How do you find prospects that use a vector database?

Best move: don’t hunt for a master list. Point per-domain detection at a candidate set and keep the domains whose front end loads a vector-store client.

Why it works: a vector database is backend infrastructure. A company that runs one has to load its client and call its API, and those signatures survive minification, so a per-domain check returns a high-confidence yes or no.

Key takeaways

• No free, authoritative global index of every company using a given vector database exists. Detection runs per domain.
• The workflow is seed then confirm: pull candidate domains from a corpus, funding news, event sponsors, or your CRM, then fingerprint each one.
• Pinecone, Weaviate, Qdrant, Chroma, and Milvus each ship a distinct client and a named API hostname that survive minification.
• A detected vector store is hard proof a company ships AI. It costs money to run, it does one job, and nobody fakes it.
• Bassethound returns the vector store alongside firmographics and contacts in one call, so a hit is already a qualified prospect.

Where do candidate domains come from?

Bassethound analyzes a domain you name. It does not maintain a global reverse index of every vector-store user, so sourcing is step one. Good seeds: recent seed and Series A funding announcements (AI infra buyers cluster there), sponsor and exhibitor lists from AI conferences, GitHub org pages whose repos import a vector client, accelerator batch pages, and your own CRM’s untouched accounts. A tech-usage corpus (BuiltWith, TheirStack) can export a rough “uses Pinecone” list you then verify. Any of these gives you a domain column. Once you have domains, the detection loop is uniform: one call per domain, filter on the vector-store signal. An agent host connected to mcp.bassethound.ai runs the loop itself, because the endpoint is keyless and stateless. No key to provision per lookup, no session to carry between calls. Volume past the free tier’s daily limit moves to a paid plan. Your seed list sets recall. The per-domain call sets precision.

How do you turn a candidate list into a confirmed prospect list?

Fan each candidate domain through sniff_domain and read layer four, AI-readiness. The vector store, when present, appears there with a category, confidence, and how_detected trail (client library, package name, or API hostname). Keep the domains that hit, drop the rest. Then read the other four layers in the same response. Firmographics hands you the company name, vertical, and contacts. The rest of AI-readiness names the model provider and orchestration framework sitting next to the vector store. That correlation turns a hit into a prospect. A domain running Pinecone plus the Anthropic SDK plus LangChain is a team shipping real AI, and you hold the contact to reach it and the stack detail to open with. Use the standard profile for the AI stack and firmographics. Use deep when you want security posture and Wayback first-seen history to gauge how long they have been building. One call per domain, five correlated layers, no stitching across tools.

Why not use a prebuilt “companies using Pinecone” corpus?

Because the two do different jobs. BuiltWith and TheirStack crawl the web continuously and index tech usage, so they answer “list companies using X” with recall no per-domain tool matches. If your only need is a wide, rough pull, use them. Their limits show at qualification. Their AI-stack detection stays shallow (a homepage read plus a library check), their data is a static snapshot you re-query, and no agent calls it mid-task without a key and a plan. Bassethound inverts that. You point it at domains, and per domain it goes deep: model provider, SDK, orchestration, vector store, observability, MCP endpoints, all correlated with infrastructure and security in one keyless call the agent makes itself. So the honest pattern uses both. Pull a wide candidate list from a corpus, then run it through Bassethound to confirm the vector store, kill the false positives, and enrich each survivor with the contacts and full AI stack you will pitch against. The corpus gives breadth. The dossier gives depth and the last mile of qualification.

What makes a vector-database hit a good ICP signal?

A vector store is the highest-intent tech signal you can read from the outside. It is backend infrastructure that does one thing: retrieval over embeddings. A company does not stand one up to look modern. It stands one up because it is building RAG, search, or an agent that needs memory. It costs money to run and there is no reason to fake it, so the signal carries almost no marketing noise, unlike a homepage that reads “AI-powered.” Bassethound folds this into the top-level icp_signal. A domain that hits the vector store plus a model provider plus an SDK lights up hot, because that combination is a team in production, not a landing page. Cross-reference the ai_readiness verdict: shipping beats experimenting beats none. For a GTM motion selling to AI builders (infra, tooling, eval, observability, data), a confirmed vector store is a buying-window signal. They have the problem your product addresses, and they have already spent to prove it.

What will this miss?

Be honest about the gaps. Bassethound reads a static crawl plus an optional JS render. A vector store called only from a server-side backend, with no client library or named API hostname reachable from the front end, can stay invisible. Same for usage routed through a first-party proxy that hides the vendor endpoint. Self-hosted open-source stores (Qdrant, Milvus, or Chroma on your own infra) are harder to detect than managed SaaS, because no vendor hostname exists to key on. The dossier reports these gaps rather than guessing. A null in the vector-store slot means “not detected from the outside,” not “definitely absent.” So treat a hit as high-confidence and a miss as inconclusive. For coverage, pair the front-end signal with the other four layers (a company running the Anthropic SDK and LangChain but no visible vector store still earns a look) and, on the deep profile, the Wayback history that shows how long the AI stack has been in place.

Bassethound perspective

Most prospecting tools sell you a list and call it done. The list is a static corpus, and its AI-readiness column is a homepage keyword scan, so it fills your pipeline with companies that say “AI” and ship nothing. That is the wrong end of the problem. The signal that matters is not what a company claims, it is what its stack proves. A vector database is proof. Our conviction: the moat is fusion, not fan-out. An agent could call five separate MCPs (one for tech, one for infra, one for firmographics) and stitch the results, but it pays five round trips and still has to correlate them itself. Bassethound returns the vector store already sitting next to the model provider, the contact, and the security grade, correlated, in one keyless call the agent makes inside a single request. That correlation is the qualification. Because we own the crawl, there is no key to bring and nothing carried from one call to the next, so the loop runs at list scale instead of one dashboard tab a human clicks through. Point us at domains. We tell you which ones are building.

Sources

• Pinecone documentation: https://docs.pinecone.io
• Weaviate documentation: https://weaviate.io/developers/weaviate
• Qdrant documentation: https://qdrant.tech/documentation/
• Model Context Protocol: https://modelcontextprotocol.io