AI Media Asset Management

Lecen AI Media Asset Management

Content indexing and time-based discovery for media production.

Lecen AI Media Asset Management organizes video, audio and images for cataloguing, search and reuse. Text, people, scenes and other content clues are linked to source media and relevant time positions. Teams combine keywords, semantic queries and metadata filters to find and preview useful material.

Product brand: Lecen

Lecen AI Media Asset Management workflow illustration
Workflow illustration; not a product screenshot, wiring diagram or measured result.
Media
Video / audio / images
Discovery
Keywords / semantics / filters
Results
Source files and segments

Overview

Search what media contains, not only where it is stored

A directory locates a file but rarely answers where an interview statement or particular scene occurs. Lecen AI MAM relates descriptive metadata to source files, segment timing and editorial cataloguing. Discovery extends from files to relevant moments for news research, programme selection, course reference and archive reuse.

In focus

Capabilities and production benefits

Multimodal content cataloguing

Organize speech transcripts, visible text, people and scene descriptions as searchable clues linked to the source. Summaries and labels explain content while time references lead back to the relevant moment.

Keyword and semantic discovery

Use keywords for exact expressions and semantic queries for scenes or meaning, then narrow results with metadata and categories. This supports selection when the filename or exact wording is unknown.

Time-linked segment location

Associate video and audio matches with time ranges, preview the surrounding context and locate a usable excerpt. Image results point to their source files, preserving an appropriate path for each media type.

Editorial cataloguing and correction

Apply consistent names for people, projects, scenes and business topics. Human review makes descriptions reflect the organization’s vocabulary and established catalogue practices.

Storage and production integration

Keep media in existing or configured locations and manage the index-to-file relationship. Shared paths and selection workflows connect discovered content with production, reducing repeated locating and hand-off work.

Workflow

From media connection to segment reuse

Keep content indexes associated with their files so search results can be verified against the source and used in daily selection.

  1. Connect media

    Define media types, shared paths, directories and analysis scope.

  2. Analyse and catalogue

    Create the selected analysis results and organize them with business categories and editorial labels.

  3. Search and locate

    Use keywords, semantic queries and filters to find media and matching time ranges.

  4. Preview and reuse

    Review the match in context and use its source through the production workflow.

Integration

Library deployment and business configuration

Plan around source locations, analysis tasks, search questions and user roles, with separate management and backup for indexes and originals.

Lecen AI Media Asset Management workflow illustration
Workflow illustration; not a product screenshot, wiring diagram or measured result.

Inventory the library

Record media types, file counts, duration, capacity and shared directories, with permissions and incoming sources.

Configure cataloguing and search

Define people, projects and content categories against business needs, selected analysis and common queries.

Size processing and storage

Plan analysis resources, indexes and media-reading paths around duration, turnaround and concurrent use.

Verify the workflow

Test real queries, previews and source locations alongside permissions, production hand-off and model-service data paths.

Configuration

Scope and configuration

Lecen AI Media Asset Management — Scope and configuration
Product nameLecen AI Media Asset Management
Media typesVideo, audio and images
Information modelTechnical metadata, text, content labels and segment timing
DiscoveryKeywords, semantic queries and metadata filtering
Result usePreview, context review and source location
CataloguingAI analysis combined with editorial labels and correction
Storage integrationDeliberate management of media paths and content indexes
DeploymentSized for library scale, analysis turnaround and access roles

Applications

Production applications

Broadcast and news libraries

Find interviews, locations and programme excerpts using content clues rather than relying on remembered filenames.

Corporate and education libraries

Organize events, classes, meetings and demonstrations into a library that different teams can search repeatedly.

Production and cultural archives

Add searchable descriptions to long-held collections while preserving source and version relationships.

Application and workflow visuals

Media organization, review and archive workspace concept
Media organization, review and archive workspace concept; illustrates the application context.AI-generated concept, not a product test, software screenshot or customer project.

Engineering decisions

Define the configuration. Validate the workflow.

Inventory media and real search tasks

Provide video, audio and image counts, total duration, daily growth, languages and existing metadata. Collect actual editorial search questions. Specify keyword, semantic, person-related and on-screen-text requirements separately, confirming each against the proposed release rather than assuming every analysis module is available.

Define sources, proxies and indexes

Agree asset identifiers, paths, versions, segment boundaries and proxy relationships, including what should happen after moves, renames and deletion. Distinguish file-relative time from programme timecode. Metadata standards can inform field design without establishing implemented standard import or export.

Plan permissions and analysis turnaround

Identify who may search, preview, download and edit metadata, and how analysis workers access originals. Size initial indexing separately from daily additions, including compute, proxy space and storage reads. Confirm local deployment, external model calls, auditing and single sign-on individually.

Suggested validation and handover checks

  1. Evaluate search with editorial questions

    Create manually reviewed queries and relevant excerpts, including paraphrases, noisy speech, subtitles, duplicates and questions with no answer. Record top-result relevance, missed material, segment timing and selection effort separately. A successful demonstration query is not an overall accuracy result.

  2. Check access across the entire workflow

    Use different roles to test search results, thumbnails, previews and original files. Check that restricted material is not exposed through summaries or caches. Where APIs, auditing or synchronized deletion are required, test those paths explicitly rather than checking only login and folder permissions.

  3. Verify updates and recovery

    Add, move, rename, delete and restore media in a test library, checking the agreed source-to-index reconnection procedure. Validate recovery requirements for editorial corrections, proxies, database and configuration. Record failed jobs and reprocessing procedures, and deliver a field dictionary, role matrix and tested versions.

FAQ

Common questions

How does AI MAM differ from folder-based management?

Folders organize files by path. AI MAM adds searchable descriptions and segment timing so users can search interviews, scenes or business topics within the media.

How do keyword and semantic searches work together?

Use keywords for names, terms and exact phrases; semantic queries describe scenes or intent. Media types and catalogue categories further narrow the results.

How is a usable excerpt found from a result?

Video and audio matches link to time positions. Review the surrounding context and locate the source. Image results point to the corresponding file.

How are AI results combined with editorial cataloguing?

Analysis supplies initial clues; staff review and organize them using business names, people and project rules. Important information is checked against the source.

How does it connect to an existing NAS or production network?

Confirm directories, permissions and source paths, then configure analysis and indexing resources. Users locate the required media and access the original through the established production workflow.

Does a search match establish that a clip is ready for publication?

No. Editors still need to review context, people, dates, rights and restrictions. AI summaries and labels are search clues requiring verification, not substitutes for source media or publication permission.

Does an existing NAS remove the need to back up the catalogue database?

No. Originals, proxies, indexes, editorial metadata and configuration are separate recovery objects. Media alone may not preserve cataloguing work, while an index cannot restore missing originals. Test coordinated recovery and establish how long any rebuild will take.

Technical references and supply scope

Confirm the supplied release and deployment configuration of Lecen AI Media Asset Management, with delivered functions supported by acceptance records. EBU, NIST and W3C references inform metadata, permissions and recovery design, not implemented interfaces, complete standards conformance or certification.

These references explain principles and integration conditions. Confirm capabilities against the supplied revision, project configuration and agreed tests.

Project brief

Information for configuration

Media and paths

Describe volume, folders, access rights and update patterns.

Search tasks

Supply real queries and expected clips for evaluation.

Deployment policy

Define local models, external APIs, index backups and access scope.

Talk to Lecen

Build discovery around real production questions

Share library scale, directories and frequent search tasks to plan analysis, segment discovery and production integration.

Discuss AI MAM deployment