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Semantic search has mostly meant sending your data off to someone else's servers, waiting on an API, and hoping your queries don't end up as training fodder somewhere down the line. This latest entrant on Product Hunt flips that script by keeping the whole pipeline local, aimed squarely at developers building AI agents that need to find relevant context fast without phoning home.

The pitch is simple enough: agents increasingly need to search through documents, code, or memory stores to decide what to do next, and doing that search locally cuts latency, avoids per-query costs, and keeps sensitive data off third-party infrastructure. That last point matters more than it used to. Plenty of teams building agentic workflows are handling proprietary codebases or internal documents, and shipping that data to a hosted embedding API isn't always an option, whether for compliance reasons or just plain paranoia.

What's notable here isn't the concept of semantic search itself — vector databases and embedding models have been commodity tech for a couple of years now. It's the framing around agents specifically. Rather than positioning itself as a general-purpose search product, this tool leans into the idea that autonomous agents need fast, private, always-available retrieval as a core primitive, not a bolted-on feature. That's a narrower niche than