Introduction

Rippling broke every old B2B rule to build 25+ products in 9 years. Here is how they did it fast, smart, and all together.

What's the problem it solves?

Most B2B companies build one tool at a time, growing slowly while customers juggle dozens of disconnected apps. Rippling fixed this by building a single platform that connects everything, removing data silos and endless tool switching.

Quick Summary

Rippling calls itself a “compound startup” instead of a “point solution” company. Instead of creating one product and expanding later, they built a shared platform with reusable building blocks that power many products across HR, IT, and finance. This lets customers use one system for all employee needs, fast, simple, and automated.

Their culture is built around three broken “rules.” First, they do not over delegate. Leaders “go and see” the problems themselves to understand customers deeply. Second, they do not build one thing at a time, they run an “AND culture” where many connected products grow together. Third, they move with urgency using their “MMDD culture,” where every project must have a clear deadline and owner.

This focus on operations, not just ideas, lets Rippling move faster than typical SaaS companies. Their platform structure also gives them a huge edge in AI, since all employee data is unified, AI tools can automate work across multiple products easily. Point solution companies cannot do this because their data is stuck in silos.

Key Takeaways

• Point solutions are fading, platforms that connect everything are winning.
• Rippling’s “compound startup” model uses one shared data foundation for all its products.
• Leaders stay close to customer problems (“go and see”) instead of managing from dashboards.
• The “AND culture” builds parallel products instead of waiting for one to finish.
• The “MMDD culture” keeps everyone accountable with specific due dates and ownership.
• Their AI advantage comes from structured, shared data across all tools.

What to do

• Design your first product as part of a future platform, not a one off tool.
• Build shared systems (data, permissions, analytics) that new products can reuse.
• Stay close to real users, do not rely only on reports.
• Create cultural urgency with fixed deadlines (MMDD).
• Encourage leaders to own understanding, not just outcomes.
• Use integration and data structure to prepare for strong AI capabilities later.

‍