| What it does | Search, visualize, curate, and label multimodal data for physical AI models
| An API that verifies each RAG response against its sources
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| Stage | Established
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| Founded | 2018
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| Based in | US
| FR
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| Pricing model | Paid
| Contact for pricing
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| Pricing | Not given
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| Key features | - Natural language search across datasets
- Synchronized multimodal playback with video, 3D, sensors, and maps
- Automated annotation quality assurance
- VLM-assisted labeling and active learning
- Agentic data work automation
- Model evaluation and failure pattern detection
- Dataset versioning and comparison
- Support for billions of samples
- Role-based access controls and SSO
| - Response verification against source documents
- Confidence levels: certain, probable, or I don't know
- Nightly automatic improvements with proof
- REST API with coming Python and TypeScript SDKs
- LangChain and LlamaIndex integration support
- MCP server integration option
- Data hosted in Paris, Europe
- Full verification in French and English
- Graceful degradation if API fails
- User data never used for AI training
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| What makes it different | - Unified platform keeps data iteration continuous across the entire workflow
- Search and curate across multimodal data types in one place
- Agentic workflows automate repetitive data tasks
- Built on open source FiftyOne toolkit
- Deployed in regulated industries with full compliance and governance controls
| - Honest "I don't know" instead of hallucinated answers
- Proven improvements: 31 out of 32 correct on sealed test set
- Drops in after your existing RAG: one API call only
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 70 ● no change since last check | Not checked yet ● no change since last check |
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| Trust Flow (Majestic) | 23 | 0 |
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| Citation Flow (Majestic) | 47 | 0 |
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| Referring domains (Majestic) | 1128 | 0 |
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