The use case: how Peerbound deploys Tavily
Peerbound replaced a SERP API and LLM pipeline with Tavily to gather accurate, concise information about hundreds of thousands of companies. It also added Tavily web search to its LangChain-powered chat agents in its Slack app.
More reported results
Figures published in Tavily’s case study. They are not independently measured by usedby.
5 minutes
5 minutes to integrate Tavily end to end
In the customer’s words
The results were concise, not information overload — but also all relevant. The information was prioritized, concise, and better than a top-level Google search. I was able to get exactly what I wanted.Other companies using Tavily
Customers across industries, with public evidence.
See all Tavily customers
Go straight to the source
Based on Tavily’s published case study. We check the public evidence; the results are reported by the vendor.
tavily.com- Last checked
- Nov 12, 2026
Open source capture What the story claims, and what we checked
What we compared with the page.
- The figure: 100%Checked
The figure is on the page; its label is our wording.
- The passage quoted aboveChecked
Copied word for word from the page, near the name of Peerbound.
- Peerbound uses TavilyChecked
Confirmed line.
- The result itselfNot checked
We quote it; we did not measure it.
Dasha had been tracking examples of bad descriptions from internet searches. Before, "maybe 2 out of 10 would be accurate and 8 would be wrong." When she ran them with Tavily, "all 10 were correct. It felt like a massive leap in quality."