Scale of the problem
A single AI image generator may produce millions of images per day. Each one must be individually signed with provenance metadata — manual approaches are impossible at this scale.
Case study — AI companies
Building compliance into
your AI pipeline
How generative AI companies integrate Capture's multi-layer provenance into their inference pipelines — from image generators to LLMs — to comply with EU AI Act Article 50 without sacrificing performance.
The challenge
Why AI providers are the primary compliance target
Article 50(2) places the marking obligation squarely on providers of AI systems that generate synthetic content. This means if you build or operate a generative AI model — whether for images, video, audio, or text — you are responsible for ensuring every output carries machine-readable provenance marking before it reaches any user.
Performance constraints
Users expect sub-second generation. Any compliance layer must add negligible latency. A signing step that doubles response time is a non-starter for production workloads.
Downstream liability
If your API customers distribute unmarked AI content in the EU, both you (as provider) and they (as deployer) face enforcement. Building compliance into your API output protects the entire chain.
Architecture
Where Capture fits in your pipeline
AI model generates content
Your model (image, video, audio, or text) produces the output as normal. No changes to model architecture, training data, or inference code.
Capture signs the output
A single API call to Capture embeds C2PA credentials (generator identity, timestamp, content hash) and registers the content hash on-chain via ERC-7053. Sub-100ms median latency.
Deliver to user
The signed content is returned to the user or downstream API consumer. The C2PA credentials travel with the file; the on-chain record is independently discoverable.
Audit on demand
The Capture dashboard provides exportable compliance reports showing every signed asset, its on-chain record, and verification status — ready for regulators or third-party auditors.
Use case 1
Image generation API
Scenario
An AI image generation company serves an API that produces 500,000+ images per day across enterprise and consumer tiers. Their customers use the generated images in marketing, e-commerce, and social media — much of which reaches EU audiences.
Integration
The company added Capture's Node.js SDK as a post-processing step in their image delivery pipeline. After the diffusion model generates an image, the SDK signs it with C2PA credentials and registers the hash on-chain — all within a single asynchronous call that does not block the response.
Result
- Every API-generated image carries dual-layer provenance automatically
- Average signing overhead: 47ms per image (P99: 92ms)
- Zero changes to model architecture or training pipeline
- Downstream customers receive Article 50-compliant content by default
Use case 2
Large language model (LLM) output
Scenario
An enterprise AI company runs a large language model that generates reports, summaries, and customer-facing content for Fortune 500 clients. Under Article 50(2), AI-generated text intended for public dissemination must carry machine-readable provenance marking.
Integration
After the LLM assembles a complete response, the text is serialised and signed via Capture's REST API. The C2PA manifest is attached as a sidecar file (for plain text) or embedded in document metadata (for PDF/DOCX outputs). The content hash is simultaneously registered on-chain.
Result
- All generated reports carry verifiable AI-origin credentials
- Enterprise clients receive compliance-ready outputs with no extra integration
- The text exception in Article 50(2) for "assistive" content is clearly documented per asset
- Audit trail shows which outputs were human-reviewed vs. fully AI-generated
Use case 3
Video synthesis and editing
Scenario
An AI video generation platform creates marketing videos, product demos, and social media clips. Article 50(4) specifically covers deepfake-style content — any AI-generated video depicting realistic scenes must carry provenance marking.
Integration
Capture's SDK signs rendered video files (MP4, MOV, WebM) at export time. The C2PA manifest includes the generator model, all editing actions applied, and timestamps. The on-chain record ensures provenance survives video platform re-encoding and social media compression.
Result
- Article 50(4) deepfake labelling satisfied for all generated video
- Editing actions documented in the C2PA manifest chain
- On-chain hash survives platform re-encoding and compression
- Customers can verify the AI-generated nature of any video via hash lookup
Performance
Built for production scale
<100ms
Median signing latency
500K+
Assets signed per day (production)
99.9%
API uptime SLA
1-2 wks
Typical integration timeline
Frequently asked
AI pipeline compliance questions
Does Article 50 apply to all AI companies?
Article 50 applies to providers of AI systems that generate synthetic content (images, video, audio, or text) and to deployers who distribute such content. If your AI system generates content that reaches EU users, you are in scope — regardless of where your company is headquartered.
What if our AI model is used by downstream customers?
As the provider of the AI system, you share the marking obligation with your deployers. The most efficient approach is to build marking into your API output, so every downstream customer automatically receives compliant content. This is exactly what Capture's SDK enables.
Does signing add latency to our inference pipeline?
The Capture signing API has sub-100ms median latency. For image generation pipelines where inference takes 2-30 seconds, the signing overhead is negligible. For text generation, signing happens asynchronously after the response is assembled.
Can we sign text outputs, not just images?
Yes. C2PA supports text content credentials. For LLM outputs, Capture creates a C2PA manifest that embeds the model identity, generation timestamp, and content hash. The ERC-7053 on-chain record provides the durable second layer. This satisfies Article 50(2) for text content.
What about fine-tuned or custom models?
Capture's signing is model-agnostic. Whether you run GPT-4, Llama, Stable Diffusion, or a proprietary model, the signing step happens after generation. The C2PA generator assertion identifies your specific model and version.
How do we handle content that users modify after generation?
Each modification can be signed as a new C2PA action in the manifest chain. The original generation credential remains, and the modification is appended. This creates an auditable edit history that satisfies Article 50 regardless of how many edits occur.
Build compliance into your pipeline
Start a free 30-day proof-of-concept. Integrate multi-layer provenance into your AI inference pipeline before the August 2026 enforcement deadline.