Capable AI inside the plant network. Not in someone else's cloud.
Biologics and small-molecule manufacturers are being asked to adopt AI with data that cannot leave the building: batch records, deviation investigations, tech-transfer packages, supplier quality data. NGARi runs the model on the appliance in your network and produces evidence that QA and IT can review.
The problem, stated the way your QA group states it
Every generative tool your teams want is a data-egress event. A deviation investigation pasted into a cloud assistant is a controlled record leaving the quality system. A tech-transfer package summarized by an API is intellectual property crossing a boundary your NDAs and export classification do not permit. The usual answer — "the vendor says it is secure" — is a vendor assertion, not evidence, and your quality system runs on evidence.
What we run, and where
| Layer | What it means for your site |
|---|---|
| On-premises inference | The model runs on hardware inside your network. No API call leaves for a prompt, a document or a summary |
| Air-gapped tenancy | Each site gets an isolated workspace; the runtime is designed to operate with no route to the internet |
| Hash-chained receipts | Every model load and inference writes a tamper-evident record you can hand to an auditor, not a log line you have to trust |
| No per-token billing | Capital purchase, not a metered cloud line that grows with adoption — the "zero token tax" case |
| Exportable evidence | Your data, your model versions and your receipts export as a signed bundle, verifiable by checksum |
Where it earns its place first
Not "AI everywhere". Three workflows where an offline model pays for itself and the risk is contained:
| Workflow | What the model does | What stays with your people |
|---|---|---|
| Deviation and CAPA investigation | Reads the batch record, the SOP and the prior similar deviations; drafts the timeline and the likely root cause | The root-cause decision, the CAPA, the signature |
| Regulatory and SOP Q&A | Answers "what does our procedure require here" with the clause it used | Interpretation and the quality decision |
| Tech transfer and supplier documents | Extracts parameters, flags conflicts across documents, summarizes for review | Approval, filing, and any commitment to a partner |
What we do not claim
NGARi is not a validated system out of the box, is not 21 CFR Part 11 certified, and does not replace your CSV, data-integrity or vendor-qualification process. What we provide is an appliance and an evidence trail designed to be qualified by you: deterministic model versions, no external dependencies at runtime, and receipts that make the assessment tractable. The limits are published at ngari.ai/limits and the disclosure at ngari.ai/transparency.
The economics
A metered API charges for every question your quality team asks, forever, and the bill grows exactly as the tool succeeds. An appliance is a capital line with a known number, and it keeps working when the network does not. For a site running hundreds of investigations a year, the crossover is measured in months, not years — and the avoided cost is not only the tokens, it is the review time currently spent re-checking a tool nobody can audit.
NGARi builds sovereign AI infrastructure for regulated operations. Written for quality, IT and manufacturing leadership in biopharma; no customer is named on this page.