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AI Research Pipeline Stack

A three-step research workflow that moves from broad discovery to academic verification to structured synthesis. Perplexity AI handles the initial search across web, news, and academic sources. Elicit takes those findings and runs them against peer-reviewed literature to verify claims, extract methodologies, and surface contradicting evidence. NotebookLM brings everything together into a structured briefing document with cross-references across all your sources. The handoff between tools is manual (URLs and PDF uploads), which is how analysts, consultants, and strategy teams actually work. No API connection is required. The value is in the sequence: breadth first, rigor second, synthesis third. All three tools are free or freemium with self-serve signup, so you can run this workflow within an hour of reading this.

The workflow — 3 agents in sequence

1
Perplexity AI logo
Perplexity AIMCP server

Broad discovery and sourced answers with citations across web, news, and academic sources

AI-powered answer engine with real-time web access and inline citations. Free, Pro $17/mo, Max $167/mo, Enterprise Pro $34/seat. Computer agent on all paid plans. Comet browser, Sonar API.

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Manual: copy key findings and source URLs to use in step 2

2
Elicit logo
ElicitMCP server

Academic verification against peer-reviewed papers with extracted findings and methodologies

AI research assistant for systematic literature reviews across 138M papers and 545K clinical trials. MCP server, full API, SOC 2 certified. Free; Pro $49/mo annual. 5M+ researchers.

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Manual: upload papers and claims from step 1, export verified findings for step 3

3

Synthesis into a structured briefing document with cross-references across all sources

Google source-grounded AI research assistant. Free up to 50 sources per notebook; paid tiers from $7.99/mo via Google AI subscriptions. Audio Overviews, mind maps, and citation-backed Q&A on uploaded sources only.

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Some agents in this stack expose an MCP server

MCP (Model Context Protocol) is an open standard for connecting AI agents to tools and data. The agents that publish a server can be connected into directly by an AI assistant. The others act as clients, meaning they consume external tools rather than exposing their own, so those steps still need conventional integration.

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