Give customers answers they can verify
Ground responses in current, relevant web evidence with source links, helping customers confirm results and reducing the time your team spends correcting poor outputs.
Give AI-powered products relevant, information-dense web context with fewer wasted tokens, controls to tune retrieval by workflow, and the flexibility to switch models without rebuilding search.

Build customer-facing agents that retrieve current, source-linked information for account research, legal drafting, threat investigation, and other product workflows.

Connect AI assistants to current web context, improving answers beyond model training data without maintaining your own search infrastructure.

Ground coding agents in current documentation, SDK references, and community discussions, so developers spend less time correcting outdated suggestions.

Retrieve current firmographics, ownership details, and public company information to fill CRM gaps and support more accurate account prioritization.

Give internal research agents current, source-backed information for competitive briefs, market reports, and product or positioning decisions.

Supply live web retrieval for high-volume training workloads, helping research agents find relevant context with fewer repeated searches.

99.8%
retrieval success rate
monday.com
monday.com uses Tavily for news monitoring, contact enrichment, and open-ended research across multiple AI products, with 99.8% of retrievals completing successfully.
Read the monday.com story
67K of 80K
trajectories used to train Nemotron 3 Super
NVIDIA
NVIDIA used Tavily across approximately 80,000 research trajectories and retained approximately 67,000 to fine-tune Nemotron 3 Super.
Read the NVIDIA story
Millions
of requests a day
Rox
Rox uses Tavily to bring fresh company news and web context into its revenue agents, supporting account intelligence and personalized outreach at scale.
Read the Rox storyGround responses in current, relevant web evidence with source links, helping customers confirm results and reducing the time your team spends correcting poor outputs.
Send focused passages to your models instead of noisy pages, helping control inference costs as queries become more complex and product usage grows.
Tune sources, freshness, search depth, and result volume to balance answer quality, speed, and cost for each product experience.
Preserve your search integration and retrieval controls as you change models or cloud platforms, making it easier to choose the best model for each product.
Scale AI products on a production-ready web search API with custom rate limits, enterprise SLAs, and dedicated support. Keep engineers focused on the product instead of retrieval infrastructure.
Support enterprise requirements with Zero Data Retention, prompt-injection protection, malicious-source filtering, PII filtering, SOC 2 Type II, and ISO 27001.
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Bring web retrieval to your existing AI stack




Built-in search is convenient for prototypes, but it ties retrieval to one provider and gives teams limited control over how agents search. Tavily provides an independent web search API with configurable sources, freshness, depth, and result volume.
Tavily ranks results and extracts focused passages, so models process less irrelevant content. Teams can also control when agents search and how much they retrieve, helping balance answer quality, latency, and cost for each workflow.
Yes. Tavily keeps web retrieval separate from the model, allowing teams to reuse the same search integration and controls across models and cloud platforms. You can switch models without rebuilding how your agents access the web.
Yes. Tavily supports production workloads with custom rate limits, enterprise SLAs, dedicated support, and security controls including Zero Data Retention, prompt-injection protection, PII filtering, SOC 2 Type II, and ISO/IEC 27001:2022.