Pharma & Life Sciences · Packaging line inspection
A label error or under-filled blister found after release costs more than the batch
Banao deploys vision AI on pharma packaging and fill lines — blister pack integrity, vial fill levels, label accuracy, cap presence, and foil seal completeness — with a per-unit audit image record built in for GxP traceability.
The system integrates with existing line cameras and reject gates. Models are version-controlled and validated under your change-control process; no separate GxP wrapper is grafted on after the fact.
CP Plus— line-camera vision infrastructure applied to packaging and label verification at a manufacturing site.
The first call is free · 45 minutes · no obligation
What we build
What a packaging line inspection build delivers
Each item below corresponds to a failure mode you already track — fill-level OOS, label mix-up, blister integrity. We start with the one that costs most.
Fill-level and blister integrity detection
Vision models check every cavity of a blister pack and every vial in a tray for correct fill level, presence, and seal integrity — at line speed, before the reject gate.
Label and print accuracy verification
OCR and template-match verify lot number, expiry date, product code, and barcode against the batch record in real time, stopping mislabelled units before they enter the carton.
Cap, closure, and foil seal completeness
Presence models confirm cap application, crimp depth, and foil seal completeness on vials, ampoules, and sachets — the checks that manual inspection misses at high line speeds.
Per-unit audit image archive
Every unit inspected generates a timestamped image record linked to its lot and batch data — a GxP-ready audit trail that replaces end-of-line sampling with 100% coverage.
Deviation alerting into your QMS
Out-of-specification events write directly to your quality management system as deviations, with the trigger image attached — no manual transcription, no delay in opening the CAPA.
Model versioning and validation pack
Each deployed model carries a validation package — IQ/OQ protocols, performance benchmarks, and version history — ready for a regulatory inspector or internal audit without additional preparation.
Receipts
Where this approach is running
Metrics shown dotted (··) are being finalised in our verified metrics pack — published once confirmed.
Vision layer added to existing line cameras for packaging verification
Banao applied a computer-vision inspection layer to the client's existing line-camera infrastructure, adding defect detection and label verification without replacing hardware already on the floor.
Dogfooding
We run our own systems in production before we build yours
Banao operates a ~300-person engineering business on its own AI tools. InterviewGod processes every engineering hire we make; Vikaas runs our own demand generation. A system that runs on our own operation every working day has already been debugged past proof-of-concept.
That is the standard we apply to pharma inspection work: the model runs in production, carries audit logs, handles operator corrections, and earns its place on the line — or it does not leave our lab.
Screens every Banao engineering hire in production.
Runs Banao's own demand-gen pipeline daily.
The honest version
When vision inspection on the packaging line is the wrong call
Vision AI earns its cost only under certain conditions. We will say so before we quote:
- Low line speed: below a threshold your throughput sets, a trained quality technician with a consistent SOP is cheaper. We will be direct about that number for your line.
- Unstable packaging formats: if your format changes weekly, retraining cost erodes the ROI faster than the inspection earns it back.
- Poor imaging conditions: if the line layout prevents consistent lighting and framing, the week-one discovery is an imaging problem, not a modelling one — and sometimes the honest answer is 'not yet, given this layout.'
- Validation timeline mismatch: if your release date is six weeks away and your internal validation cycle is eight, we will scope a phased approach rather than over-promise.
How we start
How a packaging line inspection engagement starts
We do not quote a vision system from a specification sheet. We assess your actual line, cameras, and defect history first.
- 01
AI Discovery Sprint
2 weeks · fixed price
We review your existing line-camera output, your highest-frequency defect types, and your current reject gate performance, then hand back a feasibility assessment, accuracy estimate, and validation-effort forecast — yours to keep regardless of next steps. If you proceed, the Sprint fee credits against the build.
- 02
Build and validate
Model training on your defect classes, integration with your reject gates and QMS, validation package authored to your change-control requirements, and operator training before go-live.
- 03
Production and continuous improvement
Live monitoring with operator override, per-unit audit archive, and a scheduled model review against new defect patterns or format changes.
FAQ
Frequently asked questions
How many defect samples do you need before training starts?
Enough to cover your real failure modes — typically a few thousand images across your priority defect classes. The Discovery Sprint establishes whether your existing line-camera footage is sufficient or whether a targeted capture run is needed before training.
Can the system work with our existing line cameras?
In most cases yes. Banao assesses your current camera positions, resolution, and lighting in week one. Where imaging is workable, we deploy on existing hardware. New cameras are specified only when the current setup genuinely limits the accuracy you need.
How does validation work for a GxP environment?
Each model is delivered with an IQ/OQ validation package — test protocols, performance benchmarks, and version-controlled model artefacts. We work within your existing change-control process rather than adding a parallel one.
What happens when the model makes a wrong call?
The operator override is part of the standard build. Incorrect calls are flagged by the line team, the image and correction are logged, and the correction feeds back into the next model update cycle. Your quality team retains full decision authority throughout.
Does the system cover blister lines and vial or ampoule fill lines?
Yes. The inspection architecture covers blister pack integrity, vial and ampoule fill levels, label and print accuracy, cap and closure presence, and foil seal completeness. We scope which checks apply to your specific line formats during the Discovery Sprint.
Get started
Show us your line's hardest defect class
Bring your current line-camera setup and your top three escape categories. In 45 minutes we will tell you whether vision inspection is worth building on your line — and what validation looks like in your environment.
Book a Discovery Sprint