Alternatives
Products that do what PageIndex does
Review SEC filings with cited answers and reusable reports
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Not all improvements come from adding complexity — sometimes it's about removing it. PageIndex takes a different approach to RAG. Instead of relying on vector databases or artificial chunking, it builds a hierarchical tree structure from documents and uses reasoning-based tree search to locate the most relevant sections. This mirrors how humans approach reading: navigating through sections and context rather than matching embeddings. As a result, the retrieval feels transparent, structured, and explainable. It moves RAG away from approximate "semantic vibes" and toward explicit reasoning…
2025 · github.com
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(* within a few minutes of SEC filing) Currently does it for 1000+ US companies and specifically earnings related filings. By US companies, I mean the ones that are obliged to file SEC filings. This was the result of almost a year long effort and hundreds of prototypes :) It currently auto-publishes for 1000 ish US companies by market cap, relies on 8-K filing as a trigger. e.g. https://www.signalbloom.ai/news/NVDA will take you to NVDA earnings Would be grateful to get some feedback. Especially if you follow a company, check its reports out. Thank you! Some examples:…
2025 · signalbloom.ai
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I built Site Spy after missing a visa appointment slot because a government page changed and I didn’t notice for two weeks. It watches webpages for changes and shows the result like a diff. The part I think HN might find interesting is that it can monitor a specific element on a page, not just the whole page, and it can expose changes as RSS feeds. So instead of tracking an entire noisy page, you can watch just a price, a stock status, a headline, or a specific content block. When it changes, you can inspect the diff, browse the snapshot history, or follow the updates in an RSS reader. It’s…
Mar 2026 · sitespy.app
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2018 · github.com
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The word "index" originally came from how humans retrieve info: book indexes and tables of contents that guide us to the right place in documents. Computers later borrowed the term for data structures: e.g., B-trees, hash tables, and more recently, vector indexes. They are highly efficient for machines; but abstract and unnatural: not something a human, or an LLM, can understand and directly use as a reasoning aid. This creates a gap between how indexes work for computers and how they should work for models that reason like humans. PageIndex is a new step that "looks back to move forward".…
Oct 2025 · github.com
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My main motivation was that I wanted to be able to drill down and filter across all the available stocks, look at the data for myself, and narrow down on the stocks I am interested based on my own sets of criteria, and make data-driven analysis for my personal investment strategies. I used PostgreSQL as the backend database for ELT data pipelines, and used Citus Data cstore_fdw for columnar compression for the final dataset. All financial data is coming from SEC Edgar, https://www.sec.gov/developer. I used Python for downloading most of the data. I also run the data load…
2023 · tesseractanalytics.ai
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Not all improvements come from adding complexity — sometimes it's about removing it. PageIndex takes a different approach to RAG. Instead of relying on vector databases or artificial chunking, it builds a hierarchical tree structure from documents and uses reasoning-based tree search to locate the most relevant sections. This mirrors how humans actually read: navigating through sections and context rather than relying on embedding similarity. As a result, the retrieval feels transparent, structured, and explainable. It moves RAG away from approximate "semantic vibes" and toward explicit…
2025 · github.com
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2025 · publicview.ai
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We built PageIndex, a document indexing system that turns documents into hierarchical search trees to support reasoning-based RAG. Traditional vector-based RAG often struggles with retrieval accuracy because it optimizes for similarity, not relevance. But what we really need in retrieval is relevance — which requires reasoning. When working with professional documents that demand domain expertise and multi-step reasoning, vector-based RAG and similarity search often fall short. So we started exploring a more reasoning-driven approach to RAG. Reasoning-based RAG enables LLMs to think and…
2025 · github.com
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There's a lot of paid search engine indexation tools out there and I wanted to create my own. Been working as an engineer for over a decade now and my open-source contribution has always been something I wanted to do. Thus I decided to create FastIndex, an open-source search engine indexation alternative to paid solutions such as TagParrot, URLMonitor, Omega Indexer and many more. Source: https://github.com/maurocasas/fastindex Wiki: https://github.com/maurocasas/fastindex/wiki
2024 · github.com
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Hi HN, I built a website myfinsight.com that aims to make complicated company financials easy to understand. The problem: The go-to place for financial data such as revenue, sales, net income is Yahoo finance. However, their data is usually wrong and very limited. The numbers are hard to digest to get insight quickly. There are also numerous websites that provide much better data for a very expensive monthly fee. Solution: a website that provides free diagrams and charts that visualize important financial data, such as income growth rate by date, revenue breakdown etc. It is free because the…
Nov 2025 · myfinsight.com
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RAG pipelines have become bloated: embeddings, vector DBs, rerankers, and ad-hoc pipelines everywhere. Projects like Claude Code showed a simpler path: In-Context Retrieval — letting the LLM reason directly over context for retrieval instead of outsourcing search to external infrastructure. PageIndex takes that one step further with In-Context Indexing. If retrieval happens in-context, the index should live there too. Each document is transformed into a hierarchical, human-readable tree structure (like a table-of-contents tree index) inside the model's context window. The LLM reads the…
Oct 2025 · github.com
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In this blog, we introduce a pure JSON index to enable reasoning-based RAG without relying on any Vector DBs. Any feedback is welcome!
Oct 2025 · vectifyai.notion.site
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2025 · pageindex.ai
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