Discover everything your social monitoring AI needs from the web
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The most accurate & efficient web search for social monitoring
What this measures
This benchmark evaluates Opus 5.5’s accuracy and total cost (web search fees + token consumption) to answer complex questions about companies, litigation, financials, products, and industry news.
Methodology summary
We created a ground truth dataset for 250 questions that require company research by running Fable 5.1 with unlimited resources and manual verification. We then ran Opus 5.5 on high effort six times, each with a different web search provider, and compared the results against ground truth.
Try out Social Media Monitoring use cases with Nimble
Or see our developer docs for building social monitoring agents with Nimble
Track every mention of your brand
Log posts across X, Reddit, YouTube and TikTok with sentiment, intent and a link to each one.
Catch sentiment shifts and what caused them
Compare this period with the last, and see the themes and posts behind the change.
Spot trends before they peak
Find topics and formats taking off in any niche, with momentum and the top posts.
Profile creators across every platform
Get confirmed accounts, follower counts, recent posts and sponsorships, each sourced.
Mine communities for product feedback
Rank complaints, feature requests and churn signals by how many users raised them.
How Nimble discovers deep, trustworthy social data
Get the precise data you asked for
Self-learning research agents optimize search & retrieval for specific tasks.
Access web data others can’t
Nimble doesn’t just retrieve search results, it browses sites to go deeper.
Retrieve data you can trust
Web search governance and confidence scoring to flag weak evidence.
How agent builders integrate Nimble
Embed research into your agent workflows
Via MCP, API or connectors
Ad hoc research through your personal AI
Like Codex, Claude Code, or Cursor
Build large-scale web datasets
Stream structured data into your datastore
