Show exactly where every
AI answer came from.
One API call takes your document, the question, and the answer your AI gave — and highlights exactly the tokens behind each part of it.
Northwind Cloud — Master Service Agreement
§8.1 Cure period
A party in breach of any material term has thirty (30) days from written notice to cure the breach.
§11.3 Termination for convenience
Either party may terminate this Agreement on thirty (30) days written notice to the other party.
How long do we get to fix a breach, and how much notice to walk away?
You have thirty (30) days to cure a breach, and terminating also takes thirty (30) days notice.
What people build with it
Every highlight below is real output from the API, with the confidence it returned. Hover any highlighted phrase to see what it resolved to.
Chat with your documents
Ask a question about a contract, a filing, or a policy. Every fact in the answer links to the exact tokens it came from, so a person can check it in a second instead of reading forty pages.
Try it on a real contractWhat uptime does the enterprise tier get?
Enterprise is held to 99.99% monthly uptime.
Is that support available at weekends?
Yes — enterprise cover runs seven days a week.
Northwind Cloud — Service Level Schedule
§4.1 Availability and support
| Tier | Monthly uptime | Support hours |
|---|---|---|
| Standard | 99.5% | Business hours |
| Pro | 99.9% | 24 x 5 |
| Enterprise | 99.99% | 24 x 7 |
§4.2 Service credits. If monthly uptime falls below the committed target, the Customer accrues credits equal to 10% of the monthly fee.
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Support and internal help bots
Your bot answers from your own help center or wiki. Show the exact tokens it used, so your team can trust the answer and your customers can see where it came from.
See a grounded chatCan I get a refund 40 days in?
Not on a one-off purchase — that only covers the first month. On an annual plan you get back the unused part, returned to the card you originally paid with.
help-center/billing.md
One-off purchases can be refunded within 30 days of purchase. Annual plans are prorated to the day on cancellation. Refunds are issued to the original payment method.
Search and research tools
Right now your citation points at a whole page and the reader has to go hunting. Point it at the exact tokens instead.
See web search with citationsWhat happens if I go over the rate limit on the free tier?
Short spikes to double the per-minute rate are tolerated for ten seconds; past that the limiter starts refusing calls.
Rate limits and quotas
Free tier. Each API key may issue 1,000 requests per minute. Bursts of up to 2,000 requests are permitted for 10 seconds before the limiter rejects further calls with HTTP 429.
API availability history
Hitting 429s on the free tier
One endpoint. No SDK to install.
Send the document, the question, the answer, and which parts of the answer you want sourced. You get back the exact tokens behind each one and how confident the match is — enough to highlight it, cite it, or log it in a few lines. Want the raw token-by-token map instead? That's /v1/attributions/heatmap. Rate limits, usage metering, and key management come with the platform.
# one call — the source behind each part of the answer curl https://api.tokenpath.ai/v1/attributions \ -H "Authorization: Bearer $TOKENPATH_API_KEY" \ -d '{ "document": "In Q3, Northwind's revenue grew 18%…", "question": "How fast did revenue grow?", "answer": "Revenue grew 18% year over year.", "spans": [[13, 16]] # the part you want sourced: "18%" }'
# where in the document it came from { "spans": [{ "answer": { "start": 13, "end": 16, "text": "18%" }, "source": { "start": 32, "end": 35, "text": "18%", "confidence": 0.82 # how sure we are of the match } }] }
Under the hood, TokenPath measures attention over your document and rolls it up into spans — no prompting, no second answer to generate. The method, with real attention maps, is in How TokenPath works.
Why not just ask the model to cite itself?
You can. Here's where it breaks.
Ask the model
Have the LLM footnote itself. Straightforward, no extra infrastructure.
Too coarse. Points at a few numbered sources at best — never the exact tokens inside a long document, so the reader still has to go hunting.
Retrieve and re-rank
Pull the relevant chunks and re-rank them with an embedding model — the standard RAG path, and it scales.
Confuses duplicates. When the same fact, number, or date appears in two places, it can't tell which one the answer used — common in tables and financial docs.
Measure the attention
Run the document and the answer through an attribution model and read which tokens the answer leaned on. Nothing to prompt, nothing to trust.
Exact. Lands on the exact tokens — the right cell, the right clause — down to the character.
Never confused. Points at the specific occurrence the answer used, even when the same value appears a dozen times.
Fast. One extra read of the document — no second answer to generate, no reranker round-trips. A 20,000-token document (roughly 30 pages) comes back in under two seconds, and it scales to 128k tokens.
Frontier-grade citations, without the frontier bill.
On LongBench-Cite — one shared answer, one judge — reading attention post-hoc scores 0.815 F1: it matches Anthropic's Citations API (0.812) and lands within 0.04 of a prompted frontier LLM (0.851), while doing it ~7× cheaper and ~5–6× faster— on any model's answer, without regenerating it.
Read the full benchmarkStart with 10 million free tokens.
No card required, no monthly minimum. One endpoint, any model, a dollar per million tokens after that. Why we're building this.
Talk to us
15–30 min — bring your stack, we'll show you attribution on your own docs.