Reveal is an enablement platform that automates the creation and distribution of personalized, context-aware training content, such as guides and tooltips, directly within applications. This technology addresses the challenge of low user adoption and proficiency with digital tools by delivering timely, relevant support that enhances productivity and maximizes return on investment.
Funding
Funding not disclosed
Founders
Product
Problem
Many organizations struggle with low user adoption and inefficient use of digital tools due to inadequate training and support. Creating and maintaining relevant, up-to-date training content is a manual, time-consuming process, resulting in inconsistent user experiences and underutilization of software features.
Solution
Reveal is an AI-powered enablement platform that automates the creation and delivery of personalized training content directly within applications. By capturing user workflows and leveraging AI, Reveal generates guides, tooltips, and other contextual support materials tailored to individual roles and organizational needs. This just-in-time learning approach enhances user proficiency, accelerates software adoption, and maximizes the return on investment in digital tools. The platform simplifies content management, ensures consistency across channels, and proactively addresses user challenges as they arise.
Target Audience
Reveal targets enterprises implementing new digital tools, undergoing large-scale transformations, or seeking to improve the adoption and utilization of existing software.
Features
- Automated workflow capture to document processes and generate training content
- AI-powered content creation for guides, tooltips, and walkthroughs
- Personalized content delivery based on user roles and organizational context
- Multi-modal content distribution across various channels within applications
- Copilot feature providing in-app product expertise and guidance
- Knowledge Center for centralized access to all training materials
- Surveys to measure product effectiveness and user satisfaction