The Agile Data Product Cycle
From a high-risk gamble to a validated, iterative process.
The Traditional Dilemma: A Risky Divide
Deep R&D
Heavy upfront investment in perfecting algorithms before user contact.
Quick Release
Rushing an application out to see if it solves a core need.
"Builders of data products face an inherent tension between how much to invest in the R&D upfront and how quickly to get the application out..." - HBR
The Solution: A Unified Ecosystem
KreateBots.com
Uses AI to transform raw data into intelligent, predictive signals.
KreateWebsites.com
Builds intuitive web interfaces to make data signals actionable.
ABExperiment.com
Validates both signals and interfaces with real-world user testing.
The Synergy Loop in Action
Hover over a stage
to see its role in the cycle.
Generate
Build
Validate
Refine
How the Cycle Works
This iterative loop ensures technology and user needs evolve together, reducing risk and building products people actually want and use.
Case Study: Churn Predictor
Generate Signal
AI analyzes user data to create a 'churn risk score'.
Build UI
Two competing dashboards are built to display the risk score.
Validate
A/B testing reveals one UI is 25% more effective at driving user intervention.
Refine
Feedback is used to improve the AI's suggestions, making the signal itself more valuable.