Illustrative case study
MediaTech & AdTech

Searchable Media and Campaign Intelligence

A publisher or marketing team manages videos, audio, campaign assets, and performance reports.

Illustrative case study: a technically detailed example of how we approach this type of project. It is not a report of a completed client engagement, and no results are claimed.

01Challenge

A publisher or marketing team manages videos, audio, campaign assets, and performance reports. Staff spend time creating transcripts, tagging content, finding reusable assets, and preparing campaign summaries. Metadata is inconsistent and performance data is spread across platforms.

02Why it was difficult

  • Media differs in duration, quality, language, and format.
  • Transcription can fail on names, accents, specialist vocabulary, or overlapping speech.
  • Campaign metrics use different definitions and attribution windows.
  • Rights, privacy, moderation, and brand requirements vary by asset.

03Solution architecture

Create a searchable media index using transcription, language detection, metadata extraction, and semantic search. A separate reporting pipeline normalizes campaign metrics and drafts summaries linked to source reports. Generated metadata remains editable and sensitive assets are reviewed.

  • Ingest approved assets and metadata.
  • Generate timestamped transcripts and subtitle drafts.
  • Extract topics, entities, language, and content descriptors.
  • Index text and metadata for permission-aware keyword and semantic search.
  • Support searches by concept, quote, speaker, or campaign.
  • Normalize campaign definitions and create source-linked summaries with dates and caveats.

04Implementation and validation

Measure transcription error, metadata accuracy, search success in task-based tests, and time to locate assets. Reconcile campaign totals with source systems and test missing data, attribution windows, and changing metric definitions.

05Outcomes we measure

  • Time to find a relevant asset.
  • Transcription and metadata error rates.
  • Acceptance/edit rate for generated tags.
  • Time spent on recurring reports.
  • Discrepancies found during reconciliation.
Intended outcome (illustrative, not a verified result): Media became easier to search and repetitive preparation work was reduced through source-linked metadata and reporting drafts.

Key lesson

Fast generation is insufficient unless teams can verify context, rights, and performance figures.

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