Peerbound's 5x Data Accuracy Leap with Tavily's AI Search API

Peerbound helps customer and product marketers deliver customer proof points to sales teams right when they need it. Think case studies, customer references, and competitive intel. The company's Slack-based tools serve thousands of salespeople by surfacing relevant customer stories matched to specific verticals, industries, and locations. Powering these capabilities requires Peerbound to maintain rich and accurate data about the companies its customers sell to — a dataset spanning hundreds of thousands of companies.

The Problem: The Data Foundation Everything Depended On Was Breaking

Dasha Bobrova, an engineer at Peerbound who is responsible for the company's data infrastructure, built an initial solution using a traditional SERP API to scrape Google and process results with an LLM. The challenge was the sheer diversity of the dataset. Dasha explained that they have "hundreds of thousands of companies and need fairly uniform, short, brief but accurate information about these companies.” Some are household names like "Bob and Joe's Truck in Missouri, and that's the company we need to find information for."

The SERP-based approach quickly hit its limits. Dasha found that "the results from Google were sometimes not enough and sometimes too much." Small companies might return irrelevant top results, and context window problems created a whack-a-mole dynamic where "you fix one problem, you create a new one."

The stakes were high because this company data underpins nearly everything Peerbound offers, from Slack queries to filtering and vertical classification. Dasha called it "the base layer" — the foundation every Slack query, vertical filter, and customer match is built on. When that data is off, the wrong result surfaces for a sales rep mid-deal, eroding the trust between teams that Peerbound is built to strengthen. Getting that base layer right is exactly what Tavily made possible.

Why Tavily: Perplexity Did Too Much. Exa Ran Too Long. Tavily Was Just Right.

After evaluating multiple tools across the web search and research space, Dasha chose Tavily as the replacement for Peerbound's SERP-based pipeline. The decision came down to the quality of the data returned. She said the results were "concise, not information overload, but also all relevant. The information was prioritized, accurate, and better than a top-level Google search. I was able to get exactly what I wanted."

Alternatives fell short in different ways. Dasha noted that Perplexity "did almost too much post-processing on top of the results," when what she really wanted was "the relevant raw data." Exa's responses, meanwhile, ran long. "Because I wanted short, concise, to-the-point responses, it was neither here nor there with what Exa returned."

A key differentiator was Tavily's built-in relevance scoring, which allowed Dasha to programmatically filter results before passing them to downstream LLM processing. She said she really liked the relevance score because it let her "only take the results that are actually relevant, because I need just the perfect amount of information, not information overload."

The Solution: A Five-Minute API Swap That Fixed the Base Layer

Implementation was equally straightforward. Dasha described it as "a five-minute API URL replacement," explaining that she "basically took the part of the code where I used to have the Google SERP API and just dropped it right in. I launched it and never had any problems with it." Beyond the core data pipeline, Peerbound also integrated Tavily into its LangChain-powered chat agents to add web search capabilities to its Slack app. Enterprise pricing further supported running mass information-gathering pipelines across hundreds of thousands of companies.

The Outcome: 2/10 to 10/10 — Overnight

The impact was immediate and measurable. 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."

This jump in accuracy strengthened every layer of Peerbound's product, from vertical classification to location-based filtering to the Slack queries used daily by thousands of salespeople. With reliable company data flowing through the pipeline, the team could trust the results being served to customers without manually auditing outputs or cycling through iterative fixes. Since adopting Tavily in fall 2025, Peerbound has expanded its use from batch data enrichment into real-time agent-powered search, with plans to explore newer endpoints like Crawl for even deeper research capabilities.

Tired of inaccurate company data slowing down your sales team? Explore the Tavily Search API or talk to our team to see how Tavily can power your data pipeline.