Data Layer
4 stories filed under Data Layer on Beyond Market Intelligence. The newest of them: “Balancing precision and cost to feed AI agents the right data”, “Build AI Applications That Remember, Not Just Retrieve”, and “From NASA to nanosecond data: Direct-access Valkey cuts latency and cost.”. Feeding enterprise data to token-hungry AI agents isn't just about scale; it's about precision. RAG systems fetch, but they don't hold on to what matters. 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 Data Layer story on Beyond Market Intelligence, newest first.
Balancing precision and cost to feed AI agents the right data
Feeding enterprise data to token-hungry AI agents isn't just about scale; it's about precision. At TOTVS, Fabiane Nardon tackles this head-on by balancing deterministic logic with the non-deterministic nature of LLMs. Her approach is refreshingly practical: using data mesh principles, low-latency databases, and semantic ontologies to keep context windows lean and costs controlled. Dynamic MCP tool selection becomes the key to trimming token overhead. It's a smart, human-centered strategy for making AI genuinely useful in transactional systems.
Build AI Applications That Remember, Not Just Retrieve
RAG systems fetch, but they don't hold on to what matters. That's the gap this guide tackles head-on, offering a vendor-neutral blueprint for building a knowledge layer that actually accumulates understanding, not just query results. The Azure-native walkthrough, mapping Foundry, AI Search, Cosmos DB, and FastAPI to a property-insurance corpus, turns theory into something you can build on. If you're still questioning how much AI truly grasps, "Verify Your AI's Understanding" pairs well with this practical read. It's a grounded, actionable take.
From NASA to nanosecond data: Direct-access Valkey cuts latency and cost.
Dumanshu Goyal doesn't just tweak a database; he rethinks the path data takes. His talk, *From ms to µs*, draws a sharp lesson from NASA's Space Shuttle: every layer of proxy adds hidden CPU costs and widens tail latencies. The fix is direct-access Valkey architectures, which deliver microsecond responses while shrinking both risk and infrastructure bills. It's a practical, no-hype case for simplifying your data layer.
Give your AI agents a single source of truth for business data
Pinecone's Nexus Engine is now generally available, and it's a practical answer to a messy problem: enterprise data that sits scattered and unstructured. Nexus compiles that business context into a queryable layer, so AI agents stop guessing and start working from curated knowledge. Teams ingest once, then reuse that context across agents, cutting token costs while boosting accuracy. That's a meaningful step toward tools that actually understand the work. For more on where this tech leads, our guide to distributed training offers useful context.