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Media management and collaboration · AWS

Find the moment. Build the clip. Get it approved.

A workspace designed for organizations with large video libraries. Teams upload assets, find spoken or visual moments, collect clips, and coordinate reviews.

A possible implementation uses SvelteKit · Go APIs · Python processing functions · DynamoDB. Illustrative example, not a client engagement.

Sample workspace · Media
Sample recording · 00:00 / 02:00

    Collection · Saved clips (0)

    • No clips yet.

    Who it serves

    • ▸Editors and producers searching archives
    • ▸Reviewers approving selected clips
    • ▸Administrators managing access and asset policies

    The operational challenge

    Hours of recordings sit in folders, findable only by filename. Reviews happen in email threads with timecodes typed by hand.

    A guided product journey

    1. 01

      A large library of recordings

      Collections, tags, permissions, and processing status in one library.

    2. 02

      Search a phrase or topic

      Results show matching text, thumbnails, and timestamps.

    3. 03

      Open at the matching moment

      The video workspace jumps to the timestamp, with transcript following playback.

    4. 04

      Select a range and save

      The clip builder captures start and end points, a title, and a draft.

    5. 05

      Comment at a timestamp, approve

      Reviewers comment on moments, not in a generic thread, and set approval state.

    Capabilities

    Media library

    Collections, tags, permissions, processing status

    Video workspace

    Player, transcript, scene timeline, metadata

    Search

    Matching text, thumbnails, timestamps

    Clip builder

    Start and end points, title, save a draft

    Review

    Time-stamped comments and approval state

    Processing view

    Upload, transcription, analysis, ready, error

    Administration

    Team access and asset policies

    Applied AI

    • ▸Speech transcription with timestamps.
    • ▸Visual scene and object labels.
    • ▸Searchable moments assembled from transcripts and metadata.
    • ▸Draft titles and summaries for selected clips.

    AI suggestions are marked as suggestions. Nothing becomes a title, label, or approved clip until a person accepts it.

    How the system is designed

    Experience

    • SvelteKit frontend

    Services

    • Go APIs
    • AWS Step Functions
    • Python processing functions

    Data & AI

    • DynamoDB (metadata, reviews)
    • Amazon S3 (media)
    • Transcribe · Rekognition Video · Bedrock
    • ▸Media analysis is asynchronous, orchestrated by Step Functions so jobs can retry and report status.
    • ▸Python functions normalize service output into one searchable moment format.
    • ▸DynamoDB suits the access patterns: asset lookups and time-anchored comments.

    Where people stay in control

    • ▸Team and asset-level permissions
    • ▸Suggestions visually distinct from accepted content
    • ▸Approval state tracked per clip

    Managing a large media library? Let's talk.

    Share the problem at a level you are comfortable with. We can discuss sensitive details in a direct conversation.

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