Produce a deep research report on demand
Turn one question into a structured, cited report combining web research and your own knowledge.
An AI research report generator should hand you something you'd stake a meeting on. Give this workflow a question ("Should we expand to the German market?", "What are the real alternatives to our current payment provider?") and it returns a structured, cited report that combines web research with what your own company already knows.
What does this workflow do?
The agent breaks your question into sub-questions, researches each across the web, and cross-checks every claim across multiple sources. Numbers get traced toward their original source rather than lifted from roundups.
It then adds the half that external research tools miss: your internal context. Past experiments your team ran, existing customer data, earlier decisions and their reasoning, all pulled from Notion, Slack and Google Docs with permissions intact. A market-entry report that knows you tried outbound in DACH last year is a different document from one that doesn't.
The output is a report in Google Docs: summary and recommendation up front, findings per sub-question, every claim cited, and an honest section on what the research couldn't establish.
How does it work?
- Ask the question. Add constraints if you have them: budget, timeline, what a good answer looks like.
- The agent plans the research. Your question becomes explicit sub-questions. You can review and edit the plan before it runs.
- It researches wide, then verifies. Multiple sources per claim, disagreements surfaced rather than averaged, statistics traced toward their origin.
- It brings in internal knowledge. Related past work, decisions and data from your connected tools. If a key fact lives only in a colleague's head ("Why did we deprioritize this market in 2024?"), the agent asks them directly.
- It delivers the report. Google Docs for the full document, a summary in Slack, and the findings retained in the knowledge base so the next related question starts warmer.
What kinds of questions work?
Market sizing and entry questions, vendor and build-vs-buy comparisons, competitive landscape mapping, regulatory overviews, and technology evaluations. Concrete beats vague: "Which of these four CRMs fits a 20-person sales team selling into healthcare?" produces a sharper report than "Tell me about CRMs."
Reports compound. Because findings persist in the knowledge base, the second report on an adjacent question reuses verified groundwork, and it cites your first report where relevant.
Works with
Web, Google Docs, Notion, Slack. Run on demand; recurring versions (a quarterly market update) run on a schedule.
Frequently asked questions
How long does a report take?
Minutes to a few hours depending on scope, not days. You see progress while it runs and can answer the agent's clarifying questions midway.
How does it handle conflicting sources?
It shows the conflict. When two credible sources disagree on a number, the report presents both with dates and lets you judge, rather than silently picking one.
Does it use our internal data?
Yes, from the tools you've connected, filtered by what you personally have access to. Internal findings are marked as internal in the report so you know what's shareable.
Can it ask people on my team questions?
Yes. When a sub-question is answerable only by a colleague, the agent sends them an information request and weaves the answer into the report, attributed.
How is this different from asking ChatGPT?
A chat answer draws on training data and a handful of searches, and it forgets your company between sessions. This workflow verifies claims across sources, includes your company's own knowledge and history, cites everything, and files the findings so they compound.