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 can't real-estate brokers search their own WhatsApp history for a deal they lost 3 months ago?

Indian residential brokers run their entire book on WhatsApp; when a buyer comes back, they have no way to find the original conversation, the listings shown, or the price quoted.

Category: PropTech / Real Estate · Trend: LLM · Opportunity score: 8.3 / 10

What is the “Why can't real-estate brokers search their own WhatsApp history for a deal they lost 3 months ago?” problem in 2026?

Indian residential brokers run their entire book on WhatsApp; when a buyer comes back, they have no way to find the original conversation, the listings shown, or the price quoted.

Who has this problem?

Indian residential / commercial real-estate brokers (1-15 person teams) running deal-flow primarily through WhatsApp.

Evidence this problem is real

“A client from January called me yesterday. I have no idea which 11 properties I sent him.”

Sourced from MagicBricks / NoBroker partner forums + broker WhatsApp groups

Existing players in this space

MagicBricks / 99acres — Listing-first; not a broker CRM with chat memory.
WhatsApp Business labels — Manual; brokers don't use them.
No one — Auto-extracts deal context from WhatsApp threads + makes it searchable.

What existing players are missing

A WhatsApp-first broker CRM that ingests every chat, extracts buyer-budget-area-shortlist context, and lets the broker ask 'what did I send Rajesh in January?' in plain Hindi/English.

How Real Problem AI scores this opportunity

Aggregate score: 8.3 / 10. Four-axis rubric:

Problem severity: 8 / 10
AI feasibility today: 9 / 10
Market signal: 8 / 10
Competition gap: 8 / 10

How to build a solution: stack hints

WhatsApp Cloud API + multi-device
LLM extractor
Vector DB
Voice / text Q&A