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Pharmaceuticals · AI on your own servers

Why a pharmaceutical company needs sovereign AI

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.

Where the AI runs and where your documents stay
Your servers
Every finding shows the page it rests on
Cited
Irreversible actions wait for your reviewer
A person approves
Paid pilot, 4–6 weeks on your own past documents, ex-GST
₹5–8 L
Book a scoping call
30 minutes with an engineer. We map your workload and walk you through the product on sample data. You leave with a written next step.
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What sovereign AI for a pharmaceutical company is

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.

Why sovereign

Five reasons pharma cannot use ordinary cloud AI

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.

Partners forbid it

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.

Regulators ask where data went

Inspections look at data integrity and access. "It was sent to an AI service abroad" is a hard sentence to defend.

Patient data in trials and safety reports

Case narratives and trial records contain personal data under the DPDP Act, with duties and penalties for how they are handled.

Consistency, not creativity

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.

Six jobs, your rules

What sovereign AI does in a pharma company

SOP and batch-record review

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.

Deviation, OOS and CAPA drafting

A first draft of the investigation report assembled from the record, the SOP and past cases, every line sourced. QA writes the conclusion.

Regulatory dossier questions

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.

Pharmacovigilance intake

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.

Medical information answers

Responses drafted only from your approved labels and documents, with the citation, for medical affairs to approve before anything goes out.

Tender and contract checks

The same agents read procurement tenders and supply contracts clause by clause and cite what they find.

Three steps

How it is set up

  1. Install.

    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.

  2. Ground.

    The agents read the documents you give them: SOPs, specifications, batch records, dossiers, labels. Every answer and finding cites its source.

  3. Decide and log.

    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.

The law and the audit

The rule that binds: data integrity and personal data

Data integrity

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.

Personal data

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.

Validation

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.

Our approach

A person decides

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.

Honest status

What runs where, plainly

On your servers

AI Factory, Agent OS, the models and your documents.

No pharmaceutical customer yet

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.

Not a compliance certificate

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.

Built to your site

Agent OS applied to your documents and rules, scoped per plant or per function, not an off-the-shelf pharma product.

Own it

Pricing

ItemPrice (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 serverAt cost; indicative ₹68–80 L for a four-GPU server, varies with the market

We show ranges and never publish one client's price.

FAQ

Frequently asked questions

What does sovereign AI mean for a pharmaceutical company?

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.

Does any document leave our network?

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.

Is the system validated for regulated use?

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.

Which documents can it read?

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.

Can it submit a safety report or change a record?

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.

Who has used this before?

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.

Start with one document type

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.