Use cases
Each page names the specific parameters that make the workload work, and a section on where this is not the right tool. No customer names — we do not cite customers without permission.
RAG over the open web
Retrieve, deduplicate, trust-rank and pack to a token budget — either as separate calls you control, or as one call that does the whole loop.
Teams grounding an LLM in current web content rather than a private corpus.
Autonomous research agents
Graph traversal replaces the search-read-search loop: triage a field by centrality, build entity briefs in one call, and follow edges instead of guessing new queries.
Teams building agents that investigate a topic across many sources.
Fact-checking with corroboration counts
Count distinct hosts asserting a claim rather than counting copies — and get the count from an index where deduplication does not destroy it.
Teams that must verify claims before repeating them.
Monitoring topics and entities
Browse by recency with no query at all, bound by publication date, and collapse syndication so a monitoring feed carries distinct events.
Teams tracking coverage of a topic, company or event over time.
Competitive and market intelligence
Entity dossiers, co-mention discovery and connecting paths — the operations that make competitive research structural rather than a search loop.
Teams tracking companies, products and market movements.
Documentation search for coding agents
The code vertical and the docs content type remove tutorial blogspam; the term filter makes error-code lookup exact.
Teams building coding assistants and developer tooling.
Grounded question answering
Get a packed, corroborated context set in one call, with a reason attached to every passage so the answer can be explained.
Teams building assistants that answer factual questions from current sources.
Regulatory and compliance research
Time-bounded queries, authority filters, uniform corroboration thresholds and an endpoint that explains any exclusion.
Teams in finance, legal, pharma and the public sector who must defend a retrieval decision.
Finding sources worth following
Centrality surfaces what a field treats as foundational; similar and related widen from a seed without inventing new queries.
Teams building recommendation, curation and discovery products.
Serving users in many languages
One embedding space for 101 languages means retrieval crosses languages without a translation step or a per-language index.
Teams whose users search and read in different languages.
Start with the free tier
10,000 requests a month, no card. Every parameter documented here works on every plan.