Company Background:
Flabee, operates in the healthcare technology and women’s wellness sector as a digital maternal care and wellness platform within the Sono Group ecosystem. Flabee Care is designed to support expectant mothers throughout their prenatal journey, offering capabilities such as appointment scheduling, milestone tracking, ultrasound image and medical-record storage, pregnancy care tips, and baby growth monitoring. Through its connection with Sonobee Ultrasound and Sono Group, which supports prenatal and postnatal care services for a large Malaysian customer base, Flabee is positioned to address the growing need for accessible, personalized, and technology-enabled maternal wellness experiences. As a digital health and wellness business, Flabee is best aligned to the SMB/Startup market segment, with a focus on improving user engagement, care accessibility, and scalable delivery of maternal and menstrual wellness insights through AI-assisted digital services.
Problem statement:
Flabee operates in the women’s wellness domain, where users seek timely, personalized, and easy-to-understand guidance related to menstrual cycles, maternal health, symptoms, lifestyle patterns, and general wellbeing. In a conventional digital workflow, users may need to manually interpret fragmented health information, review static content, or rely on generalized recommendations that do not reflect their individual context. This can limit user engagement, reduce the relevance of insights delivered, and create inconsistency in how wellness information is surfaced across the platform.
Solution:
Webby delivered an AWS-based AI-powered wellness insights platform to digitalize and enhance how maternal and menstrual wellness information is generated and presented to users. The solution ingests user-provided information such as symptom inputs, cycle data, profile attributes, lifestyle indicators, and other permitted wellness-related data, then generates contextual wellness insights, educational summaries, and next-best informational prompts. This enables Flabee to move from static content delivery toward a more personalized, scalable, and consistent user engagement model.
AWS plays a central role in the solution architecture. Amazon S3 can be used for secure storage of application assets, user-submitted records, and analytics-ready data objects, while backend orchestration can be implemented using Amazon API Gateway, AWS Lambda, and supporting observability and security services. For the AI layer, Amazon Bedrock can be used to evaluate and deploy managed foundation models, to generate personalized wellness insights, educational explanations, and safe user-facing summaries based on approved input data and application guardrails. Where retrieval of approved domain content is required, Amazon Bedrock Knowledge Bases can be used to ground responses in curated maternal and menstrual wellness information so outputs remain relevant, consistent, and aligned with approved content sources.
Outcome:
The business outcome of this transformation had improved user engagement, more personalized wellness experiences, faster delivery of contextual insights, stronger consistency in content delivery, and better scalability of the platform as user volumes grow. To make this example fully competency-ready, Flabee’s submission should include quantified before-and-after metrics such as increase in user engagement rate, increase in repeat app usage, reduction in manual content curation effort, average insight-generation response time, percentage of user interactions handled through AI-assisted workflows, user satisfaction scores, and measurable operational efficiencies achieved through automation.