TokenPath

TokenPath is an API that adds verifiable citations to AI-generated answers by identifying exactly which tokens in a source document support each part of the response. When your AI produces an answer based on a document, TokenPath traces the connection back to specific character positions in the original text. This isn't the model citing itself through clever prompting or asking for a second pass to generate references. TokenPath measures actual attention patterns within the model to determine what information it relied upon when generating each answer span, then returns precise locations for both the claim and its supporting source material.

The problem TokenPath solves becomes more critical as AI handles increasingly important document-heavy workflows where trust matters. Legal teams asking AI to summarize contracts need to verify that claims about indemnification clauses or payment terms actually appear in the document. Support chatbots referencing help documentation should point users to the exact paragraph that contains the answer, not just link to a general article. Research tools synthesizing information from multiple papers require traceable attribution so readers can evaluate sources themselves. Financial analysts reviewing earnings reports need AI summaries they can fact-check against specific line items. Without proper citations, users face an uncomfortable choice: trust AI output blindly and risk propagating errors, or spend significant time manually verifying every claim. TokenPath automates the verification layer that makes AI output auditable.

The API works through a single endpoint with a straightforward request structure. You send your document content, the user's question, and the AI-generated answer you want to annotate. TokenPath processes the request and returns attribution objects containing confidence scores and exact character positions within both the answer and the source document. The precision extends down to individual tokens, which becomes crucial when documents contain repeated values. Tables, financial statements, and structured data frequently have the same numbers appearing multiple times on a single page. Token-level positioning disambiguates exactly which specific instance the answer references, eliminating the confusion that page-level citations would create when pointing to a value that appears in three different rows of a spreadsheet.

Performance characteristics make TokenPath practical for production deployments handling real user traffic. A 20,000-token document processes in under two seconds, and the system scales to documents up to 128,000 tokens without requiring answer regeneration or multiple API calls. Because TokenPath analyzes attention patterns rather than re-prompting the model to add citations, you're not paying for additional inference time or waiting for another model completion. The citation layer adds minimal overhead to your existing workflow. Pricing runs approximately one dollar per million tokens after the free tier, which the company documents as roughly seven times cheaper than typical retrieval-plus-reranking citation approaches.

TokenPath operates completely independently of which AI model generated the original answer. It works equally well with OpenAI's GPT models, Anthropic's Claude, open-weight models you're running yourself, or proprietary fine-tuned models specific to your organization. This model-agnostic design provides important flexibility. You can upgrade or switch your primary AI provider without rebuilding your citation infrastructure. You can A/B test different models while maintaining consistent citation behavior. The integration point comes after answer generation, so TokenPath slots into existing pipelines without restructuring how you prompt or call your main model. There's no SDK to install or agent framework to adopt. It's a REST API that accepts text and returns JSON.

Benchmark performance on LongBench-Cite, a standardized evaluation of citation accuracy across 480 test examples, shows TokenPath achieving a 0.815 F1 score. This matches Anthropic's Citations API performance of 0.812 and comes within 0.04 of frontier prompted approaches that ask models to generate citations themselves. The latency advantage compounds at scale. At 1.6 seconds average response time compared to 8 to 10 seconds for retrieval-then-rerank alternatives processing 20,000-token documents, teams handling high query volumes save substantial time and compute costs. The per-query cost difference accumulates into meaningful budget differences for products serving thousands of document queries daily.

Getting started requires no credit card and no commitment. The free tier includes 10 million tokens of processing, which provides substantial room to build a prototype, validate the integration against your specific document types, and stress-test performance before making any purchasing decision. Documentation covers integration patterns for common frameworks, a GitHub cookbook provides working code examples, and a platform dashboard offers usage monitoring and API key management. For teams building AI products where user trust matters, where compliance requires audit trails, or where errors have real consequences, TokenPath provides the citation infrastructure that transforms AI responses from plausible-sounding text into verifiable claims grounded in actual source material.