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 India tier-2 call centers still pay ₹2L/month for 30 agents handling the same 50 FAQs?

Indian SMB call centers spend ₹6-8k per agent per month answering the same 50 questions in Hindi/Tamil/Telugu/Bengali; voice AI options are all English-first.

Category: Others · Trend: Voice · Opportunity score: 8.5 / 10

What is the “Why do India tier-2 call centers still pay ₹2L/month for 30 agents handling the same 50 FAQs?” problem in 2026?

Indian SMB call centers spend ₹6-8k per agent per month answering the same 50 questions in Hindi/Tamil/Telugu/Bengali; voice AI options are all English-first.

Who has this problem?

Indian SMB call-centre operators (50-500 seats) serving DTC, BFSI and e-commerce in regional languages.

Recorded source context

Dataset source note: Sarvam shows the demos. We need a voice agent that actually closes a refund call in Telugu without a human.

This note may summarize the referenced material rather than quote it verbatim. Source label: BPO industry forums + Sarvam AI / Krutrim coverage, 2026.

Existing players in this space

Vapi / Bland.ai: English-first; weak on Indian regional languages and accents.
Sarvam / Krutrim: Foundation models; not packaged as drop-in IVR replacement.
Ozonetel: India-native cloud telephony but agent stack is human.

What existing players are missing

An opinionated voice-agent stack pre-tuned for the 5 largest Indian languages, sold as a drop-in replacement for tier-1 agents in BFSI/DTC, billed per-resolved-call.

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

Sarvam / Krutrim TTS+ASR
Asterisk / Twilio India SIP
Workflow engine
Quality scoring + escalation