| What it does | Encoding Human Expertise into Machines
| On-device moderation that flags hateful, abusive and threatening text in 23 European languages, before the message is sent.
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| Stage | Not given
| MVP
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| Founded | Not given
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| Based in | US
| NL
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| Pricing model | Not given
| Freemium
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| Pricing | Not given
| Free up to 100k monthly active devices with unlimited inference. Custom licenses available.
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| Key features | - Converse-STT Benchmark for speech-to-text evaluation
- Converse-Diarization Benchmark for speaker attribution
- Expert network with domain specialists
- Audiovisual conversational datasets
- Multilingual conversational AI datasets
- Open-source model post-training research
| - Three separate scores: hateful, abusive, threat
- Ten target signals under the hate score: race, color, religion, descent, national or ethnic origin, sexual orientation, gender, disability, age, other
- Runs fully on device with no server calls
- Supports 23 European languages from one model file
- 80MB model size optimized for iOS, Android, and browser
- 0.847 macro F1 accuracy on Multilingual HateCheck
- Warn before message is sent
- Comment triage without infrastructure
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| What makes it different | - Real-world conversational data from elite professionals
- Multimodal and multilingual expertise encoding
- Rigorous benchmarking with open-source evaluation tools
- Custom data pipelines for each discipline and accent
- Focus on nuance beyond scale, capturing lived experience
| - Multilingual support across 23 European languages in a single 80MB model
- Complete privacy with no data leaving the device
- Free access up to 100k monthly active devices with unlimited inference
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| Community votes | 1 | 0 |
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| DR (Domain Rating by Ahrefs) | 27 ▼ −1 (down) since last check | 37 ● no change since last check |
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| Trust Flow (Majestic) | 4 | 20 |
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| Citation Flow (Majestic) | 32 | 39 |
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| Referring domains (Majestic) | 264 | 616 |
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Ocular AI
Toxic