What is Real Problem AI?

Real Problem AI is a curated directory of 100 real problems worth building an AI startup around. Each problem is scored honestly on severity, AI feasibility, market signal, and competition gap. We're for builders looking for what to build, not for ethics debates about AI.

What is the difference between Real Problem AI and AI ethics or AI risks indexes?

Real Problem AI catalogues problems AI can solve — productive, commercial opportunities for builders. AI ethics indexes catalogue problems with AI itself (bias, environmental cost, copyright, consciousness). Different intent, different audience: founders and indie hackers vs researchers and policy folks.

What should I build with AI in 2026?

Look for problems with high severity (it costs people hours, sleep, or revenue every week), high AI feasibility (LLMs, vision or voice are great at this today), strong market signal (Reddit threads + willingness to pay), and a clear competition gap (incumbents miss the same obvious thing). Real Problem AI scores 100 such problems for you on those four axes.

Where do Real Problem AI's startup ideas come from?

From three sources, scanned each cycle: 1) Reddit threads where people describe a real friction in their own words, 2) podcasts and talks of founders young builders trust (Nikhil Kamath, Aman Gupta, Ankur Warikoo, Raj Shamani, Andrej Karpathy, Greg Isenberg), and 3) a continuous 1,000+ source scan covering forums, app store reviews, regulator filings, and field interviews.

Is Real Problem AI free?

Yes. Browsing every problem, opening every score breakdown, raising your hand on a co-founder match, and the founder vault are all free. There is no paywall.

How is each problem scored?

Four axes, each 1-10, weighted: Problem Severity (30%), AI Feasibility (25%), Market Signal (25%), Competition Gap (20%). Weighted average is the Opportunity Score on each card. Only problems clearing 7.0+ make the live list.

Why cant Indian SaaS pre-PMF pull its own cohort retention without an analyst?

Pre-Series-A Indian DTC and SaaS founders cannot read their own retention chart without hiring an analyst, so they pitch acquisition numbers and only learn retention is broken at the next diligence.

Category: Marketing, Sales & CRM · Trend: LLM · Opportunity score: 8.0 / 10

What is the “Why cant Indian SaaS pre-PMF pull its own cohort retention without an analyst?” problem in 2026?

Pre-Series-A Indian DTC and SaaS founders cannot read their own retention chart without hiring an analyst, so they pitch acquisition numbers and only learn retention is broken at the next diligence.

Who has this problem?

Founders of Indian DTC and SaaS pre-Series-A, no in-house data hire.

Evidence this problem is real

“The biggest mistake Indian startups make is ignoring product-market fit. Founders pitch acquisition numbers because they cannot articulate retention.”

Sourced from Aman Gupta on Figuring Out FO105 (link)

Existing players in this space

Mixpanel / Amplitude — Need event schema design; non-technical founders never get past setup.
Metabase / GoodData — BI tools, not opinionated cohort views.
June.so — Closer fit but US-priced and English-only schemas.

What existing players are missing

An opinionated, INR + GST aware cohort retention dashboard that ingests Shopify + Razorpay + PostgreSQL out of the box. One screen, no schema design.

How Real Problem AI scores this opportunity

Aggregate score: 8.0 / 10. Four-axis rubric:

Problem severity: 8 / 10
AI feasibility today: 8 / 10
Market signal: 8 / 10
Competition gap: 7 / 10

How to build a solution: stack hints

Shopify/Stripe/Razorpay connectors
DuckDB / ClickHouse for cohort SQL
Opinionated v1 dashboard (next.js)
Slack/email weekly digest