Automated B2B prospect lists from public place data (fast, serverless)

Many B2B sales teams—roughly 3.0 million globally spending about $6,000 per year on prospecting and enrichment tools—struggle with stale, generic lists, high procurement costs, and poor local or real‑time targeting for field or regional campaigns. The result is wasted outreach, low conversion rates, and slow reactions to changes like new store openings or market exits that matter most for territory sales and SMB targeting. You could build a serverless, API‑first pipeline that harvests public place data (maps, business registries, directories), enriches and deduplicates records with AI models (title inference, firmographics, contact enrichment), and exposes fast exports and CRM integrations with geo and foot‑traffic signals. The architecture emphasizes near‑zero ops, pay‑per‑request pricing, sub‑second lookup latencies for interactive list building, and automated data freshness so small teams can iterate quickly and keep list accuracy high. This is an attractive moment: the total addressable market is about $18.0B, market score 92/100 and revenue potential 88/100, while third‑party business data APIs and serverless platforms have materially lowered infrastructure and time‑to‑market costs. AI models now reduce manual cleaning work, making it feasible for a small, capital‑efficient team to deliver comparable quality to incumbents at lower marginal cost. To stand out you’ll need rigorous data sourcing and quality control, a UX that makes geo‑driven list building trivial, and an API/plug‑in ecosystem for CRMs and engagement tools; offering transparent freshness SLAs and privacy/compliance safeguards can be a practical differentiator. The real challenges are high competition and potential dependency on rate‑limited or licensed sources, so early effort must focus on defensible supplier contracts, scalable dedupe/enrichment IP, and distribution partnerships rather than just product features.