Cleanroom Compliance

Cleanrooms in pharmaceutical manufacturing, semiconductor fabrication, and biotech facilities follow strict gowning and procedural protocols. A single gowning violation can contaminate a production batch worth millions. Compliance audits happen periodically, but violations between audits go undetected.

Datature Vi trains a model on your cleanroom camera feeds. You label frames showing correct gowning (full bunny suit, gloves, mask, booties) and frames showing violations (exposed hair, missing gloves, improperly sealed gown). The model monitors feeds continuously and flags deviations the moment they occur.

For regulated environments, this provides a continuous audit trail. The model generates timestamped records of compliance state, which your quality team can review alongside batch records.

For an interactive overview of this application, visit the cleanroom compliance use case on vi.datature.com.

Common applications

Choose your task type

Annotation examples

Deploy and test

from vi.inference import ViModel

model = ViModel(
    run_id="your-run-id",
    secret_key=".your-secret-key.",
    organization_id="your-organization-id",
)

result, error = model(
    source="cleanroom_frame.jpg",
    user_prompt="Is every worker in this frame properly gowned for cleanroom entry?"
)

if error is None:
    print(result.result.answer)

Training tips

Use your facility's own gowning protocol: different cleanroom classifications (ISO 5, ISO 7, etc.) have different PPE requirements. Train on images that reflect your specific protocol.

Include partial compliance: the most common violations are subtle (exposed wrists, unsealed neck, mask below the nose). Include these edge cases prominently in your training data.

Cover all camera positions: gowning looks different from overhead cameras versus wall-mounted cameras. Include frames from every camera in your cleanroom.

Label with your SOP references: if your annotations reference specific SOP clauses ("SOP-201 section 4.2: gloves must overlap gown cuff"), the model learns the vocabulary your quality team uses.

Next steps