AI Pipeline Building Blocks for Audio, Visual Content
Our goal is to give you all the building blocks you need to achieve your AI goals -> across any workflow, across any department, across any need.
Enterprise scale, AI building blocks
Each AI building block addresses a major component of AI pipelines.
AI-optimized proxy generation for cost-efficient media pipelines
- Purpose-built proxies for AI, not playback
- Generate intelligent audio, video, spritesheet, and scene-detected frame proxies designed specifically to maximize AI signal while minimizing data volume.
- Dramatically lower AI enrichment costs
- Reduce spend across vision, audio, and multimodal AI services by submitting optimized proxies—without materially impacting detection accuracy or enrichment quality.
- Flexible, composable, and AI-pipeline native
- Control resolution, frame density, audio sampling, and scene logic to tailor proxies per AI modality, vendor, or workflow.
Multi-vendor AI enrichment across audio, video, and image modalities
- Connect once, enrich everywhere
- Seamlessly integrate with multiple AI enrichment endpoints across vision, audio, and language—through a single, unified orchestration layer.
- Mix and match AI vendors by modality
- Combine best-of-breed AI services (logos, speech, objects, actions, semantics) across vendors to optimize accuracy, performance, and cost.
- Vendor-agnostic by design
- Avoid lock-in by routing content dynamically across AI providers and modalities as requirements, pricing, or performance evolve.
Open access to AI-enriched metadata across your organization
- Extract once, reuse everywhere
- Comprehensively pulls raw and enriched metadata from every AI provider, endpoint, and modality—normalized and ready for downstream workflows.
- Open, structured, and vendor-neutral
- Stores AI outputs in an open JSON-based metadata layer (MeshDB), ensuring portability and long-term ownership of your enrichment data.
- Power multiple downstream pipelines
- Re-route metadata back into existing MAMs, enable RAG with LLMs, trigger automated sub-clipping, or feed analytics and discovery workflows—without reprocessing media.
Exascale AI metadata fabric for media organizations)
- Built for exabytes, designed for AI
- A multi-dimensional, open database engineered to scale across exabytes of AI-enriched and legacy metadata—without sacrificing performance or flexibility.
- One fabric, multiple data models
- Seamlessly combines JSON, vector, and raw metadata storage to support search, enrichment, reasoning, and analytics across modalities.
- A single source of truth for all metadata
- Maintains normalized, versioned, and vendor-neutral metadata as the authoritative backbone for your entire organization.
- Foundation for next-generation AI workflows
- Powers RAG, automated re-purposing, discovery, and monetization pipelines—future-proofing your media intelligence stack.
Chat based LLM ehnacement
- LLM-powered semantic enrichment for audio & video timelines
- Context-aware enrichment, frame by frame
- AI Sage applies LLM reasoning to time-sliced video and audio, combining existing AI tags, temporal context, and user intent to generate deeper, structured insights.
- Prompt-driven intelligence on top of any AI vendor
- Leverage AI Sage across outputs from multiple vision, audio, and language models—without retraining or vendor lock-in.
- From raw tags to usable understanding
- Transform fragmented AI detections into coherent descriptions, summaries, narratives, metadata, or domain-specific interpretations—directly on the timeline.
Benefits
Why Composable AI Pipelines Matter
DNAfabric’s composable AI architecture is designed for organizations that want control, transparency, and long-term flexibility in how AI is applied to audio and video content. Instead of locking you into a single vendor, modality, or workflow, DNAfabric allows you to assemble, evaluate, and evolve AI pipelines over time—based on real results, not promises.
(01) Legacy + Future Metadata, Unified
Merge legacy metadata, AI-generated enrichments, and LLM-enhanced insights into a single, evolving intelligence layer. This enables richer discovery, better context, and future-ready workflows without discarding existing investments.
(02) True Single Source of Truth
Build an open, normalized metadata foundation that spans all AI vendors, modalities, and enrichment strategies—ensuring consistency across teams, tools, and downstream systems.
(03) No Vendor Lock-In, Ever
All metadata—legacy, AI-enriched, and LLM-generated—is stored in an open, vendor-neutral JSON fabric. You retain full ownership and portability, regardless of how AI models, pricing, or providers change.
(04) Maximize Efficiency, Minimize Cost
Optimize AI spend by choosing the right proxy, modality, and enrichment strategy for each workflow. Reduce unnecessary processing while maintaining meaningful AI signal.
(05) Mix, Match, and Continuously Improve
Combine multiple AI vendors, modalities, and LLMs within the same pipeline. Test, compare, and tune performance over time—selecting the best tool for each task instead of settling for one-size-fits-all AI.
(01)
Legacy + Future Metadata, Unified
Merge legacy metadata, AI-generated enrichments, and LLM-enhanced insights into a single, evolving intelligence layer. This enables richer discovery, better context, and future-ready workflows without discarding existing investments.
(02) True Single Source of Truth
Build an open, normalized metadata foundation that spans all AI vendors, modalities, and enrichment strategies—ensuring consistency across teams, tools, and downstream systems.
(03) No Vendor Lock-In, Ever
All metadata—legacy, AI-enriched, and LLM-generated—is stored in an open, vendor-neutral JSON fabric. You retain full ownership and portability, regardless of how AI models, pricing, or providers change.
(04) Maximize Efficiency, Minimize Cost
Optimize AI spend by choosing the right proxy, modality, and enrichment strategy for each workflow. Reduce unnecessary processing while maintaining meaningful AI signal.
(05) Mix, Match, and Continuously Improve
Combine multiple AI vendors, modalities, and LLMs within the same pipeline. Test, compare, and tune performance over time—selecting the best tool for each task instead of settling for one-size-fits-all AI.
(02)
True Single Source of Truth
Build an open, normalized metadata foundation that spans all AI vendors, modalities, and enrichment strategies—ensuring consistency across teams, tools, and downstream systems.
(03) No Vendor Lock-In, Ever
All metadata—legacy, AI-enriched, and LLM-generated—is stored in an open, vendor-neutral JSON fabric. You retain full ownership and portability, regardless of how AI models, pricing, or providers change.
(04) Maximize Efficiency, Minimize Cost
Optimize AI spend by choosing the right proxy, modality, and enrichment strategy for each workflow. Reduce unnecessary processing while maintaining meaningful AI signal.
(05) Mix, Match, and Continuously Improve
Combine multiple AI vendors, modalities, and LLMs within the same pipeline. Test, compare, and tune performance over time—selecting the best tool for each task instead of settling for one-size-fits-all AI.
(03)
No Vendor Lock-In, Ever
All metadata—legacy, AI-enriched, and LLM-generated—is stored in an open, vendor-neutral JSON fabric. You retain full ownership and portability, regardless of how AI models, pricing, or providers change.
(04) Maximize Efficiency, Minimize Cost
Optimize AI spend by choosing the right proxy, modality, and enrichment strategy for each workflow. Reduce unnecessary processing while maintaining meaningful AI signal.
(05) Mix, Match, and Continuously Improve
Combine multiple AI vendors, modalities, and LLMs within the same pipeline. Test, compare, and tune performance over time—selecting the best tool for each task instead of settling for one-size-fits-all AI.
(04)
Maximize Efficiency, Minimize Cost
Optimize AI spend by choosing the right proxy, modality, and enrichment strategy for each workflow. Reduce unnecessary processing while maintaining meaningful AI signal.
(05) Mix, Match, and Continuously Improve
Combine multiple AI vendors, modalities, and LLMs within the same pipeline. Test, compare, and tune performance over time—selecting the best tool for each task instead of settling for one-size-fits-all AI.
(05)
Mix, Match, and Continuously Improve
Combine multiple AI vendors, modalities, and LLMs within the same pipeline. Test, compare, and tune performance over time—selecting the best tool for each task instead of settling for one-size-fits-all AI.