An AI copilot for pharmaceutical visual inspection (Sichtkontrolle) — guiding batch workflows, reconciling quantities, and explaining discrepancies in plain language.
Visual inspection is one of the most demanding steps in sterile pharmaceutical production. Operators work under time pressure, every unit must be accounted for, and a single unbooked AQL sample can turn into a deviation and hours of investigation.
PharmaPilot AI is a prototype of a digital assistant for exactly this environment. It walks through a batch from receipt to release checklist, keeps a live quantity reconciliation (Menge) running in the background, and when the numbers don't balance, the built-in AI copilot explains why — before it becomes a deviation.
Designed by a software engineer who went into GMP production deliberately — to study these workflows where they actually happen, and to build software that solves real problems, not imagined ones. Every scenario in this demo — the forgotten AQL scan, the eject booked to the wrong category, the count that doesn't balance at shift end — comes from direct process observation on the production floor.
A step-by-step checklist covering the full inspection lifecycle — batch takeover from filling, material verification, machine setup, automated inspection run, Muster & AQL sampling, manual re-inspection, 100% manual control, label & barcode verification, quantity reconciliation, and batch record review — with live status tracking.
The core formula of every inspection batch, always visible and always up to date:
received = good + ejects + samples + breakage
The panel tracks input units, good/labelled units, eject categories (physical defects → Re-Sicht, particle/liquid → Re-Sicht, uncertain → rails), AQL and Muster samples — and flags unbooked removals the moment a delta appears.
A conversational assistant that understands the context of the running batch. Example from the demo:
"The system prompted an AQL sample of 125 units from the good stream, and the quantity balance shows Δ −125 unaccounted. That usually means the units were taken but the AQL tab wasn't scanned. Sound familiar?"
Built-in quick diagnoses for the most common real-world discrepancies:
- I forgot to scan the AQL tab
- Ejects booked to the wrong category
- I forgot to switch the eject mode
- Wrong material at the machine
- The final count doesn't balance
A release checklist that mirrors how batch records are reviewed before release — designed with GMP documentation principles (ALCOA+) in mind.
- Frontend: Single-file HTML / CSS / JavaScript — zero dependencies, runs anywhere
- Design: Industrial MES-inspired UI, optimized for shop-floor readability
- Data: Mock batch data for demonstration purposes
git clone https://github.com/mahbejam/pharmaPilot-AI.git
cd pharmaPilot-AI
# open index.html in your browser — no build step, no installThis is an MVP demonstrating the core concept. Planned next steps:
- Connect the chat to a real LLM
- Rules engine for automatic state detection
- Expanded exception library
- Persistent batch history (localStorage → small backend)
- Shift handover summary generated by the AI copilot
- Deviation pre-report drafting from reconciliation deltas
- German / English interface toggle
PharmaPilot AI is a frontend prototype using mock data only. It is not affiliated with any real manufacturing system, product, or company. All batch numbers, materials, quantities, and process steps are invented. Built for demonstration and portfolio purposes.
Mahbube Bejam Software Developer (B.Sc. Software Engineering) — building AI-assisted tools for pharma and healthcare digitalization.
This project is based on deep first-hand insight into GMP production processes — I went into the industry to study these workflows where they actually happen, in order to design software that solves real problems, not imagined ones.
- GitHub: @mahbejam
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MIT — free to use, modify, and build on.