Discoverability pain: unify and rank scattered “best apps” lists into one index

Discoverability is a growing pain for both users and vendors: consumers, product managers, and the roughly 8 million businesses that spend on discovery tools don’t have a single reliable place to compare recommended apps because “best-of” lists are scattered, unstructured, duplicated and often biased. That fragmentation wastes time, inflates customer acquisition costs for niche app vendors, and forces buyers to reconcile inconsistent recommendations across blogs, newsletters and micro-influencers. You could build a unified index that ingests tens of thousands of public “best apps” lists, uses LLM-enabled extraction to canonicalize app entities, deduplicates mentions, and produces a multi-factor ranked score with provenance and filters by vertical, budget, and use case. Offer a searchable UI, embeddings-driven recommendations, a developer API for integrations, and paid tiers—SaaS subscriptions for SMBs and enterprise licensing for vendors and marketplaces. This is an attractive moment: the immediate addressable market is roughly $4.8B (8M paying entities × ~$600 ARR), LLMs now make large-scale parsing and normalization feasible, and app sprawl plus the creator economy are accelerating the supply of curated lists to aggregate. Competition is medium and fragmented today, leaving room for a high-quality neutral index to capture both users and B2B customers. To stand out you’ll need transparent, explainable scoring, strict source provenance, hybrid human+AI curation for quality control, and strategic partnerships with creators and platforms; the strengths are a defensible dataset and multiple monetization channels, while the main challenges are noisy source quality, legal/scraping risks, and the ongoing cost of maintaining freshness and trust.