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Dictionary

Research Automation

Research automation turns a question into a sourced, structured answer using search, retrieval, extraction and synthesis agents.

1 min readupdated 2026-08-01

/ quick answer

A research automation pipeline has four stages: query expansion (turn one question into many), retrieval (search and fetch), extraction (pull claims with citations), and synthesis (compose an answer that only uses extracted claims). The quality lever is extraction discipline — forcing every statement to carry a source kills most hallucination.

Research automation turns a question into a sourced, structured answer using search, retrieval, extraction and synthesis agents. A research automation pipeline has four stages: query expansion (turn one question into many), retrieval (search and fetch), extraction (pull claims with citations), and synthesis (compose an answer that only uses extracted claims). The quality lever is extraction discipline — forcing every statement to carry a source kills most hallucination. In practice: A competitor-monitoring pipeline that runs 40 queries weekly, extracts pricing and feature claims with URLs, and outputs a diff against last week.
Definition
A research automation pipeline has four stages: query expansion (turn one question into many), retrieval (search and fetch), extraction (pull claims with citations), and synthesis (compose an answer that only uses extracted claims). The quality lever is extraction discipline — forcing every statement to carry a source kills most hallucination.
Example
A competitor-monitoring pipeline that runs 40 queries weekly, extracts pricing and feature claims with URLs, and outputs a diff against last week.
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What is Research Automation?

A research automation pipeline has four stages: query expansion (turn one question into many), retrieval (search and fetch), extraction (pull claims with citations), and synthesis (compose an answer that only uses extracted claims). The quality lever is extraction discipline — forcing every statement to carry a source kills most hallucination.

What is an example of Research Automation?

A competitor-monitoring pipeline that runs 40 queries weekly, extracts pricing and feature claims with URLs, and outputs a diff against last week.

Why does Research Automation matter?

Research automation turns a question into a sourced, structured answer using search, retrieval, extraction and synthesis agents.

What should I learn after Research Automation?

Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.

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The vocabulary this page depends on.

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Related workflows

Turn this into a repeatable process.

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Related tool stacks

The tools that run it in production.

  • →Research Automation Stack

    Search, fetch, extract and synthesise sourced briefs on a schedule.

  • →AutoGen Agent Research Stack

    This stack outlines the core components for building an autonomous research agent system using AutoGen, focusing on dynamic information retrieval, analysis, and report generation.

  • →Agentic Research Browser Stack

    This stack combines an AI browser, an LLM, a vector database, and an orchestration layer to enable autonomous, in-depth web research, data extraction, and synthesis. It facilitates intelligent information gathering beyond simple scraping.

  • →AI Research & Knowledge Stack

    Default toolset for analysts, founders and creators doing deep research with AI.

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Related prompts

Reusable prompts for this job.

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Related use cases

How people apply it, and what came out.

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Comparisons & alternatives

Pick between the options.

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