Evolv’s solution revolves around an AI-driven SaaS platform that allows launching multiple UX experiments at a scale never possible before. Evolv started in 2019 and is the result of a technology spin off from Sentient Technologies who spent the better part of decade as an AI research and development firm. That team incubated some innovative algorithms which are now included as a building block in Evolv’s AI-driven optimization platform. Initially, we provided our service to Sentient Technologies; after Sentient sold the majority of its business to Cognizant, Evolv was born from the intellectual property of the offering previously called Sentient Ascend.
Maintaining and upgrading such a robust system requires significant development resources. Since the Redwerk team had already worked with Sentient Technologies and was familiar with the platform’s business logic, Evolv decided to continue our partnership.
Evolv works closely with their clients to define their optimization strategy, generate a constant source of new ideas to be explored, and leverage their proprietary AI to serve progressively better customer experiences and find the best customer journey. Evolv leverages evolutionary algorithms (a branch of AI that uses the principles of natural selection) to determine which combination of different improvements being tested makes up the winning design. An Evolv project represents the long-term optimization effort of a given user journey.
Evolv uses an ensemble of machine learning algorithms and statistical models to gain an in-depth understanding of user behavior and variant performance. Evolv also uses their own UX research to identify areas for improvement and outline multiple changes and their variations that can positively impact conversion. This approach allows Evolv to validate more with orders of magnitude, less traffic, and more robust results.
From there, the UX improvements are coded and tested in Web Editor and then exported to Manager for the actual release. Once the experiment is launched, each variant’s performance is evaluated in relation to the control candidate (original version) in the standard A/B testing mode, with the worst-performing candidates automatically excluded. Once the platform has aggregated a sufficient amount of traffic to move to the evolution phase – EVO – it starts combining variants from different variables using an active learning approach. Because the platform reacts to customer feedback in real-time, it continuously trains its machine learning model to predict the best-performing combinations within a short time. Having detailed statistics on each combination, the client clearly sees what UX changes are driving results toward the target KPI.