The documents are the company
Formulations, process parameters, stability data and trial results are trade secrets. A cloud AI provider is another party holding them.
Your formulations, your trial data, your regulatory dossiers and your deviation reports are the company. Sending them to a cloud AI service to save a reviewer an afternoon is a trade most pharma boards will not make. Sovereign AI is the other way: the model comes to your documents, on a GPU server in your plant or your office, and a named person approves anything that matters. BiltIQ AI builds exactly that, and tells you plainly what it has and has not yet done in pharma.
We'll walk you through it on sample documents.
Sovereign AI for a pharmaceutical company is AI that runs on the company's own servers, reads its SOPs, batch records, dossiers and safety reports, cites the page behind every finding, and leaves decisions to a named reviewer. BiltIQ AI delivers it with AI Factory and Agent OS: open AI models on your GPU server, outbound AI calls blocked by default.
Formulations, process parameters, stability data and trial results are trade secrets. A cloud AI provider is another party holding them.
Licensing, contract-manufacturing and co-development agreements usually bar disclosure of each other's technical data to third parties. A cloud model is a third party.
Inspections look at data integrity and access. "It was sent to an AI service abroad" is a hard sentence to defend.
Case narratives and trial records contain personal data under the DPDP Act, with duties and penalties for how they are handled.
A reviewer needs the same answer to the same SOP question every time, with the source shown. A general chatbot gives a fluent answer with no page.
Agents check a completed batch record or a draft SOP against your rules, flag gaps and conflicts, and cite the page for each finding. Your QA reviewer accepts or rejects each one.
A first draft of the investigation report assembled from the record, the SOP and past cases, every line sourced. QA writes the conclusion.
Ask what a module says, where a specification is stated, or which documents mention a change. The answer comes from your own files, with the page.
Adverse-event reports summarised into your case template, with the source text shown, for the safety team to assess. Nothing is submitted without a person.
Responses drafted only from your approved labels and documents, with the citation, for medical affairs to approve before anything goes out.
The same agents read procurement tenders and supply contracts clause by clause and cite what they find.
AI Factory serves open AI models on a GPU server at your site. Agent OS runs the agents on it. Outbound AI calls are blocked by default, so a document never reaches an outside service.
The agents read the documents you give them: SOPs, specifications, batch records, dossiers, labels. Every answer and finding cites its source.
A named reviewer approves or rejects each finding. Anything that would change a record waits for a person. Every request is logged, so an auditor can see who asked what and what the AI answered.
Inspectors expect to know who touched a record and when. The AI's log records every request and every approval, and the AI itself writes nothing to a record on its own.
Trial subjects and adverse-event reporters are people. Their data is personal data under the DPDP Act. On your own server there is no second party in the model path. Check the current rules on meity.gov.in.
Computer systems in regulated use need documented validation. The pilot produces the evidence your validation team needs: fixed model versions, test documents, measured results and a findings report.
The AI reads and drafts; your reviewer decides. Findings carry the page they rest on so QA can check them in seconds. Irreversible actions wait for a person, and that rule is written in the software, not in a contract clause. Like any AI it can make mistakes, which is why the page is always shown. The same controls run our agents for health-claim checking, the work that won the NHA PM-JAY AI Challenge 2026, a hackathon.
AI Factory, Agent OS, the models and your documents.
BiltIQ has not deployed in a pharmaceutical company. The first step is a paid pilot on your own past documents, under NDA, measured against your reviewers' results.
We make no claim that the system is validated or compliant for your use until your team has validated it. The pilot gives you the evidence to do that.
Agent OS applied to your documents and rules, scoped per plant or per function, not an off-the-shelf pharma product.
| Item | Price (ex-GST) |
|---|---|
| Paid pilot, 4–6 weeks on your own past documents, under NDA | ₹5–8 L, credited against the licence |
| Site licence, one-time (Agent OS included) | ₹50–90 L |
| Managed service, a year (updates, monitoring, quarterly review; no access to your documents) | ₹16–18 L |
| GPU server | At cost; indicative ₹68–80 L for a four-GPU server, varies with the market |
We show ranges and never publish one client's price.
AI that runs on your own servers, reads your own documents, cites the page behind each finding, and leaves decisions to your reviewers. No formulation, batch record, dossier or safety report goes to an outside AI service. BiltIQ delivers it with AI Factory and Agent OS on a GPU server at your site.
No. The models run on your GPU server and Agent OS blocks outbound AI calls by default. The managed service monitors the server's health without access to your documents. Your own network and access rules apply to everything else, and the pilot runs under an NDA.
Not until your team validates it, and we say so. The pilot fixes the model versions, uses test documents you choose, measures the results against your reviewers, and hands over a findings report. That is the evidence your validation and quality teams need to decide.
SOPs, specifications, batch records, deviation and CAPA files, regulatory dossiers, labels, contracts and tenders, in the formats you hold them, including scans. You decide which documents the agents may read, and every answer shows the page it came from.
No. The AI drafts a summary or a finding; a named person reviews it. Anything that would change a record or send a submission waits for a person, and that rule is written in the software. Every request and approval is logged.
No pharmaceutical company yet. The same agents and controls won the NHA PM-JAY AI Challenge 2026, a hackathon, for checking health claims against policy, and run in our own company. A paid pilot on your own past documents is how a first buyer tests it.
Pick the review that takes your QA team the longest. An engineer will walk you through it on sample documents, and you leave with a written next step.
Prefer to talk? Call +91 89868 60088 or write to [email protected].