MarketMinute.ai

Most founders launch products based on gut feeling or expensive consultant reports that take weeks to produce. Generic AI tools hallucinate market insights with no real data. Traditional market research costs $5K–50K and requires interviewing dozens of users manually.

Meanwhile, millions of authentic product discussions happen daily on Reddit, Twitter, and YouTube — but there's no way to synthesize this goldmine of insights at scale. Founders either ignore real user voices or spend weeks manually reading forums.

MarketMinute is an AI-accelerated research engine that turns messy product descriptions into comprehensive 10,000+ word GTM strategies with multi-platform citations — in under two minutes. Unlike generic AI tools, every claim is backed by real Reddit posts, tweets, or YouTube comments with source URLs.

The system uses Perplexity to understand vague inputs and dynamically route searches to the right communities, Apify scrapers pull authentic discussions in parallel, and Claude synthesizes everything into a structured strategy with confidence scores per section.

Key decision: API-first serverless architecture (Next.js on Vercel) instead of no-code workflow tools — this enables true parallel scraping across 4+ platforms simultaneously while keeping unit costs under $1 per strategy. Built for scale from day one.

01

Intent understanding

Perplexity analyzes your product blurb (even if messy or vague) and extracts domain, target audience, geo-market, and derives related topics with 70%+ confidence.

02

Dynamic hotspot discovery

Based on your product type, the system identifies the top 3–4 most active subreddits, Twitter hashtags, and YouTube channels where real discussions are happening right now.

03

Parallel multi-platform scraping

Apify actors simultaneously pull 100+ Reddit posts, tweets, and YouTube comments from those hotspots in under 90 seconds — all running in parallel via serverless functions.

04

AI compilation rooms

Three specialized Claude instances process the data: C-Room 1 analyzes expert articles, C-Room 2 validates web sources, C-Room 3 extracts pain points and Jobs-To-Be-Done from social conversations with sentiment scoring.

05

Strategy generation

A final Claude instance consolidates all insights into 9 sections — Executive Summary, Target Personas, Messaging Framework, Channel Strategy, Pricing, Roadmap, Success Metrics, Quick Wins, Risks — with multi-source citations and confidence scores. Every claim is linked to a source URL.

API-first architecture enables parallel execution without bottlenecks. Rejected Make.com and n8n workflow tools — they can't handle concurrent requests at scale. Every component chosen for serverless scalability and sub-$1 unit economics.

Phase 1 (Weeks 1–2) — Completing Reddit-only MVP with full citation system and confidence scoring. Testing with 5 verticals (SaaS, FinTech, HealthTech, EdTech, Creator Economy) to validate pipeline quality before expanding.

Phase 2 (Weeks 3–4) — Adding Twitter and YouTube scrapers for multi-platform synthesis. Building aggregation layer with fuzzy deduplication to handle cross-platform content overlap. Target: 3+ sources cited per insight.

Phase 3 (Month 2) — Sentiment trend analysis (track how user opinions change over time), a competitive intelligence dashboard (monitor competitor mentions), and launch readiness scoring (calculate market timing based on signals). Exploring a white-label option for agencies and VCs analyzing portfolio companies.