indexing
Beyond Market Intelligence keeps indexing in one place: 6 stories so far. The section currently leads with “Build Smarter Search with a Simple Translation Table from Click Data”, “A single filter closes the retrieval gap in Azure OpenAI email automation.”, and “Discover how Keenable's web index empowers AI agents to explore smarter.”. A search index that already knows which words tend to follow from its documents is a quiet kind of upgrade. Closing an Azure OpenAI assistant's retrieval gap didn't require a new identity platform. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every indexing story on Beyond Market Intelligence, newest first.
Build Smarter Search with a Simple Translation Table from Click Data
A search index that already knows which words tend to follow from its documents is a quiet kind of upgrade. This "poor man's DSSM" builds count-based translation tables from supervised query-document pairs, then bakes the top associations straight into the inverted index at indexing time. It won't capture the non-linear magic of a full deep model, but it doesn't need to. The BM25 lift is real, and the approach is refreshingly simple.
A single filter closes the retrieval gap in Azure OpenAI email automation.
Closing an Azure OpenAI assistant's retrieval gap didn't require a new identity platform. Cioffi added one query-time filter and narrowed the assistant's reach. The fix worked because he tested with a low-privilege account, not just clean evaluation scores. That two-account test exposed what his logs already hinted at: the agent answered with the indexer's permissions, not the requester's. It's a thirty-minute check that belongs before any deployment.
Discover how Keenable's web index empowers AI agents to explore smarter.
There's a quiet shift happening in how AI agents will find what they need. Accel-backed Keenable is stepping out of stealth with a $26 million seed round, having spent its time building a web search index designed specifically for those agents. It's a smart move to focus on the infrastructure beneath the surface. For anyone following the deeper questions around AI's reliability, our piece on verifying an AI's understanding offers a useful companion read. This feels less like hype and more like groundwork.
Explore the Hidden Geometry Inside Your Model's Working Memory
The KV cache isn't a flat list; it's a navigable vector space, and this researcher turned that observation into a working system. On a frozen Qwen3.5-2B at 32k context, geometric routing cuts physical KV reads by 16-31× while still retrieving the planted long-range needle. That's not a theoretical pitch; it's a reproducible demo. The insight is that attention is already similarity search, so indexing old context isn't a hack, it's the natural next step.
Three Retrieval Baselines Tested on a Standard Laptop
Reproducing BM25, dense retrieval, and SPLADE on a 16GB MacBook sounds like a rite of passage until the first out-of-memory crash. This practical walkthrough tracks the real stumbles, the fixes that followed, and the score checks that actually matter for RAG systems. It's a grounded look at what breaks when resources are tight, and what holds up. For readers ready to connect retrieval fundamentals to broader AI workflows, the related piece on bridging retrieval and action offers a natural next step.
Explore cost-effective vector search with on-disk ANN indexes like DiskANN
RAM is the ceiling that most vector search budgets hit first. When that ceiling gets too expensive, the conversation shifts to disk. The real trade-offs between in-memory HNSW and on-disk options like SPANN and DiskANN are walked through, giving a practical lens on latency versus storage cost. It's a grounded look at architecting infrastructure that scales without breaking the bank. For a deeper dive into system-level choices, our piece on adaptive recommendation systems offers a useful parallel.