| What it does | Encoding Human Expertise into Machines
| Citations, built for AI agents
|
|---|
| Stage | Not given
| Not given
|
|---|
| Founded | Not given
| Not given
|
|---|
| Based in | US
| Not given
|
|---|
| Pricing model | Not given
| Freemium
|
|---|
| Pricing | Not given
| Free tier for small requests with no API key required; 10 million free tokens to start; $1 per million tokens for pay-as-you-go, with discounts available at volume or with annual commitments; failed requests are never charged.
|
|---|
| 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
| - Token-level attribution with confidence scores
- Works with any AI model without access to weights or internals
- Processes documents up to 1 million tokens
- Returns results in under 2 seconds for 20,000-token documents
- On-premise and air-gapped deployment options
- PDF support with character-level mapping
- Supports 23+ languages including Indian languages
- Chrome browser extension for page and PDF tracing
- Raw token-by-token heatmaps available via API
- No data logging or training on request content
|
|---|
| 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
| - Measures attention directly from the model's computation rather than asking the model to cite itself, providing verifiable attribution
- Achieves frontier-grade citation quality at 7x lower cost and 5-6x faster than alternatives
- Never confused by duplicate facts—identifies the exact occurrence the answer used even when values appear multiple times
- Lands on exact tokens down to character level, not just document chunks
- Works on any model's output post-generation without modifying inference pipeline
|
| Community votes | 1 | 0 |
| DR (Domain Rating by Ahrefs) | 27 ▼ −1 (down) since last check | 2 ● no change since last check |
| Trust Flow (Majestic) | 4 | 3 |
| Citation Flow (Majestic) | 32 | 16 |
| Referring domains (Majestic) | 264 | 36 |