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 do Indian SMBs still retype 200 vendor invoices into Tally every month?

An accountant at a typical Indian SMB sits down on the 28th and re-types 100-400 vendor invoices arriving over WhatsApp, email and paper into Tally for the GST filing, with no first-pass triage.

Category: FinTech · Trend: LLM · Opportunity score: 8.5 / 10

What is the “Why do Indian SMBs still retype 200 vendor invoices into Tally every month?” problem in 2026?

An accountant at a typical Indian SMB sits down on the 28th and re-types 100-400 vendor invoices arriving over WhatsApp, email and paper into Tally for the GST filing, with no first-pass triage.

Who has this problem?

Indian SMBs (₹1Cr-₹50Cr revenue) whose finance person handles 100-400 vendor invoices a month.

Recorded source context

Dataset source note: Indian startups should build vertical AI on top of global infra rather than chase foundation models. The unsexy back-office is wide open.

This note may summarize the referenced material rather than quote it verbatim. Source label: Raj Shamani at India AI Impact Summit 2026 (reference).

Existing players in this space

Tally Prime: Ledger system; no inbound invoice triage.
ClearTax: Filing-side automation; not at the inbox/WhatsApp boundary.
Zoho Books: Full ERP; too heavy for a 5-person finance team.

What existing players are missing

An agent that reads invoices off WhatsApp + Gmail, validates GSTIN, flags input-credit mismatches, and pushes a clean entry to Tally before the human touches it.

How Real Problem AI scores this opportunity

Aggregate score: 8.5 / 10. Four-axis rubric:

Problem severity: 8 / 10
AI feasibility today: 8 / 10
Market signal: 9 / 10
Competition gap: 7 / 10

How to build a solution: stack hints

WhatsApp Business API
Gmail/IMAP
OCR + LLM for invoice parsing
GSTN validation API
Tally TDL/XML push