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Digital Asset Management

Asset metadata suggested by AI, governed by confidence

AI proposes tags and asset classes with a confidence score. You decide what is applied automatically, what needs review and what is discarded.

Walk through it All capabilities

How it works

Tune thresholds for a hydraulic fitting photo

Move the thresholds to change which suggestions are applied, reviewed or discarded. Accept or reject reviewed tags and correct an applied one.

Applied2

  • Hydraulic fitting96%
  • Brass91%

Needs review3

  • JIC 37 degree84%
  • Studio photo88%
  • Elbow 9072%

Discarded2

  • Stainless41%
  • Plumbing33%

The capability

Tagging at the speed of upload, with a person in the loop

Manual tagging rarely keeps up with the volume of photography, drawings and documents a product organization produces. Untagged assets become unsearchable, and inconsistent tagging makes filters unreliable.

Autonera analyses images and documents and suggests tags, asset types and product references, each with a confidence score. Thresholds decide what happens: high-confidence suggestions can be applied automatically, mid-range suggestions go to a review queue and low-confidence suggestions are discarded. Tags are constrained to controlled vocabularies so AI cannot invent new terms.

Every correction is recorded. Rejections and accepted reviews provide feedback for improving suggestions, and the history shows which metadata came from a model and which from a person.

Functional capabilities

What AI Asset Tagging & Classification includes

Tag and class suggestions
Propose tags, asset types and subject categories from image and document content.
Confidence thresholds
Configure auto-apply, review and discard bands per vocabulary.
Controlled vocabularies
Restrict suggestions to approved terms.
Review queues
Accept or reject mid-confidence suggestions individually or in bulk.
Duplicate detection
Flag visually similar or identical assets.
Correction history
Record model suggestions, decisions and reviewers.

Problems it solves

Where this work usually breaks down

01

Untagged uploads

Assets added in bulk never receive metadata and cannot be found.

02

Inconsistent vocabularies

Different teams tag the same thing in different ways.

03

Uncontrolled AI output

Free-text AI tags pollute the vocabulary with unreviewed terms.

04

No provenance

Nobody can tell whether a tag was set by a model or a person.

Autonomous operations

How autonomous execution changes ai asset tagging & classification

Agents tag continuously as assets arrive, while thresholds define where human review is required.

Agents carry out

  • Suggest tags and types for new assets
  • Apply high-confidence tags
  • Queue uncertain suggestions
  • Flag likely duplicates

People decide

  • Set thresholds per vocabulary
  • Review uncertain suggestions
  • Maintain the controlled vocabulary

Evidence recorded

  • Model suggestion and confidence
  • Reviewer decision
  • Corrections over time

In industrial operations

A fluid-power distributor tags 40,000 legacy images

Images imported from supplier portals had file names but no metadata, making them invisible to search and completeness rules.

  1. Suggestions are generated against the fitting and material vocabularies.
  2. Tags above 90% are applied automatically.
  3. Asset librarians review the mid-confidence band.
  4. Corrections refine later suggestions.

Result: Legacy images become searchable with metadata whose origin is recorded.

Related capabilities

Works alongside

Feature guide

Digital Asset Library

Images, drawings, video and documents searchable by product context.

Feature guide

AI Product Classification

Ranked class suggestions with evidence and margin-aware confidence.

Feature guide

Reference Data Management

Governed code lists, controlled vocabularies and cross-system crosswalks.

Feature guide

Product Asset Relationships

Asset roles linked to families and variants with channel scope and inheritance.

Capability area

Digital Asset Management

See where AI Asset Tagging & Classification fits in the wider Digital Asset Management capability.

Buyer FAQs

Questions evaluators ask

Can AI create new tags?

No. Suggestions are restricted to controlled vocabularies; new terms are added by vocabulary owners.

How is this different from AI Product Classification?

Asset tagging classifies media files. AI Product Classification assigns products to taxonomy classes.

Can we turn off automatic application?

Yes. Set the auto-apply threshold to 100% and every suggestion goes to review.

Are corrections used to improve suggestions?

Corrections are recorded as feedback. How feedback is used for model improvement is confirmed during evaluation.

Can we tell which tags came from AI?

Yes. Each tag records whether it was suggested by a model, accepted by a reviewer or set manually.

Your next step

See AI Asset Tagging & Classification on data like yours.

Bring a real file, catalog or record set. We will walk through how Autonera would handle it and where your team keeps the final say.

Book a Demo