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PageIndex
For knowledge workers: PageIndex searches your document set and cites exact sources so you verify answers in seconds PageIndex keeps the workflow clear so you ship outcomes with less busywork.
Built by Cathy, Yuexun Jiang, Yu Tang and 2 others
Visit website → Upvotes · 356
PageIndex is a Product Hunt listing on Launch Llama with 356 total upvotes, founded by Cathy, Yuexun Jiang, Yu Tang and 2 others who joined in Aug 2026, compared against 6 alternatives.
Product Hunt listing
Listing status
356upvotes
Community score
Cathy, Yuexun Jiang, Yu Tang and 2 others (Joined Aug 2026)
Founder
Compared against 6 alternatives
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Key Features
- Retrieve answers from documents without vector databases
- Show cited source lines for verification
- Explain how answers were found in documents
About
PageIndex gives you accurate, trustworthy answers across long, professional documents your work depends on. Bring in your entire document set, ask your hardest question, and click any citation to jump to the exact highlighted source line, so you can verify it in seconds.
Use Cases
- Financial analysts seeking accurate answers from lengthy financial documents
- Users who need to verify answers by checking original source text
Pricing
See the PageIndex website for current pricing.
FAQ
What is PageIndex?
PageIndex is a vectorless, reasoning-based RAG (Retrieval-Augmented Generation) engine that mirrors how humans read documents. It delivers traceable, explainable, and context-aware retrieval without vector databases or chunking.
How is PageIndex different from vector databases?
Unlike vector databases that rely on semantic similarity matching, PageIndex uses logical reasoning to understand and retrieve information from documents. This approach provides exact page references and explainable retrieval decisions, making it ideal for domain-specific documents where accuracy is critical.
What accuracy does PageIndex achieve?
PageIndex achieves 98.7% accuracy on the FinanceBench benchmark, the highest in the market. This benchmark tests RAG systems on complex financial document analysis tasks.
Does PageIndex require chunking documents?
No, PageIndex does not require chunking. Traditional RAG systems split documents into chunks for vector embedding, which can lose context. PageIndex processes documents holistically using reasoning-based retrieval.
What types of documents is PageIndex best for?
PageIndex is best for domain-specific document analysis including financial reports and SEC filings, regulatory and compliance documents, healthcare and medical reports, legal contracts and case law, and technical manuals and scientific documentation.
Founder Guides
Alternatives
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For SEO teams: BoastIndex alerts you when pages drop from Google's index so you catch deindexing issues before organic traffic and revenue decline.
- noindexed
For SEO managers: noindexed monitors key pages and alerts you within minutes when indexing breaks so you fix issues before rankings drop.
- IndexEZ
For SEO professionals: IndexEZ indexes your backlinks in hours so you prove campaign results to clients faster and stop wasting budget on undiscovered links.
- Bookmarx
For knowledge workers: Bookmarx resurfaces and organizes your bookmarks with AI so you find what you saved and understand it faster.
- Instant Index Now
For SEO professionals: Instant Index Now submits URLs to Bing, Yandex, Naver, and more via IndexNow so your pages index faster without manual verification.
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For agents
llms.txt · llms-full.txt · ai.txt · Live fact sheet · Full catalog (.md) · Endpoint index · API spec · REST access · Agent server · Server manifest · Server discovery