AI-Powered Search and Agent Automation for Digital Asset Management

bynder · Media & Entertainment · 2026 · source Agentic: yes · promotional bias: med · source-backed · extracted with prompt 0.5.4 (schema v2)

Generated from extraction.json — do not edit by hand. Every claim cites verbatim source evidence; stated means the quote directly asserts the claim, implied means it supports it indirectly.

Problem

Domain: Digital asset management for retail and CPG

Bynder needed to make large digital asset libraries searchable, governable, and automatable without forcing customers to manually tag every uploaded image, video, document, or audio file.

Constraints:

• the platform is the system of record for enterprise digital content
• manual metadata tagging created a bottleneck for every uploaded image
• AI processing had to support multiple asset types including images, video, audio, and documents
• tenant isolation and search indexing had to be preserved
• agents could suggest changes but should not mutate the asset bank without human approval

Architecture

Bynder extended its AWS microservices architecture with event-driven AI search pipelines and configurable AI agents. Uploads store files in S3, persist metadata in Aurora, and emit asset events. Search pipelines call Bedrock for image embeddings, Transcribe for audio/video text, Rekognition for faces and image text, and OpenSearch for tenant-scoped indexing and KNN retrieval. Agent workflows store user-configured global and task prompts, trigger manually or from SQS automations, use an orchestrator to publish Kafka execution commands, retrieve files and asset context, call Bedrock models with guardrails, and route suggestions through an approval service before applying changes.

Diagram confidence: reconstructed (reconstructed from prose).

flowchart LR
  asset-upload-core["asset-upload-core"] --> s3-asset-storage["s3-asset-storage"]
  asset-upload-core --> aurora-metadata-store["aurora-metadata-store"]
  asset-upload-core --> asset-upload-event["asset-upload-event"]
  asset-upload-event --> similarity-search-processor["similarity-search-processor"]
  asset-upload-event --> transcription-domain["transcription-domain"]
  asset-upload-event --> face-recognition-domain["face-recognition-domain"]
  similarity-search-processor --> opensearch-indexer["opensearch-indexer"]
  transcription-domain --> opensearch-indexer
  face-recognition-domain --> opensearch-indexer
  aurora-metadata-store --> agent-configuration-service["agent-configuration-service"]
  agent-configuration-service --> agent-orchestrator["agent-orchestrator"]
  agent-orchestrator --> agent-execution-workers["agent-execution-workers"]
  agent-execution-workers --> bedrock-models-and-guardrails["bedrock-models-and-guardrails"]
  agent-execution-workers --> approval-service["approval-service"]
  approval-service --> asset-upload-core

Components

Data flow

Users upload assets through the browser, API, or integrations. Bynder stores files in S3, metadata in Aurora, and emits an upload event. AI search domains consume the event: similarity search generates Bedrock embeddings and indexes them in OpenSearch; transcription calls Amazon Transcribe asynchronously through EventBridge/SQS and indexes transcript text; face recognition uses Rekognition and indexes named people. Agent runs start from an asset-bank action or SQS automation, load configuration from Aurora, publish Kafka commands to specialized agents, retrieve the S3 file and asset context, call Bedrock with guardrails, publish suggestions, and require user approval before asset service changes.

Why this architecture

Bynder used event-driven microservices because AI search and agent features operate asynchronously and at different speeds from the core upload path. Independent search domains can index embeddings, transcripts, and faces without blocking uploads. Agents are configurable through prompts and triggerable through UI or automation, but suggestions go through a human approval service so AI can enrich and transform assets without silently changing the asset bank. Bedrock model choice and guardrails let Bynder tune models per use case while keeping enterprise compliance controls in place.

Models used: Amazon Bedrock · Amazon Transcribe · AWS Rekognition · Amazon OpenSearch · Amazon Aurora MySQL · Aurora PostgreSQL · Amazon S3 · Amazon EventBridge · Amazon SQS · Kafka

Scale

• Bynder is described as the enterprise system of record for all digital content.
  “Bynder really is the enterprise system of record for all your digital content.”transcript, stated
• Before AI search, every uploaded image needed manual metadata tagging.
  “every single image that customers upload at Bynder needed to be manually tagged with meta properties.”transcript, stated
• One pet-food retailer saved almost 4,000 hours of tagging/categorization work.
  “one of the world's leading online retailers of pet food has saved almost 4,000 hours”transcript, stated
• A tea-brand customer cut migration time from months to weeks.
  “they have cut migration time from months to weeks and improved metadata quality.”transcript, stated
• Similarity search uses tenant-scoped OpenSearch indexes.
  “store these in OpenSearch associated to to the index of this tenant.”transcript, stated
• Bynder provides four configurable AI agent types.
  “we have at the moment we have four different types of agents.”transcript, stated

Alternatives considered (roads not taken)

lexical/Solr-only asset search

Rejected because: It required large amounts of manual metadata input and created customer bottlenecks.

synchronous transcription processing in the upload path

Rejected because: Large videos can take time to process, so Bynder used asynchronous Transcribe events, EventBridge, and SQS.

agents directly modifying assets

Rejected because: Bynder wanted users to review, discard, or approve suggestions before changes were applied.

Patterns

async-message-orchestration (agent-architecture)

Bynder fans out upload and agent execution work through events, SQS, and Kafka so search and agent domains can process independently.

model-routing (model-operations)

Bynder can pick different Bedrock models depending on tenant or use case.

human-in-the-loop-gate (reliability)

Agent suggestions are stored for user review and only applied to assets after approval.

guardrails-io-filtering (safety)

Bedrock guardrails are used to prevent inappropriate suggestions and information leakage during agent inference.

Tradeoffs

Use separate event-driven AI domains.

Chose loosely coupled processors and indexers over one synchronous upload-and-process path.

Rationale: Independent domains let similarity, transcription, recognition, and agent workflows process at their own pace without blocking uploads.

Make agents configurable with global and task-specific prompts.

Chose brand/industry global instructions plus per-agent user prompts over fixed hard-coded agent behavior.

Rationale: Customers need agents optimized for their own brand, industry terminology, and business task.

Keep human approval before applying agent suggestions.

Chose review, discard, or approve workflow over fully autonomous asset mutation.

Rationale: The approval gate preserves governance for brand-compliance and enrichment changes while still automating suggestion generation.

Gotchas (what bit them)

Lexical search made DAM searchability depend on manual tagging.

Root cause: Solr-only search could not infer visual, spoken, face, or semantic content automatically.

Resolution: Add embedding search, reverse image search, speech-to-text, text-in-image, face recognition, and AI auto-tagging pipelines.

Large media processing cannot be treated like a synchronous request.

Root cause: Transcribing large videos may take significant time.

Resolution: Use asynchronous Transcribe completion events, EventBridge, SQS, and downstream indexer events.

AI agents could damage asset governance if they changed content silently.

Root cause: Brand, legal, enrichment, and transformation agents can suggest changes to production asset metadata or content.

Resolution: Persist suggestions in an approval service and require users to approve or discard them before applying changes.

Evaluation

Bynder reports customer outcome metrics for saved tagging labor and faster migration, plus validates architecture through production usage of tenant-scoped search, transcription, recognition, and configurable agent features.

Metrics: almost 4,000 hours saved by a pet-food retailer · migration time reduced from months to weeks for a tea brand · metadata quality improved · four AI agent types available

Outcomes

• customers can search visually similar assets and reverse-search by external image
• video and audio assets become searchable through generated transcripts
• face recognition, auto tags, text-in-image, and duplicate management reduce manual organization work
• configurable agents support brand compliance, transformations, governance, and enrichment

The strongest reported outcomes are almost 4,000 hours saved on tagging/categorization and migration reduced from months to weeks.