vector search
Beyond Market Intelligence keeps vector search in one place: 6 stories so far. The section currently leads with “Build and benchmark production RAG with fully open models”, “Building trusted AI for regulated industries at global scale”, and “DynamoDB Unifies Data and Vector Search Without a Separate Database”. Building a RAG system that actually works in production takes more than stitching together a vector database. Building production-ready AI in healthcare isn't just about getting the model right, it's about engineering trust from the ground up. 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 vector search story on Beyond Market Intelligence, newest first.
Build and benchmark production RAG with fully open models
Building a RAG system that actually works in production takes more than stitching together a vector database. This workshop on August 29, led by Ben Auffarth of Chelsea AI Ventures, tackles the messy parts head-on: hybrid retrieval, reranking, and guardrails built in from the start. It's refreshing to see evaluation with RAGAS and real cost benchmarking treated as essentials, not afterthoughts. If you're tired of demo-grade AI, this hands-on session is worth exploring.
Building trusted AI for regulated industries at global scale
Building production-ready AI in healthcare isn't just about getting the model right, it's about engineering trust from the ground up. Heidi, an Australian-founded AI Care Partner, proves this by automating clinical documentation across 190 countries, handling 2.7 million patient interactions weekly. The company's success rests on infrastructure decisions made years ago, prioritizing data residency, auditability, and safety by default. As CTO Yu Liu puts it, trust is architectural.
DynamoDB Unifies Data and Vector Search Without a Separate Database
DynamoDB now lets developers store embeddings right alongside their application data and run approximate nearest-neighbor queries without spinning up a separate vector database. That's a meaningful simplification for teams building semantic search features. Filtered similarity searches and configurable indexes make it practical, not just theoretical. It's the kind of move that reduces operational overhead while keeping workflows familiar. If you're exploring how far native database capabilities can stretch, this is worth a look.
Explore how LanceDB unifies text, images, and documents for smarter AI workflows.
Large language models excel at text, but they stumble when information hides across documents or inside images. That's where LanceDB steps in. This guide breaks down how the vector database stores embeddings and powers similarity search for AI workloads, with a practical Python demo to get you started. It's a clear, accessible look at a tool that simplifies multimodal data handling. For more on how AI handles context, check out our piece on agentic learning.
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.