indexing
indexing on Beyond Market Intelligence: a running collection of 5 stories we have gathered and hand-picked because they are worth your time. Every post here touches on indexing in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around indexing, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Closing an Azure OpenAI assistant's retrieval gap didn't take a new identity platform. It took one filter and a narrower assistant.
Recent incidents highlight a critical vulnerability in many Azure OpenAI deployments: retrieval-time entitlement failures. Egiziago Cioffi, CEO of SynSphere Italia, discovered this firsthand when his email assistant, despite passing all evaluations, inadvertently exposed SharePoint content to users lacking proper access. Cioffi’s solution? A simple filter that checks user permissions before content reaches the model. This underscores a vital point: current evaluations often miss these boundary breaches.
Accel-backed Keenable is indexing the web for AI agents
Keenable is emerging from stealth mode with a $26 million seed round, backed by Accel, to fundamentally transform how AI agents access and utilize web data. The company is building a specialized index of the web, designed specifically to empower AI workflows and unlock new levels of intelligence. This focused approach addresses a critical need as AI applications increasingly rely on real-time information. For further insights into the evolving AI landscape, explore our coverage of OpenAI’s Jalapeño chip and its performance benchmarks.
Is KV Cache in a high dimensional vector space? [D]
Recent research suggests the KV cache within large language models isn't a flat data structure, but rather a navigable geometric space where keys reflect learned relationships. This transforms attention mechanisms into similarity searches, allowing for indexing and targeted retrieval—a significant shift from exhaustive scanning. Initial experiments with Qwen3.5-2B demonstrate that geometric routing can reduce KV reads by 16–31× while maintaining accuracy. This highlights a critical engineering challenge: efficiently navigating this space, as relevance clusters within specific neighborhoods.
How I Reproduced BM25, Dense Retrieval, and SPLADE on a 16GB MacBook
Reproducing retrieval baselines—BM25, Dense Retrieval, and SPLADE—on limited hardware presents unique challenges. This practical exploration details the process of implementing these techniques on a 16GB MacBook, outlining the inevitable crashes, critical fixes, and essential score checks vital for building robust Retrieval-Augmented Generation (RAG) systems. Gain insights into real-world implementation hurdles and solutions. For further exploration of optimizing data workflows, consider "Reducing Human Annotation with ML Active Learning."
How to Optimize Vector Search When RAM Gets Too Expensive: On-Disk vs. In-Memory ANN Indexes
Scaling vector search can quickly strain RAM resources. This post tackles a critical challenge: optimizing performance when memory becomes a bottleneck. We explore the trade-offs between in-memory and on-disk Approximate Nearest Neighbor (ANN) indexes, comparing HNSW, SPANN, and DiskANN to architect cost-effective infrastructure. Discover practical strategies for navigating latency and storage considerations, ensuring efficient vector search even with limited RAM. For broader context on data center resilience, see "One fallen power line exposed a growing AI data center problem."