Sovereign AI for Australian Biotech

The deployment is built backwards from the inspection. Every model interaction is logged in an attributable, contemporaneous, original and accurate manner consistent with ALCOA+ expectations. The infrastructure runs under documented Computer System Validation, with installation, operational and performance qualification evidence available. Change control covers model weights, retrieval index, prompts and access lists. When a TGA inspector asks to see how a particular regulatory document or PV case was prepared, you can produce the prompt, the retrieved sources, the model version and the user audit trail that produced it. Generic public AI tools cannot demonstrate any of those controls and would not survive an inspection.

It depends entirely on whose infrastructure the data is processed on and what their retention and use rights are. Pasting a novel mechanism into ChatGPT or a similar consumer service is, on the face of the terms of use, a disclosure to a third party with broad rights to retain and use the data — which can be argued to defeat absolute-novelty requirements in jurisdictions that demand it. A private LLM deployed under your control, where you are the only party with access to the inputs and outputs, does not create that exposure. We can provide your patent attorneys with the architecture documentation they need to confirm this for your specific filing strategy.

MTAs are reviewed during the use-case scoping phase. The standard approach is to scope the model so that MTA-restricted material is only ever processed by infrastructure that sits within the MTA-permitted boundary — typically a single-tenant deployment that you control, with explicit contractual confirmation that no third-party model provider retains, accesses, or uses any inputs or outputs. For the most restrictive MTAs (typically from large pharma or US federal grant programmes) we run an air-gapped on-premises deployment that the MTA counterparty can inspect if required.

No. The model is a retrieval and reasoning layer that sits on top of the systems your scientists already use, not a replacement for them. Scientists continue to record experiments in Benchling, LabArchives or Signals; the model ingests new entries in line with your data-governance policy and makes the cumulative record queryable. There is no requirement to migrate ELN data, no expectation that scientists change their daily workflow, and no impact on your ELN vendor relationship. Integration with LabWare or STARLIMS is similarly read-oriented.

Yes. The PBAC submission process is one of the highest-friction documentation exercises in Australian healthcare — Section 1.5 economic evaluation, Section 3 financial estimates and the supporting clinical evaluation can run to thousands of pages with strict cross-referencing. A custom LLM trained on the relevant Guidelines for preparing submissions, prior public summary documents and your own clinical-economic dataset can draft sections, identify gaps against the guidelines, and surface comparable prior submissions in the same therapeutic area. The final submission is still authored by your HEOR team, but the document-assembly cycle compresses substantially.

For an SME (under 100 staff, one to three active programmes) a typical timeline runs eight to twelve weeks: two weeks of use-case and GxP scoping, four to six weeks of validated ingestion and fine-tuning across ELN, trial documentation, regulatory dossier and grant records, then a pilot inside one therapeutic programme. The model is usually delivering useful regulatory and ELN retrieval inside the pilot window, with full company-wide roll-out following once the QA and scientific leadership are satisfied. For larger sponsors the timeline extends to four to six months because the validation and change-control evidence packs are correspondingly larger.

A due-diligence room is one of the most concentrated information-security exposures a biotech ever creates — every term-sheet party gets read access to your most sensitive material under an NDA whose enforceability is, in practice, limited. A private LLM lets your team prepare for and respond to diligence requests using AI without that AI being a separate disclosure surface. The internal preparation (gap analysis, expected-question synthesis, prior-Q-and-A retrieval) happens entirely inside your private model. The diligence room itself remains under your control. We have specifically engineered the deployment so that nothing in the diligence preparation flow creates an additional party that could be subpoenaed or breached in a separate incident.