Exa is a neural search system for semantically relevant web sources. RankBits uses it to reveal source candidates that keyword search can miss, especially in the kind of semantic retrieval layer used by products like Cursor and Cognition.
Exa's API supports search modes from instant and fast through deep and deep-reasoning, plus categories such as company, research paper, news, financial report, and people. Exa's site also highlights users including Cursor and Cognition, which makes it a strong lens for semantic source discovery in modern AI products.
RankBits scores the generated answer, not just the source list: brand mentions, citations, citation rank, competitor share of voice, prompt-level wins, and movement across repeated scans.
RankBits runs Exa beside AI assistants and search engines, then shows whether semantically relevant sources translate into mentions, citations, and competitor movement.
Run Exa neural search on the same market prompts.
Look for semantically relevant sources that classic search misses.
Compare Exa sources with actual assistant citations.
Track whether source coverage and answer visibility converge over time.
These are the kinds of queries where AI engines decide who belongs in the market.
Run a free scan and get a complete report with mentions, citations, competitors, and source attribution — in under 2 minutes.
Exa is neural search, so RankBits uses it to find semantically relevant sources that may not appear in classic keyword search.
If Exa finds sources that assistants do not cite, that gap can show where content relevance exists but authority or citation behavior still needs work.
Yes. Exa can be part of a tracked scan lineage with the same prompts and engines over time.