Nango vs Luminai
Side-by-side comparison of AI visibility scores, market position, and capabilities
SF YC W23 unified API integration infrastructure at $1.2M revenue 2024 with 8 employees; 400+ APIs, 600+ pre-built integrations with custom code for OAuth/rate-limiting/pagination competing with Merge for developer integration platform.
About
Nango is a San Francisco-based integration infrastructure platform — backed by Y Combinator (W23) with $1.2 million in annual revenue in 2024 — providing developers and engineering teams with a unified API platform that abstracts the complexity of building, maintaining, and scaling product integrations, offering 400+ pre-built API connectors, 600+ ready-to-use integration templates, and a custom integrations-as-code framework that handles OAuth, rate limiting, pagination, error handling, and data normalization across every third-party API a product connects to. Founded in 2022 and operating with an 8-person team, Nango replaces the custom integration code that engineering teams write and maintain for every API connection their product requires.
US YC W20 enterprise workflow automation for insurance/financial services/govt at 16% handle time reduction and 60% faster onboarding; $58.
About
Luminai (formerly DigitalBrain) is a United States-based enterprise workflow automation platform — backed by Y Combinator (W20) with $58.1 million in total funding including a $33 million Series B in March 2025, a $20.7 million Series A in March 2022, and a $3.4 million seed from investors including Peak XV Partners, General Catalyst, Underscore VC, Craft Ventures, and Avra — providing mission-critical industries (insurance, financial services, healthcare, and government) with AI-powered workflow automation that handles complex, multi-step operational tasks end-to-end, reducing average handle time by 16%, cutting customer service agent onboarding time by 60%, and saving 25+ hours per month per agent. Founded in 2018, Luminai's AI automation platform deploys in enterprise environments where regulatory requirements and operational complexity make fully automated AI agents impractical without human oversight integration.
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