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Case Study

Case Study

Ground models with fresh web context

Retrieve live web data, extract relevant content, and return it structured and chunked for models, so agents reason over facts without hallucinating.

Handle thousands of web queries in seconds

A production-grade retrieval stack with real-time search, intelligent caching, and indexing keeps latency predictable as traffic grows.

Ship to production with built-in safeguards

Requests pass through security, privacy, and content validation layers that block PII leakage, prompt injection, and malicious sources.

About this benchmark

This benchmark evaluates search-augmented reasoning using Virginia Tech's SealQA, which measures how well models handle fact-seeking questions where web search returns conflicting, noisy, or unhelpful results. It focuses on deep reasoning under ambiguity.

Methodology

Dataset: Full set of Virginia Tech's SealQA

Model: GPT-5.4-mini (reasoning_effort="medium"), grounded by retrieved documents from provider

Scoring: Accuracy (correct answers ÷ total questions), graded by GPT-4.1-mini using official SealQA prompt

Normalization: Comparable document length across providers

Retrieval: max 10 documents per query

300M+

monthly requests handled

99.99% uptime

SLA powering mission-critical systems

180 ms

p50 on Tavily /search making us fastest on the market

2M+

developers using Tavily

Billions

of pages crawled and extracted without downtime

Drop-in integration

with leading LLM providers (OpenAI, Anthropic, Groq)

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