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    Industrial AI solution

    Pharma Visual Inspection AI Tablets, Vials, Ampoules & Packaging

    GxP-aligned AI visual inspection for tablets, capsules, vials, ampoules, blisters and cartons. 21 CFR Part 11 audit trails, validation support and MES integration.

    Plan the use case Explore ROI tools
    Computer vision inspection of pharmaceutical vials and tablets

    Acceptance before scale

    Baseline, representative test set, failure conditions and a named human owner are defined before production approval.

    Direct answer

    NeoBram delivers GxP-aligned visual inspection AI for pharma manufacturing - tablet, capsule, vial, ampoule, blister and carton inspection with full 21 CFR Part 11 audit trails, validation documentation (IQ/OQ/PQ), and integration with MES, LIMS and QMS.

    Private deployment available

    Why evaluate it

    Visual Inspection Is A GxP-Critical Bottleneck.

    The decision is whether this capability improves a defined workflow safely not whether an AI demo looks impressive.

    We deploy production-grade systems with full GxP validation support - URS, FAT/SAT, IQ/OQ/PQ documentation, computer system validation per GAMP 5, and 21 CFR Part 11 compliant audit trails. Models, datasets and inference are all version-controlled and validated.

    Uncited universal benchmarks from the legacy page were withheld. Define the baseline and acceptance threshold from customer evidence or a reviewable primary source.

    Candidate capabilities

    What a production solution may need to do.

    Each capability is validated against representative data and the customer's workflow. Product or model names describe possible components, not partnerships, certifications or guaranteed compatibility.

    Injectable particulate inspection

    A candidate capability to test against a documented baseline, representative scenarios and agreed human-review workflow.

    • Sub-50 micron particulate detection in clear and amber containers
    • Air-bubble vs particulate discrimination
    • Container defects (cracks, scratches, fill level)
    • Stopper / cap presence and seating

    Tablet & capsule defect detection

    Detect chips, cracks, capping, lamination, foreign matter, embossing defects and colour variation on tablets and capsules at high throughput.

    • Chips, cracks, capping, lamination, sticking
    • Embossing and break-line verification
    • Colour and coating uniformity
    • Foreign-matter contamination

    Packaging & serialisation verification

    Blister fill check, carton coding (batch, expiry, serial), leaflet presence and label/artwork verification - aligned with EU FMD and DSCSA serialisation.

    • Blister cavity fill and tablet integrity
    • Carton batch / expiry / serial OCR & verification
    • Label artwork match against approved master
    • Leaflet presence and orientation

    GxP-validated MLOps

    Versioned, signed models with dataset lineage, IQ/OQ/PQ documentation, change-control workflow and 21 CFR Part 11 compliant audit trails for every inspection decision.

    • URS / FAT / SAT / IQ / OQ / PQ documentation
    • GAMP 5 software categorisation and validation
    • 21 CFR Part 11 audit trail of every decision
    • Change control for model retraining

    Architecture context

    Select components after the boundary and test are clear.

    The list is a design vocabulary. Final selection depends on licences, data location, latency, security, existing systems and customer approval.

    Inspection hardware

    • Brevetti CEA / Bonfiglioli

      Particulate inspection machines

    • Basler / Cognex industrial cameras

      High-resolution capture

    • Structured lighting + backlights

      Particulate contrast

    • Telecentric lenses

      Dimensional precision

    Models

    • Custom CNN / Transformer

      Particulate detection in liquids

    • YOLO v11

      Tablet & capsule defect detection

    • PaDiM / PatchCore

      Anomaly detection on rare defects

    • OCR engines

      Batch code & expiry verification

    Validation & governance

    • GAMP 5 CSV

      Computer system validation

    • 21 CFR Part 11 audit trails

      Immutable electronic records

    • MLflow Model Registry

      Versioned, signed models

    • Dataset versioning (DVC)

      Reproducible training data

    Integration

    • Werum PAS-X / Siemens Opcenter

      MES integration

    • LabWare / STARLIMS

      LIMS reject & batch records

    • TrackWise / Veeva QMS

      Deviation & CAPA

    • SAP S/4HANA

      Batch genealogy

    Planning ranges

    A standard path from decision to operation.

    01

    Discovery and qualification

    1-2 weeks

    Named workflow, owner, baseline, risks and go/no-go questions.

    02

    Readiness assessment

    2-4 weeks

    Data, integration, security, value and operating-readiness findings.

    03

    Technical proof of value

    4-6 weeks

    A bounded test on representative data with documented limitations.

    04

    Production pilot

    8-12 weeks

    One controlled workflow, integrated and evaluated with real users.

    05

    Enterprise or multi-site rollout

    3-6+ months

    Phased scale-out, monitoring, support and change management.

    06

    AI capability or CoE programme

    3-6+ months

    Governance, delivery methods, reusable assets and team enablement.

    These are planning ranges, not guarantees. Readiness, validation, hardware, integration, access and change management affect the schedule.

    Production safeguards

    Private deployment is one control, not the whole control system.

    Data boundary

    • Select offline, edge, on-premises or private-cloud deployment from the real operating constraints.
    • Document data flows, storage, deletion, backups and support access before production.
    • The customer approves every interface and any permitted external connection.

    Model and application controls

    • Evaluate representative cases, uncertainty and harmful failure modes before use.
    • Use suitable access control, input handling and output guardrails for the selected risk.
    • Treat grounding and citations as testable behaviours, not as a promise of perfect answers.

    Traceability and operation

    • Define identity, roles, logs, monitoring, updates, backup and incident handling.
    • Keep the evidence needed to investigate outputs and reproduce important decisions.
    • Assign a named business and technical owner for production operation.

    Human authority

    • Domain, quality, safety and regulatory owners retain decision authority.
    • Compliance depends on the implemented system and the customer's validated controls.
    • Escalation and safe fallback are part of the acceptance criteria.

    Limitations to plan for

    Performance can change with data quality, equipment, process, product mix, documents, users or operating conditions. Third-party model and software licences still apply. AI output does not replace the responsible engineer, operator, quality owner, safety professional, legal adviser or regulator.

    Buyer questions

    Direct answers before you plan a pilot.

    How do you validate an AI model under GAMP 5 and 21 CFR Part 11?+
    We treat the model + dataset as a validated software system. Validation deliverables include URS, functional & design specs, IQ/OQ/PQ protocols, traceability matrix, and a documented test set with acceptance criteria (sensitivity, specificity, AUC). Models and training datasets are versioned with cryptographic hashes. Every inference decision is logged with model version, image hash, timestamp and user - meeting 21 CFR Part 11 electronic record requirements.
    Can we use AI inspection as the sole release decision, or only as an aid to human inspectors?+
    This legacy draft included a quantitative benchmark that has not been published with a reviewable source or customer evidence. NeoBram now treats it as an open validation question and defines the baseline, test method, acceptance threshold and limitations during discovery.
    How do you handle model updates without revalidating from scratch?+
    We use a tiered change-control framework. Minor retraining on new defect images (same model architecture, same data domain) goes through a like-for-like equivalency test. Major changes (new model architecture, new product line) trigger full re-qualification. This is documented in the site's validation master plan and agreed with QA upfront.
    What's the minimum defect dataset needed for injectables?+
    For particulate inspection we typically need 500-2,000 known-defect images per particulate type and 5,000+ known-good images per container format. Many of these come from your existing rejected-units library. We supplement with controlled spike-and-recovery studies for rare particulate types during validation.
    How does this integrate with our MES and batch records?+
    Direct integration with Werum PAS-X, Siemens Opcenter, or your MES via the standard OPC UA or REST APIs. Each inspected unit is logged against the batch with image, model version, decision, and operator review (if any). Rejects flow into TrackWise / Veeva QMS as deviations for investigation. LIMS receives aggregate batch quality data.
    What's typical pilot scope and timeline?+
    A focused production pilot is commonly planned over 8 12 weeks after scope, representative data, owners and acceptance tests are ready. Discovery or a technical proof of value may be shorter; integration, validation, hardware, site access and change management can extend the plan. This is a planning range, not a delivery guarantee.

    Bring one workflow

    Define the evidence, boundary and acceptance test together.

    Your team supplies process authority. NeoBram supplies AI architecture, engineering, evaluation and operating handover.

    Plan the first project