be reproduced and traced to who approved it and what produced it, which is the heart of Part 11.
Yes. We version the exact model, prompt, and dataset behind every output, so an evidence-synthesis or pharmacovigilance result that supported a submission can be regenerated and defended during inspection.
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