| What it does | Tools and algorithms for AI agents to test, fix and optimize your codebase
| Citations, built for AI agents
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| Stage | Not given
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| Founded | Not given
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| Based in | Not given
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| Pricing model | Free
| Freemium
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| Pricing | Vinv is free and open source under the Apache 2.0 license.
| 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.
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| Key features | - Acceptance test verification before fix deployment
- One-click revert of all changes from a failed fix attempt
| - 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
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| What makes it different | - Provides real runtime evidence to AI agents instead of requiring them to guess or generate test cases
- Runs completely locally with zero code changes needed to the codebase
- Proves fixes work by re-executing against acceptance tests the agent never sees
- Works with any AI coding agent without requiring new subscriptions or API keys
- Uses published scientific methods for each decision, with full math and attribution transparency
| - 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
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 18 ▲ +4 (up) since last check | 2 ● no change since last check |
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| Trust Flow (Majestic) | 0 | 3 |
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| Citation Flow (Majestic) | 13 | 16 |
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| Referring domains (Majestic) | 39 | 36 |
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Vinv
TokenPath