AI that reads batch records, COAs and safety reports, while QA keeps every decision
We build document, vision and voice systems for pharma plants, quality units, pharmacovigilance desks and field forces in India and abroad. Every output is traceable, reviewable and designed to sit inside a validated process.
Pharma runs on records that must be right the first time: batch manufacturing records, certificates of analysis, SOPs, deviation reports, stability data and safety case files. AI helps today where people spend hours reading, cross-checking and re-typing these records, and where repetitive visual inspection or field reporting slows work down. It is good at flagging gaps, extracting values, drafting case intake and answering SOP questions with citations. It does not replace the Qualified Person, the QA reviewer or the safety physician, and it should not make release, causality or regulatory decisions. Any tool that touches GxP data has to be validated for its intended use, with audit trails and access controls, and we design for that from the first sprint.
Challenges we hear
Review by exception is hard on paper
Executed batch records are long and often hand-filled, so QA reads every page to find the few entries that need attention.
Data integrity applies to AI too
ALCOA+ principles, audit trails and electronic signature controls apply to any system that creates or changes a GxP record, including AI tools.
Safety information arrives everywhere
Possible adverse events reach the company through MR visits, customer care calls, emails and complaints, and each must be captured within reporting timelines.
A large, dispersed field force
Medical representatives and stockists work across languages and towns, so call reports, orders and sample records are often late or incomplete.
Where AI fits
Document Intelligence
Extraction and cross-checking for BMRs, COAs, SOPs, CTD dossiers and adverse event reports, with every value linked to its source page.
Computer Vision
Camera inspection of cartons, labels, blisters, tablets and vials on packaging and inspection lines, with an image kept for every rejection.
Voice AI
Hindi, English and regional-language voice agents for MR call reporting and stockist ordering, built to spot and route possible adverse events.
AI Security & Compliance
Validation support and adversarial testing for AI tools used in GxP processes, producing evidence your CSV and QA teams can review.
AI Sales & Marketing Automation
Consent-based doctor engagement and sample request workflows that keep the records expected under UCPMP 2024 and your MLR review.
Use cases
Regulatory Dossier and SOP Q&A Assistant
A question-answering assistant over your effective SOPs, specifications and CTD dossier modules that cites the exact section, respects versions and access rights, and says when it does not know.
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Batch Manufacturing Record Review
Pre-review of executed batch manufacturing and packaging records that flags blank fields, out-of-limit values, signature gaps and unlinked deviations, with page references, before QA starts.
Certificate of Analysis Extraction and Spec Matching
Read supplier and in-house certificates of analysis, extract every test result and compare it with your approved specification, so QC sees out-of-limit, missing and mismatched lines at a glance.
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Adverse Event Intake from Emails and Calls
Spot possible adverse events in emails, call recordings and field reports, extract the minimum case criteria and draft an intake record for your pharmacovigilance team to verify.
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Packaging and Label Inspection
Camera checks on cartons, labels and blister foils that verify batch, expiry, MRP and codes, compare print against approved artwork, and reject mismatches with image evidence.
Tablet and Vial Visual Defect Detection
Vision models that flag chipped, cracked, spotted or broken tablets and cap, crimp, glass or particle defects in vials, sending uncertain units to manual inspection and storing every reject image.
Medical Rep Call Reporting by Voice
Medical representatives speak a short note in Hindi, English or a regional language after each doctor visit, and the agent drafts the call report, sample entries and follow-ups for them to confirm.
Distributor and Stockist Order-Taking Voice Agent
A voice and WhatsApp agent that takes orders from stockists and chemists by brand and pack, checks stock, schemes and credit, reads the order back and posts it to your ERP or DMS.
Validation and Security Testing of AI Tools in GxP Settings
Risk-based testing of LLM and ML tools used in quality, manufacturing or pharmacovigilance: accuracy, prompt injection, data leakage and audit-trail checks, packaged as validation evidence.
Doctor Engagement and Sample Request Automation
Consent-based WhatsApp and email journeys that send MLR-approved content to doctors, capture sample and literature requests, and route each one to the right MR or team with a full record.
Regulatory context
21 CFR Part 11 (US) and EU GMP Annex 11
Set expectations for electronic records, electronic signatures and computerised systems in regulated activities, including validation, audit trails and access control. AI tools that create or change GxP records fall within their scope.
Revised Schedule M (India)
The revised GMP requirements under the Drugs and Cosmetics Rules, notified in 2023, bring Indian manufacturing closer to WHO-GMP, with stronger expectations on the pharmaceutical quality system, quality risk management and documentation.
Pharmacovigilance obligations (CDSCO, PvPI, ICH E2B)
Marketing authorisation holders in India are expected to run a pharmacovigilance system and report adverse drug reactions, with PvPI coordinating national reporting. ICH E2B(R3) defines the electronic format for individual case safety reports used by many regulators.
UCPMP 2024 and DPDP Act 2023 (India)
The Uniform Code for Pharmaceutical Marketing Practices 2024 governs interactions with healthcare professionals, including samples and promotional material. The DPDP Act 2023 applies to doctor, patient and employee personal data.
FAQs
01Can AI be used inside a GxP-regulated process?
Yes, when it is validated for a defined intended use and controlled like any other computerised system. We work within your CSV or CSA approach: user requirements, risk assessment, test evidence and change control. In the systems we build, AI prepares and flags; trained people review and sign.
02Will the system make batch release or causality decisions?
No. Batch disposition stays with the Qualified Person or authorised QA, and seriousness and causality assessments stay with your pharmacovigilance team. The system prepares checked records and highlights exceptions so those decisions are easier to evidence.
03Where is our data processed?
In your own cloud account or on-premise, depending on your data policy. We can use model endpoints that do not retain or train on your data, and patient and doctor details are masked in logs.
04How do we start?
With a short pilot on one product, one record type or one line, run in parallel with your current process. The pilot produces an accuracy report against your own reviewers and a validation-ready plan for production.
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Founded by Akhilesh Mittal. 8 years building a security platform for a New Zealand company. IIT ISM Dhanbad and IMT Ghaziabad. Builds production agentic AI.
Test one record type on your own batches
Book a discovery call to choose a narrow pilot, such as BMR pre-review for one product or COA checks for one material group, and compare the results with your QA team's own review.