Use case · Research

Check whether anyone wants it before you build it

To validate a market with AI agents, give your Brainwrite Chief of Staff the idea, the customer, and the problem it solves. It sends one bot to read public discussions where buyers describe the problem, one to check search demand and competitors, and one to look for reasons it might fail. You get evidence for and against, with sources, and decide what to test next.

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Market validation

I want to build a booking and reminder tool for independent dog groomers. Check whether groomers complain about no-shows and booking in public forums, how many people search for groomer booking software, which tools they use now, and the strongest reasons this might fail. Cite everything.

Your Chief of Staff put a team on it

Nova· Chief of Staff
Atlas· Customer voice researcher
Sage· Demand and competitor analyst
Dash· Skeptic

Assumption 1, no-shows hurt: supported; 23 public posts from groomers in the past year, 6 quoted
Assumption 2, they will pay: weak; most threads recommend free or general booking tools
Search demand: modest volume for groomer booking terms; related questions listed (Semrush)
Skeptic: three established salon tools already offer reminders; differentiation unclear

DoneWhether the evidence is strong enough to keep going waits for you. Nothing was sent.

An illustrated example. Your team works on your own files and apps.

A marketing team sketches a campaign on a whiteboard while illustrated AI agents Sage, Atlas and Nova join the discussion.

The job

Doing it yourself, and handing it off.

Doing it yourself

Most ideas are validated by asking friends, who are kind, or by building first and hoping. Real evidence is out there: people complaining about the problem on forums, search volume for the words they use, competitors who tried and pivoted. Gathering it takes days of reading, and it is tempting to stop at the first post that agrees with you. The evidence against the idea is the part that rarely gets collected.

What the team does

Break the idea into assumptions you can check
Find public posts where buyers describe the problem, with links and dates
Check search demand and related questions for the terms buyers use
List competitors and alternatives, and the strongest case against the idea

What waits for you

Whether the evidence is strong enough to keep going
Which test to run next and how much to spend on it
Anything that involves contacting people or paying for data

The team

Who's on it.

Your Chief of Staff picks the specialists the job needs. Each has its own role, instructions, model, and app access.

NovaIllustrated example
Chief of Staff
Turns the idea into testable assumptions and writes the final evidence summary.
AtlasIllustrated example
Customer voice researcher
Reads public discussions, such as Reddit threads, for people describing the problem in their own words, with links.
SageIllustrated example
Demand and competitor analyst
Checks keyword volume and related questions in Semrush and reads competitor pages in its built-in browser.
DashIllustrated example
Skeptic
Looks for reasons the idea could fail, such as weak demand, strong free alternatives, or a buyer who cannot pay.

How it runs

A run, step by step.

01
State the idea as assumptions
Tell the Chief who has the problem, how often, what they do today, and what they would pay.
02
Split the research
The Chief delegates each assumption to the right bot. Each returns evidence with links.
03
Weigh both sides
The skeptic's findings sit next to the supporting evidence, not in a footnote.
04
Pick the next test
Read the summary and choose the cheapest next test, such as interviews, a landing page, or a pre-sale.

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What you'll connect

Apps this job uses.

FAQ

Questions about Market validation

Can AI really validate a business idea?

It can gather evidence quickly: public complaints about the problem, search demand, competitors, and reasons the idea might fail. That is not the same as customers paying. Use the summary to decide which real-world test to run next, such as interviews, a landing page, or a pre-sale.

Where does the evidence come from?

From public sources the bots read in their built-in browsers and from apps you connect, such as Reddit for discussions and Semrush for keyword data. Every claim comes with a link. Semrush uses your own Semrush account and API units, and the bot can check how many units remain.

How do I stop it from just agreeing with me?

Give one bot the explicit job of arguing against the idea, and ask the Chief to show its findings next to the supporting evidence. You can run the skeptic on a different model from the others, and save the rule in memory so every validation includes the case against.

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