The wrong time to hire with David Park, Narada

Large Action Models achieve 99.99% reliability

Unlike general AI models that deliver 60-80% accuracy, Narada's UC Berkeley research-based system provides the consistency required for healthcare and financial enterprise workflows.

Agentic automation without backend integration

The platform visually reasons about screen elements to execute complex multi-step processes like 'hire to retire' by mimicking human clicks and scrolling, eliminating the need for API connections or code changes.

Zero data retention with on-prem options

Narada stores no customer data and offers fully air-gapped deployments for security-conscious financial and insurance companies.

The 'open wallets' validation test

Park emphasizes that polite feedback is meaningless; real product-market fit is proven only when customers actually pay money, as people won't tell you your 'baby is ugly.'

1,000 customer conversations before fundraising

The team spent over a year conducting nearly a thousand customer calls to identify last-mile problems and high-value use cases before raising institutional capital.

Natural language enables horizontal adaptability

Users describe workflows in English or demonstrate them manually, allowing the system to adapt to niche enterprise variations without hardcoded configurations that constantly break.

Bootstrap until unit economics prove out

Park avoided premature VC scaling because excess capital removes the friction that forces customer-centricity; they raised only after demonstrating $10 return per $1 invested.

Validate investors through contribution

Lead investor Bang Shukla from Monavista Capital provided months of customer introductions and strategic advice before Narada accepted his funding, ensuring proven value before joining the cap table.

Build a lean 'building machine' first

Drawing from his first company, Park advocates staying 'poor and hungry' to maintain focus on revenue-generating features rather than burning cash on premature hiring or scale.