I Asked AI to Choose a Startup Niche. There’s Almost No Ready-Made Button

Some of you know that I’ve decided to run an experiment: build a zero-human company.

I don’t mean "I will never touch the business with my own hands ever". That’s fairy tales for landing pages.

The idea is different: find a niche where as much of the operational work as possible can be handed off to agents. Research, product, code, QA, onboarding, support, reports, part of sales. A human stays only where a decision needs to be made, not to transfer data from one spreadsheet to another.

And that’s where the first interesting question came up.

If a company is built on AI, who should pick the niche?

Well, yes. AI too.

I went to see what already exists. I didn’t "ask ChatGPT to come up with 10 ideas", I opened real tools: GitHub repos, agent frameworks, SaaS validators, Reddit pain point search tools, app review miners, all those fancy "AI co-founder" type tools.

I wanted to understand one simple thing: is it possible today to press a button and get a niche that you wouldn’t be embarrassed to work on.

Spoiler: almost none.

But it became clear what parts such a machine is made of.

First, GitHub

I intentionally didn’t start with SaaS services.

With SaaS it’s all clear: there will be a nice interface, a score, a gradient, "your idea is 87/100", a payment button, and the feeling that they just sold you confidence for $29.

GitHub is usually more honest. You can see what’s under the hood there: what agents are available, what data they require, where the scoring system is located, how the step chain is structured, how easy it is to wrap this into Codex or Claude Code CLI. This is a useful reality check.

Generators and critics

The first candidate is rhofkens/business-idea-multi-agent. This isn’t just a README with a prompt, it’s a proper application: an idea agent, a competitor agent, a critic, and documentation generation.

The keys are totally fine over there: an open-source AI tool without model keys would be way weirder than one with them. The actual adjustment needed is elsewhere. To use this as part of my workflow, it needs to be tweaked to work with the Codex or Claude Code CLI: give it my profile, strict criteria, pain points, and a separate run for the killer agent. Architecturally, it starts with idea generation rather than raw pain point hunting. In other words, the agent can come up with ideas, compare them, critique them, and format the output. But it doesn't start by going to Reddit, GitHub Issues, G2, App Store Reviews and bringing back blood.

The second one is ferdinandobons/startup-skill. It’s not a product at all, but a set of instructions for Claude Code: startup design, competitors, positioning, and pitches. The most useful part isn’t the "generate an idea" instruction, but the honesty protocol. Roughly: force the agent to ask uncomfortable questions and kill weak hypotheses.

That’s what I liked about it.

Because for picking a niche, you don’t need an "idea generator". There are already way too many ideas as is. You need a killer agent. The kind that looks at a nice-sounding niche and says: the customer base is vague, the budget is unclear, competitors are stronger, sales happen over calls, delivery is manual, kill it.

Research, pain points, and the product lifecycle

Next up are research engines: assafelovic/gpt-researcher and langchain-ai/open_deep_research. Both are useful if you need to gather sources and put together a research report. GPT Researcher looks like a straightforward, easy-to-use foundation for research. Open Deep Research is more powerful but also more heavyweight: it uses LangGraph, supports multiple model and search providers, MCP, and quality evaluation.

But it’s the same issue as before.

A research engine does not pick a niche. It aggregates and summarizes content. It requires an external layer: founder alignment, suitability for a zero-human model, target buyer, pricing, competitors, hypothesis-killing criteria, and a 72-hour test. Without these, you may end up with a polished report, but you will still have no clear idea what to build on Monday.

There was also the-wc/reddit-painpointer. This one is far more on point: it takes a subreddit, fetches posts and comments, filters for pain-point marker terms, and processes them via OpenRouter. The rest is straightforward: you plug in your Reddit API access credentials and OpenRouter key, set the target subreddit, and get a layer of live user complaints. For an article, this is a great example of not "AI came up with an idea", but "AI went out to look for pain points".

neondatabase/yc-idea-matcher is interesting as a standalone module: it compares an idea against companies that YC has already invested in. The logic is clear from the code: the idea is converted into an embedding, then pgvector searches for similar YC-backed companies in Neon. Upstash is included for rate limiting. This is a functional similarity-checking module, but it is not a niche validation tool. It answers the question "did I come up with something new, or just another variation of something that's already been pitched a hundred times?"

crshdn/mission-control is in a completely different league. Based on its code and documentation, this is not a niche selection tool, but an autonomous product operations system. The README outlines the Product Autopilot: you connect your product and repository, agents carry out research and generate ideas, you swipe through the ideas, and approved ideas are added to the build queue. Further down in the How the Pipeline Works document, the pipeline chain Builder -> Tester -> Reviewer -> Verifier is described, with a loop back to Builder if a test or review uncovers an issue. For a zero-human company, this is extremely close to what you need after selecting a niche. But it is not designed to select a niche from scratch.

In short, GitHub did not provide a ready-made machine, but individual components:

• research engine;

• pain point search;

• critic;

• competitor check;

• similarity check;

• product cycle.

There is no pre-assembled integrated system.

If you build it yourself, the resulting workflow looks like this: the seeker agent scouts for potential directions, the pain agent pulls in real user complaints, the competitor agent looks for pricing and alternative solutions, the destroyer agent debunks hypotheses, the evaluator ranks the results, and then the validation agent runs a 72-hour test.

A single AI agent is too lenient to pick a viable business.

Then I turned to SaaS

When evaluating ready-made services, I didn't focus on their landing pages, but on one practical question:

Can the tool take my profile and return not "10 SaaS ideas", but a niche that can be tested in the next few days.

The criteria were straightforward:

• whether it starts with the founder's profile or requires a pre-existing idea;

• whether it sources pain points from live, real-world sources;

• whether it shows competitors and their pricing;

• whether it can on its own identify reasons to kill a hypothesis;

• whether it provides a concrete first step, rather than just a generic rating.

Orbis: a compass, not a judge

First, I tested Orbis by inputting the following profile: CTO, AI coding, voice AI, automation, zero-human constraint, fast MVP, sales without lengthy calls, real pain point sources.

Orbis focused on voice AI for local service businesses. It provided a target customer profile, product options, rough price estimates, a list of competitors, and a list of risks. It sounded promising. The top option scored 85/100.

But then I took a closer look.

Tucked inside the report itself were reasons to kill the hypothesis: no defensible competitive advantage, oversaturated acquisition channels, local businesses may be reluctant to adopt humanless communication, high churn, and extensive custom delivery requirements. And yet a high score was displayed prominently at the top.

This is the classic flaw of AI validation tools. They can sound completely confident, even when they've already identified reasons to doubt the hypothesis.

The most useful takeaway from Orbis wasn't "build this". The useful takeaway was: dig into voice AI, but don't trust the initial assessment. Then I checked the market separately, and it became clear that the universal "AI receptionist for small businesses" is already a saturated space. Retell, Vapi, Bland, Synthflow, a ton of white-label portals, agencies on every corner.

But further down, we found a far more interesting pain point: agencies are already deploying voice agents, then manually tally up usage, minutes, failed calls, transfers to human operators, costs, margins, and client reports. This is no longer just "another AI receptionist". This is billing-level reporting for people who already sell these receptionists.

In other words, Orbis worked like a compass. It quickly pointed us in the right direction, but it shouldn't be the final judge.

PainPointer: Pain Radar

Next came PainPointer. I selected r/Entrepreneur, it scanned the posts and pulled out pain points: customer acquisition, pricing, marketing channels, fundraising compliance, and so on.

Is it decent? Yes.

Is it enough to pick a niche? No.

It's a radar. It shows "something is beeping here". The strong point of PainPointer is that it starts with actual user complaints, not hypothetical guesses. The weak point is that this kind of output on its own still needs to be turned into a full evidence pack: direct threads, quotes, context, frequency of mentions, and current workarounds.

PainOnSocial plays in a similar category: it searches for pain points on Reddit, pulls quotes and discussions with source links. GapRadar takes a different angle: instead of Reddit, it looks at negative reviews on App Store and Google Play. And this is an important point for the article. When searching for a niche, pain point search tools and review miners are often more useful than validators, because they start from people's dissatisfaction, not from the author's desire to get a nice "yes".

The most useful thing there is neither design nor assessment. The most useful element is the structure of your thinking. FoundryStart forces you to view a niche as a chain of checks:

• who is waiting;

• what is painful right now;

• where these people live;

• who is already operating in the same space;

• why competitors have not closed the gap;

• what will kill the idea;

• what the first test to run should be.

In this same example, it does not pretend the idea is perfect. In stress tests, it is stated outright that parts of this solution already exist: media kits, calculators, marketplaces, and brand platforms. It is separately noted that standard rate calculators can copy the visible feature over a single weekend. Then comes the standard death condition: if creators with active brand inquiries do not purchase the setup and do not remain on the subscription, the subscription should be discontinued and the offering narrowed.

This already resembles real-world practice.

Not "the market is huge, the opportunity is high".

But "here is the pain point, here is who is operating nearby, here is why you might fail, here is the first test".

Out of all SaaS tools, FoundryStart best fits the "niche first, then product development" format. But I still would not make it the sole judge. Its strong point is the initial pass and the framework it provides. Its weak point is the same risk as all polished AI reports: the text can sound more convincing than the actual evidence. So after using FoundryStart, a second layer of validation is still required: pain point research, source verification, competitor price checks, and first outreach to potential buyers.

And in this combination, it still looks stronger than the rest.

What I realized after all the test runs

Tools are not divided into "good" and "bad".

They are categorized by role.

There are direction generators. Orbis belongs to this category. They quickly provide the market, target buyer, and product angle. It is useful if you are stuck on a blank page. It is dangerous if you trust the assessment.

There are pain point research tools. PainPointer, PainOnSocial, GapRadar. They are closer to reality because they start with user complaints rather than a pre-formed idea. But they require sources, quotes, and direct links. Without these, they are just a neural network summary of Reddit content.

There are validation tools. Preuve, FounderFlow, DimeADozen. They can polish a concept, identify competitors, and generate a report. But they often evaluate an idea you have already come up with. That is useful, but it is not niche selection from scratch.

There are research engines available on GitHub. GPT Researcher, Open Deep Research. They are useful as an under-the-hood engine. But an engine does not choose the route.

There are also methodological layers. Startup Skill, critic agents, and honesty protocols. These are the important ones, because they force the model to not just generate ideas, but also challenge and dismantle them.

And then there is the "niche first" approach. Out of the services I have reviewed, FoundryStart best fulfills this role: founder profile -> multiple niche options -> stress tests -> plan -> first step.

Rough around the edges? Yes.

But the direction is correct.

Why "the AI picked my niche" still sounds like an overstatement

The main trap of all these services is that they sell the feeling that uncertainty has disappeared.

You open a report. It covers the market, competitors, user personas, pricing, a roadmap, and a valuation. Your brain relaxes: great, we can start building now.

You can't.

A niche does not start where the model writes "high demand". A niche starts where there is a repeatable paid process.

In my voice AI example, this became clear very quickly. "AI receptionist for small businesses" sounds like a market. But when you look at the competitors, it is a red ocean. Meanwhile, "monthly usage reports for agencies that already sell AI receptionists" is a workable process. It is narrow, boring, repeatable, with clear data and a clearly defined buyer.

Then I even built a small usage report generator for Retell: upload JSON/CSV files, it counts calls, billable minutes, failed calls, per-person transfers, provider costs, client invoices, margins, and generates a report that can be sent as a PDF.

This isn't a product. This is a test.

If agencies don't want to send anonymized exports to get this report, the hypothesis dies. And that's perfectly fine.

This is what most AI niche-finding tools are missing: they rarely get to the question of "what small artifact can validate this pain point in 72 hours".

FoundryStart in its public example at least tries to head in this direction: it lays out the first step of where to find potential users, what needs to happen for the idea to be viable.

My current niche selection toolkit

If I were putting together this workflow for my own use today, rather than for an article, it would look like this:

1. FoundryStart - get your first 3 niche ideas and a clear problem framing.

2. PainOnSocial or PainPointer - pull live user complaints and relevant links.

3. GapRadar - if the niche is centered around existing apps, check their negative reviews.

4. GPT Researcher or Open Deep Research - compile a package of relevant sources.

5. Startup Skill / custom killer agent checklist - eliminate weak options.

6. Manual review of pricing pages and competitor pages.

7. Build a small test artifact in a single evening.

8. First outreach to potential buyers: "send me your data, I'll deliver the first result for free".

Only after this can you say the idea is "roughly validated".

Not "validated".

Roughly.

Because real validation is when a person with the pain point gives you their data, time, or money.

What's next

I started with a naive idea: a zero-human company, meaning the AI would select the niche on its own.

After a couple of days it became clear: one AI can't do it. One service can't do it either.

But a combined workflow is already possible.