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TokenPath
Token-level citation API that reads attention to show exactly which source tokens produced each part of an LLM answer
0Visit TokenPath
https://tokenpath.ai
About TokenPath
TokenPath is a citation API that shows exactly which tokens in a source document produced each part of an AI-generated answer, reading the attention rather than asking a model to guess at attribution afterward. One API call takes three inputs — your document, the question, and the answer your AI gave — and returns highlighted spans mapping each claim back to the specific tokens behind it, with a confidence score. It is model-agnostic and runs after generation, so nothing about your existing pipeline changes: it works with OpenAI, Anthropic, or your own models, and there is no SDK to install. The examples on their site show why token-level matters more than page-level. Two clauses in a contract both read 'thirty (30) days' but mean different things, and TokenPath resolves each mention to the correct clause. A question about weekend support resolves 'seven days a week' onto the '7' inside a table cell reading '24 x 7'. A refund answer saying 'the first month' lands on '30 days' — not a word in common between the answer phrase and the source text. Three use cases are called out: chatting with documents such as contracts, filings, and policies where a human needs to verify a claim in seconds instead of reading forty pages; support and internal help bots grounding answers in your own help center or wiki; and search and research tools that currently cite a whole page and leave the reader hunting. Pricing is $1 per million tokens pay-as-you-go with 10M tokens free to start.
Key Features
TokenPath Pros & Cons
✅ Pros
- +Token-level precision instead of page- or chunk-level citations
- +Drops in after generation without touching your existing stack
- +10M free tokens is a real evaluation budget
- +Confidence scores let you gate what you surface to users
⚠️ Cons
- −Adds a second API call and latency to every answer
- −Attention-based attribution is a heuristic, not a proof of causation
- −Narrow use case — only matters if citations are core to your product
- −Very early with a small public track record
Who Is TokenPath Best For?
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