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Hyperresearch

Durable, verified agentic search

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Hyperresearch does the research and shows the evidence. Ask a question, get a cited report; each cited claim is checked against its source, and all sources are kept in a searchable vault. On the DRACO benchmark, we are the world's number-one research agent. New: Answers MCP and API. Your agent calls ask and gets a short answer with word-for-word quotes, or a simple "not found" instead of a hallucination. We combine 4 AI search engines and 40+ other data sources for the widest and deepest access.

PH · launched Nov 2, 2026 · 105 upvotes (top 1%) · 0 views

Niche: ai research assistants · 76 launches, 3 in the last 7 days ▼ -67% · Explore this niche →

Category and tags
  • Horizontal
  • Search & retrieval
  • Developer
  • AI-native
  • Commercial product
  • API
  • Developer Tools
  • Artificial Intelligence
  • GitHub

Visit website ↗ Source listing ↗

More like this: all 76 launches in ai research assistants →

Could you build this?

Partial A cited, source-checked research assistant can be assembled from search APIs and LLMs, but being the top-ranked agent on a benchmark like DRACO requires substantial research on retrieval, planning, and verification.

What it would actually take: A basic version would use a search API, a retrieval layer over stored sources, and an LLM that drafts claims with citations, followed by a verifier pass that checks each claim against its source text. Matching a top benchmark score needs a tuned multi-step agent, a strong evaluation harness, and ML researchers who iterate on retrieval and verification quality. Source-quality data and compute for experiments are also needed.

Competitors

Other products that read as similar to this one : 168 launches clear the similarity bar, closest 8 shown.

Attention rank: #7 of 169 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 364 days after the earliest competitor.

Related data

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