AI in GMP Manufacturing for Pharma and Chemical Companies
AI in GMP manufacturing drafts deviation reports, CAPA and batch documentation from your raw data and pre-reviews batch records. NexPatch runs the models on your own servers or in a private cloud in Germany and connects them to LIMS, MES and QMS. Every statement links to its source document, and release stays with the qualified person.
More documentation, fewer specialists
Chemical and pharmaceutical production in Germany fell by 3.0% in the first half of 2026. At the same time, around a quarter of open positions in the pharmaceutical industry remain unfilled, and chemical and pharmaceutical technicians account for almost half of that gap.
These are the people who spend a growing share of their time on deviation reports, protocols and records instead of process and product. This is where private AI helps: it writes the draft, your team reviews and approves.
VCI, half-year report 07/2026 · IW study for vfa, 10/2024
Regulation: what AI in pharma and chemicals has to meet
AI in a regulated environment needs an architecture that survives an inspection. Three sets of rules shape every solution, and we plan for them with your quality assurance team before the project starts.
EU GMP Annex 22 on artificial intelligence
Annex 22 introduces dedicated rules for AI in GMP. The July 2025 draft requires static, deterministic models with validation and explainability for critical GMP applications. Generative AI remains possible outside critical decisions, with review by a human. The EMA work plan targets the final text for the end of 2026. We already build to this draft today.
Annex 11, Chapter 4 and data integrity
Annex 11 and Chapter 4 are being revised in parallel. Audit trails, lifecycle validation and data integrity under ALCOA+ are part of our architecture, not an afterthought.
NIS2
Manufacturing of pharmaceutical products is one of the sectors of high criticality, and chemicals are one of the other critical sectors. From medium company size onwards, risk management and reporting duties can apply.
Run privately: formulations and study data stay in your house
Formulations, synthesis routes, study data and batch records are your capital. We run language models and data on your servers or in a private cloud in Germany, without calling external AI services. Every use is logged with an audit trail, and every answer links to its source document. You pay predictable costs instead of per-token billing and stay free of vendor lock-in.
Typical flow: from deviation to approved report
Capture.
The deviation is recorded in the QMS as usual.
Research.
The AI gathers raw data, logbooks and comparable past cases.
Draft.
The AI writes a structured report draft in your terminology, with a reference to every source.
Assessment.
Your specialist reviews the draft, assesses root cause and risk and defines the CAPA.
Approval.
The report is approved in the QMS with a complete audit trail.
Integration with LIMS, MES, QMS and ERP
Your company runs on LIMS, MES, QMS and ERP, and we do not change that. We connect the AI through interfaces, and results arrive where your team needs them.
LIMS and laboratory
Test orders, measurements and certificates flow automatically into reports, trend analyses and batch documentation.
MES and production
Process and batch data from MES and historian feed forecasts, deviation analyses and electronic batch records.
QMS and DMS
SOPs, deviations, CAPA and training records stay in your usual quality system, while the AI contributes and documents every step.
ERP
Demand, inventory and verified receipts flow on in structured form, including e-invoicing.
Use cases from lab to plant
Deviation reports and CAPA
Raw data, logbooks and past cases become structured report drafts, stored in an audit-proof and searchable way.
Batch record review by exception
The AI pre-reviews batch records, flags gaps and anomalies and prepares the release. The decision stays with your qualified person.
Safety data sheets and REACH
Safety data sheets, raw material documents and input for REACH dossiers are recognized, extracted and assigned to substances and formulations.
Knowledge assistant for SOPs and specifications
The system answers questions on SOPs, test methods and hazardous substance rules from your own documents, with source references. New employees get up to speed faster.
Forecasts for yield, demand and maintenance
Yield and quality forecasts per batch, demand planning and predictive maintenance, run on our data platform Orpheon.
How a project starts
Audit. Analyze processes, system landscape, data quality and savings potential, and set priorities per use case.
Design. Architecture, model selection, interfaces and permission concept, aligned with LIMS, MES and your GxP requirements.
Pilot in 72 hours. A first use case on your data, followed by rollout in short, measurable steps.
Deploy. On premises or private cloud, including security, validation documents and training. We expect 4 to 10 weeks until production.
Operation. Monitoring, updates, model maintenance and governance with 99.9% availability.
For strategy, consulting and training we work with our partner Alic.ai. One engagement, two clearly separated roles, one shared outcome.
Frequently asked questions
In 60 minutes we show which documentation and quality processes can be automated and what a GMP-ready architecture looks like in your setup.