We are pleased to announce QuanticMind has selected Split’s feature flag and experimentation platform to streamline development work and deliver innovation faster.
As a growing cloud-based predictive ad management platform, QuanticMind needed to aggressively increase their release cadence, but they saw risk in rapidly releasing new features across their diverse enterprise customers. Their internally-built feature flagging system wasn’t robust enough to meet their needs at scale, which meant diverting engineering resources to make the necessary upgrades. Building new capabilities such as audit trails for changes made to feature flags or establishing access control would take months of effort.
Their engineering team also needed a less cumbersome branching strategy. Reliably merging branches of code into the main branch was a big issue that hindered release velocity, often causing rewrites of existing code to accommodate a new feature.
Split helped QuanticMind separate code deployment from feature release, letting them press the gas pedal on release cadence while gaining full control over the customer exposure to new functionality.
“Split has transformed the way that we deliver software. Granular targeting of features to customers has made us more responsive, and data from Split gives us an audit trail in case any issues arise.”
Engineers now put all new code behind a feature flag, making every feature rollout an experiment to ensure production stability. They have also significantly improved their branching strategy to reduce the risk of production failure. Code is incrementally merged into the trunk and kept dark from customers until fully functional and thoroughly tested.
The engineering team works hand-in-hand with the Product Management and Quality teams to define feature flags and establish rollout plans. With Split, QuanticMind can safely phase in new functionality across their customer base.
QuanticMind has achieved the following benefits from using Split:
- Reduced their release cadence from 3 months down to a high-velocity continuous delivery model
- Eliminated a 1 week delay of new feature rollouts with an improved branching strategy
- Avoided 3 months of engineering effort to update an in-house feature flagging system
Learn more by reading QuanticMind’s customer story.
Stay up to date
Don’t miss out! Subscribe to our digest to get the latest about feature flags, continuous delivery, experimentation, and more.
Feature launches in leading engineering teams increasingly look like a ramp rather than a one time switch, going through dogfooding, debugging, max power ramp, scalability and learning phases.
We’re excited to announce the release of dynamic configurations. You will now be able to attach configurations to your treatments to instantly change components of your features without needing an engineer to make any code changes.