How they use Tavily

Peerbound uses Tavily’s Search API to enrich company records for its customer marketing and sales tools. It replaced a search results pipeline with Tavily to supply descriptions for classification and matching. Tavily’s relevance scores let Peerbound filter search results before downstream LLM processing. Peerbound chose Tavily after finding Perplexity added too much post-processing and Exa returned responses that were too long. Peerbound also added Tavily web search to LangChain-based chat agents in its Slack app. The case study describes a five-minute API URL replacement and information-gathering pipelines spanning hundreds of thousands of companies. The enriched data supports vertical classification, location-based filtering and Slack queries used daily by thousands of salespeople.

Tavily’s May 2026 case study says adoption began in autumn 2025. On ten tracked examples, accurate descriptions increased from two to ten after switching to Tavily. Tavily’s customer-story announcement describes the improvement with Tavily Search as happening overnight. The reported accuracy improvement came from a small customer sample. Peerbound expanded from batch enrichment into real-time agent search and planned to explore Tavily Crawl for deeper research.