Free resource from Aiversity

AI Startup Validator

Five steps to test whether your business idea can be built, validated, and scaled with AI — before you spend a dollar.

01Problem fit

02Feasibility

03Economics

04Moat

05Sprint

5 steps · 25 checkpoints · Takes 10–15 minutes

Step 1 of 520%

01

Problem-Market Fit

Is the problem worth solving with AI?

Before you build anything, make sure the problem is real and AI is the right solution.

Is the problem real?

Who has this problem? How many of them? How do they solve it right now? If you cannot name at least ten people who have described this problem to you — in their words, not yours — you are guessing.

The AI angle

Can AI solve this faster, cheaper, or more accurately than what people do today? Not theoretically — practically, with models and infrastructure available right now.

Red flag: "AI could do this" is not the same as "AI must do this." If the solution works equally well without AI, you are adding complexity for a marketing bullet point.

Validation checklist

I can name 10+ real people who have this problem and describe how they currently solve it AI solves this problem faster, cheaper, or more accurately — not just differently People are already paying money or spending significant time to solve this with existing tools The problem requires AI specifically — a rules-based system would not suffice I can describe the problem in one sentence a non-technical person would understand

0 of 5 checked

Step 2 of 540%

02

AI Feasibility Audit

Can you actually build this?

Test whether today's technology can deliver what you need.

What data does your solution need?

Every AI product depends on data. Where does yours come from? Do you have access to it? Is there enough? If your product requires proprietary data you do not have, that is your first blocker.

Build vs. buy

Which capabilities are commodities you access through APIs, and which are differentiators worth building custom? Most AI startups should buy 80% and build 20%.

What breaks when the model changes?

When the API provider raises prices 3x? When a capability gets deprecated? Your architecture needs to absorb these shocks, not shatter from them.

Validation checklist

I know exactly what data my product needs and have a realistic plan to get it I have identified which AI capabilities to buy (APIs) and which to build custom I have tested the core AI task with available models and confirmed acceptable results My architecture can survive an API price increase, model deprecation, or provider outage I can build a working prototype of the core AI feature in under two weeks

0 of 5 checked

Step 3 of 560%

03

Unit Economics

Does the math work with AI costs?

The equation most AI founders skip until it is too late.

API costs per transaction at scale

What does it cost to serve one user through your AI pipeline? Not at prototype scale — at 10,000 users. Token costs, compute, storage, embeddings. If you do not know these numbers, you cannot price your product.

Revenue per user minus everything

Revenue minus AI costs, infrastructure, and support equals your margin. For traditional SaaS, gross margins above 70% are standard. For AI products, 40–60% is more common — and many startups are operating at negative margins once they account for real costs at scale.

The free tier trap: A generous free tier with AI features can destroy your economics. Every free user consumes real compute. Unlike traditional SaaS where a free user costs nearly zero to serve, an AI free user can cost dollars per month.

Validation checklist

I have calculated per-user AI cost at current usage and projected it to 10x and 100x My target price produces gross margins above 50% after all AI and infrastructure costs My free tier has usage caps that prevent AI costs from exceeding acquisition budget I have a strategy to reduce per-unit AI costs over time Customers would pay my target price based on value delivered, not because "AI" is in the name

0 of 5 checked

Step 4 of 580%

04

Competitive Moat

What stops someone from copying you?

If anyone can call the same API, what makes you defensible?

The moat question

Your competitor can call the same API you call, often on the same day you launch. What stops them from replicating your product in a weekend? If the honest answer is "nothing," you do not have a business — you have a feature.

Types of moats

Data moats — proprietary data that improves your product and that competitors cannot easily acquire.

Workflow moats — deep integration into how customers work. The switching cost is the workflow they built around you.

Network effects — each additional user makes the product more valuable for every other user.

Speed moats — moving so fast that competitors are always 6–12 months behind.

The wrapper test: Are you building a product or a skin on someone else's API? If your value proposition disappears the moment a foundation model provider adds your feature natively, you are a wrapper.

Validation checklist

I can name at least one moat that is not "we use AI" My product would survive if the foundation model provider launched a competing feature tomorrow The product gets better with each user or each use — there is a compounding advantage A competitor could not replicate my core value in under 30 days with access to the same APIs I pass the wrapper test — my product delivers value beyond what the raw AI model provides

0 of 5 checked

Step 5 of 5100%

05

Validation Sprint

The 30-day test

Four weeks from idea to informed decision.

Week 1: Smallest possible prototype

A functional demonstration of the core AI capability. Use APIs, no-code tools, scripts. The goal is to prove the AI can do the thing, not to build the business.

Week 2: 10 real potential customers

Not friends. Not other founders. Ten people who have the problem. Watch them use it. Do not explain. Note where they get stuck and whether they ask "can I keep using this?"

Week 3: Measure what matters

Are they using it more than once? Would they pay? Did they share it? Usage data and willingness to pay are the only signals. Compliments are noise.

Week 4: Decide

Proceed — customers used it, would pay, economics work. Build the real thing.

Pivot — they liked the AI but used it for something else. Follow them.

Kill — they did not use it or would not pay. Killing in 30 days is a success — it saved you six months.

Validation checklist

I can build a working prototype of the core AI feature within one week I have access to at least 10 potential customers who will test and give honest feedback I have defined specific, measurable success criteria — not "positive feedback" but numbers I am prepared to kill this idea at the end of 30 days if the data does not support it I have a clear proceed/pivot/kill framework and know which signals trigger each decision

0 of 5 checked

Your results

Validation Score

0 / 25

checkpoints completed

0 – 8: Early stage — lots of unknowns. Start with Step 1. Talk to more people. Understand the problem before you build the solution.

9 – 16: Making progress. Focus on the unchecked items. The steps with the fewest checks are where your attention should go next.

17 – 21: Strong foundation. Time to build. The unchecked items are your risk register. Address them methodically.

22 – 25: Ready to execute. Go. You understand the problem, the technology, the economics, and the landscape. Stop planning.

Problem-Market Fit0/5

AI Feasibility0/5

Unit Economics0/5

Competitive Moat0/5

Validation Sprint0/5

This validator is one tool. The full program is 38 courses.

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