AI Search
Search 600,000+ facilities in plain English. No NAICS codes to memorize, no filter combinations to build.
Facilities Finder is built on AI-powered semantic search. Type what you are looking for in plain English — our AI extracts intent and ranks all 600,000+ facilities by match quality, not by keyword.
This is the fastest way to prospect. No filter combinations to build, no industry classifications to memorize.
How AI Search works
At the top of Facility Search you will see a natural-language input:
"Food-processing plants within 150 miles of Columbus with 200+ employees"
"Injection molders making medical-grade parts in Texas"
"Tier 2 automotive suppliers in Michigan doing metal stamping"
Our AI parses your query into structured signals — geography, industry, products, facility size, certifications — and ranks the full 600,000+ facility dataset against it. Results are sorted by semantic match, not keyword overlap.
What powers it
Two AI systems work together:
AI Enrichment. Before you ever run a search, our AI has ingested billions of public signals — satellite imagery, map providers, company websites, EPA filings, permit records, and trade publications — and extracted a structured profile per facility. That includes facility-level products made (7 million+ indexed), waste profile, estimated employee count, facility type, and parent-company rollup.
AI Classifications. Instead of relying on 6-digit NAICS codes alone, we have generated 35,000+ AI-derived industry classifications at the facility level. That is why a search for "makers of medical-grade plastic components" returns the right plants even if none of them list themselves under a single NAICS code.
Stack filters on top
Semantic search and hard filters combine. Common workflow:
- Type a natural-language query in AI Search
- Apply your territory or states filter to restrict geography
- Add hard filters (employee count, EPA flags, certifications) to narrow further
- Save the result set to a list or export
Every filter you add tightens the ranking. The semantic match quality does not reset.
Query examples that work well
- Product-first: "plants making industrial adhesives in the Midwest"
- Process-first: "powder-coating shops in the Southeast doing aerospace work"
- Buyer-first: "chemical plants with on-site waste treatment"
- Territory + size: "food plants in my territory with 500+ employees"
- Parent + footprint: "every Berry Global facility in the US"
Keep queries concrete. The more specific the signal (product, process, size, location), the better the ranking.