Search,
sifted for AI.
Fast Retrieval & Search API for AI Agents
The retrieval layer for agents that need precise, fresh, structured web context — sub-180ms, neural-ranked, and built for production.
One endpoint.
Four modes of retrieval.
Pick the latency / depth tradeoff per call. Same schema, same auth, radically different shapes of context for your agent.
Trusted by teams building the next generation of agents
Best-in-class accuracy,
at sub-second latency.
Evaluated against the most demanding retrieval benchmarks and head-to-head with leading providers. Numbers below are 30-day rolling averages.
Leads across FRAMES, Tip-of-Tongue, and Seal-0 — the three retrieval evals that punish shallow indexers.
SiftQ Fast returns ranked results under 180ms at p50, faster than any other major retrieval provider.
Specialized indexes for companies, people, code, papers, filings, and news — not just one generic crawl.
Structured output,
built into the call.
Pass a JSON schema. Get typed records back. No second LLM hop, no parser-of-parsers, no extraction prompt drift.
- ✓ Typed fields with schema validation
- ✓ Aggregations across 70M+ companies, 480M+ people, 12M+ repos
- ✓ Stream results as they're enriched
"schema": {
"companies": [{
"name": string,
"ceo": string,
"founded": int
}]
}
// → results
{
"companies": [
{ "name": "General Electric",
"ceo": "Larry Culp",
"founded": 1892 },
{ "name": "RTX",
"ceo": "Christopher Calio",
"founded": 2020 },
{ "name": "Boeing",
"ceo": "Kelly Ortberg",
"founded": 1916 }
]
}Industry-leading indexes,
built for agents.
Dedicated, freshness-tuned indexes for every retrieval shape your agent needs: people, companies, code, docs, filings, papers, news.
Browse the SiftQ index catalog →The Boeing Company is an American multinational corporation that designs, manufactures, and sells airplanes, rotorcraft, rockets, satellites, and missiles worldwide. The company also provides leasing and product support services. Boeing is among the largest global aerospace manufacturers; it is the fifth-largest defense contractor in the world based on 2022 revenue.
Pay for signal,
not for HTML.
Return only the spans of a page that match the query. Cut your context windowcost, and ship faster agents that don't drown in chrome and footer junk.
Security posture
without overclaiming.
Retention options
Default logs support debugging and abuse prevention. Shorter retention or special handling can be reviewed for eligible enterprise customers.
Security review
We can share security documentation and technical architecture notes as part of enterprise evaluation. Formal reports are provided only when available.
Access controls
API keys stay server-side in the product flow. Team access, SSO, and dedicated deployment options are handled by agreement.
Trusted by
world-class teams.
SiftQ's coverage on long-tail technical papers is the only one that consistently surfaces what our researchers ask for. It quietly replaced four other tools.
We swapped retrieval providers in an afternoon. p50 dropped from 920ms to 174ms, and the agent's grounding scores went up double digits.
The structured output mode collapsed a five-step enrichment pipeline into one call. We deleted more code than we wrote that quarter.
Highlights alone paid for the contract. We dropped LLM token spend by 58% and shipped a faster product the same week.
Their long-tail recall on Seal-0 was the deciding factor. For an agent that has to be right about obscure things, SiftQ was the only one that held up.
Models are only as good as what they retrieve. SiftQ gave us the high-quality grounding signal we couldn't assemble in-house at any price.
Latest from
the SiftQ blog.
Web Data Ingestion for LLMs Ultimate Guide
how to ingest live web data for LLMs, reduce RAG hallucination, manage crawling latency, and stream structured JSON context directly to AI models.
Using the FRAMES Benchmark to Test RAG Search Quality
Learn how to evaluate Retrieval-Augmented Generation using the FRAMES benchmark. Improve multi-hop reasoning, factuality, and search latency for AI agents
The Shift From Keyword Search to Semantic Web Retrieval
Learn how AI agents and RAG systems are shifting from keyword matching to intent-driven semantic web retrieval. Discover how structured APIs power this change.
Speak the language of
modern AI agents.
Dive into our comprehensive glossary to understand the core concepts behind retrieval-augmented generation, neural search, and agentic workflows.
Explore all terms ->Questions agents
ask first.
The same answers we expose as structured FAQ data, rendered for readers before the final call to action.
What is SiftQ?
SiftQ is a web retrieval API purpose-built for AI agents. It returns ranked, structured search results in under 180ms p50, across webpage, scholar, podcast, video, image, and document scopes.
How is SiftQ different from Exa or Tavily?
SiftQ adds verticalized indexes (people, code, filings), lower p50 latency, an EU region for GDPR, and structured output as a first-class feature. See the full comparison at https://siftq.com/compare.
Does SiftQ support RAG (Retrieval-Augmented Generation)?
Yes. SiftQ is the retrieval layer in a typical RAG stack: it returns ranked passages and full content extraction with highlights, ready to be concatenated into an LLM context window.
Can I use SiftQ with LangChain or LlamaIndex?
Yes. SiftQ ships official integrations with LangChain and LlamaIndex, and an MCP server for any MCP-compatible client (Claude Desktop, agent frameworks).
Is there a free tier?
Yes — 1,000 queries / month, no credit card required. Per-query pricing thereafter starts at $0.003.
Search built
for your AI.
The fastest, most accurate retrieval API on the public web. Give your agents the context they need — and only that.