Palomma

Palomma launched their MVP with a single AI agent focused on inbound leasing inquiries—automating responses and qualification for property managers drowning in lead volume. ​​‌‌‌‌‌‌‌​‌‌​​‌​​​​​​‌‌​‌‌‌​​​‌‌They deliberately excluded outbound sales and collections features that seemed logical but required deeper operational integration. This narrow focus shipped in eight weeks. The speed proved critical. Early customers immediately saw response time drop from hours to minutes, and qualified leads increased 40% within the first month. Palomma resisted the temptation to build a "complete platform" upfront, instead validating that property managers would actually adopt AI agents for their messiest workflow problem. This constraint-driven approach revealed something unexpected: customers wanted embedded payments alongside collections automation. Rather than treating this as scope creep, Palomma built it into their second agent, informed by real usage patterns. The execution discipline—shipping narrow, learning fast, then expanding deliberately—generated the validation signals that attracted their Series A. Their restraint became their competitive advantage.