What is Real Problem AI?

Real Problem AI is a curated directory of 200+ 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 200+ 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 does every Indian CA lose September to matching Tally invoices against GSTR-2B by hand?

Indian CAs match Tally purchase ledgers line by line against GSTN's GSTR-2B, chasing ITC mismatches on supplier invoices filed in the wrong month, and ClearTax only flags without resolving.

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

What is the “Why does every Indian CA lose September to matching Tally invoices against GSTR-2B by hand?” problem in 2026?

Indian CAs match Tally purchase ledgers line by line against GSTN's GSTR-2B, chasing ITC mismatches on supplier invoices filed in the wrong month, and ClearTax only flags without resolving.

Who has this problem?

Indian CA running a 200-client compliance practice.

Evidence this problem is real

“Every September I have 80 clients and each one has 200 to 2000 invoices where 2B does not match Tally. ClearTax shows me the mismatch list but I still have to email each supplier asking why they filed our invoice in the wrong month.”

Sourced from CAClubIndia forum and r/IndiaTax recurring GSTR-9C reconciliation threads.

Existing players in this space

ClearTax GST — Shows mismatches but does not auto-draft supplier follow-ups or post Tally rectification.
TallyPrime — Ledger side only, no GSTN reconciliation logic.
Zoho Books — Weak on multi-client CA workflow.

What existing players are missing

An agent that ingests Tally XML plus GSTR-2B JSON, classifies each mismatch (wrong month, wrong GSTIN, missing invoice, rate diff), drafts WhatsApp and email follow-ups, and queues rectification journal entries back into Tally.

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

Tally XML plus GSTN API ingestion
LLM mismatch classifier with Indian GST RAG
WhatsApp Business API for supplier chase
Tally rectification entry writer