How ideas are researched and assessed

We start with problems people appear to have, not random product ideas. We look for repeated signals, group them into a clear problem, research the opportunity and only then propose and score a product worth testing.

Where researched ideas come from

Daily Product Idea does not begin by asking AI for random startup ideas. We first look where customers, professionals, operators and business owners discuss real work: repeated questions, complaints, workarounds, requests and relevant market changes. When a promising signal appears, we look for the same underlying problem across related communities and other evidence sources. Similar signals are grouped into a clear problem. AI can then help propose a focused product, research the opportunity and structure the assessment. Every researched idea is reviewed before publication, and evidence is kept separate from assumptions.

1. 1Observe focused customer and professional audiences
2. 2Find and group repeated problem signals
3. 3Expand promising problems across related communities and sources
4. 4Propose a focused product
5. 5Research the opportunity
6. 6Score the evidence
7. 7Review it
8. 8Publish one idea

Subscriber counts, upvotes and comment counts are useful context, but they never stand in for repeated independent evidence. A problem raised once in a large community is weaker evidence than the same problem raised repeatedly across several smaller ones.

Score dimensions

Score bands

• 80–100: Strong opportunity, still requires testing
• 65–79: Promising
• 50–64: Mixed
• 35–49: Needs a sharper angle
• 0–34: Not currently recommended

Scoring happens after problem discovery, clustering, candidate generation and research — not independently of it. Where evidence is weak, conflicting or absent, the score notes say so explicitly.

Evidence levels

Illustrative · Limited · Indicative · Supported · Direct

Only Direct evidence can justify the word "Validated," and the direct evidence must be described alongside the idea.