How Rappi Scales Experimentation with Feature Flags and AI Analytics

How Rappi scales experimentation with feature flags and AI analytics

Imagine trying to juggle countless customer interactions across different devices and platforms without dropping the ball. That's the challenge Rappi faced as they grew. They needed to deliver quick, consistent experiences to their users, and the solution lay in embracing an online experimentation mindset. This approach allowed them to navigate the complexities of large-scale operations.

By setting up strict A/B tests and establishing clear KPIs, Rappi ensured that their experiments were not just random shots in the dark. The team codified their processes, and their data leaders championed a culture of data evangelism. This alignment was crucial for getting everyone on the same page and driving meaningful results.

How Rappi overcame experimentation hurdles

To safely roll out new features, Rappi turned to feature flags. These handy tools let them stage releases without the chaos. Ownership of these flags was shared across teams, preventing any one person from having too much control. Paired with robust guardrails, feature flags ensured stability and consistency during deployments.

Quality assurance at scale was another hurdle Rappi tackled head-on. They used robots and computer vision to catch issues fast, maintaining real-world parity. This tech-savvy approach kept their user experience smooth, even under heavy loads. As a result, Rappi could test AI and UX updates swiftly across various user journeys, using live signals to iterate on models and prompts.

Integrating robotics for QA breakthroughs

Rappi’s innovative use of robotic arms transformed their QA process. By simulating real-user actions, they slashed manual QA time and minimized the risk of missing bugs. Computer vision stepped in to handle layout checks, spotting inconsistencies immediately so teams could fix them before users even noticed.

This blend of robotics and vision-based QA created a faster feedback loop. It freed up valuable time for engineers to focus on building features rather than chasing down minor visual bugs. Automated QA allowed them to catch subtle UI issues, test edge cases, and quickly adapt to layout changes.

Scaling predictive analytics for personalization

Rappi harnessed the power of predictive models to personalize user experiences. These smart algorithms improved courier routing and matched users with relevant merchants, saving time and resources. With each interaction, the system learned and adapted, offering more accurate recommendations.

The real-time data collection enabled Rappi to test new features swiftly. Fast experiments and quick updates to models meant they could refine recommendations on the fly. This agility resulted in practical improvements: faster deliveries, better matches, and more efficient operations.

Sustaining a culture of ongoing improvement

Cross-team engagement was key to keeping Rappi’s rollouts in sync with company goals. By inviting feedback early, they fostered shared ownership and better results. Consistent documentation ensured new engineers could ramp up quickly, while regular clean-ups prevented technical clutter.

They maintained a habit of updating docs and removing unused toggles, which made future changes fast and safe. This streamlined process minimized friction as the team expanded, ensuring that everyone stayed on track as features scaled.

Closing thoughts

Rappi's journey shows how embracing experimentation, automation, and analytics can transform a business. By leveraging feature flags, robotics, and predictive models, they achieved efficiency and consistency across the board. For more insights, explore their engineering lessons or dive into the Marvik case study.

Hope you find this useful!