October 24, 2025 — San Francisco, CA
We’re thrilled to celebrate our partner MyAtlas, named a Gold winner at the 2025 Davey Awards for Best AI Integration in a Mobile App. The Davey Awards announced this year’s honorees in their Winners Gallery, and the MyAtlas entry reflects recognition for excellence in applying AI to real-world mental wellness.
This award validates what we’ve known from day one of partnering with the MyAtlas team: when you combine thoughtful product design, responsible AI architecture, and a genuine mission to help people, you create something that stands out—not just technically, but in the impact it has on users’ lives.
Explore the full story: Read our MyAtlas case study to learn how we helped build the AI-powered mental wellness platform from the ground up.
Why This Win Matters
MyAtlas is pushing the frontier of proactive mental wellness—turning continuous, real-time behavioral signals into timely support and personalized interventions. Their product vision aligns perfectly with our belief that AI should be practical, privacy-respecting, and measurably helpful.
The AI Integration Excellence
What makes MyAtlas’s AI integration award-winning?
Real-time personalization at scale: The platform processes behavioral data from connected devices (sleep patterns, activity levels, heart rate) and combines it with user-reported mood, goals, and context to deliver personalized recommendations that adapt throughout the day.
Proactive intervention, not reactive: Instead of waiting for users to seek help during a crisis, MyAtlas’s AI identifies patterns that suggest stress, burnout, or declining mental health—and surfaces relevant content, exercises, or professional resources before things escalate.
Privacy-first architecture: Mental wellness requires deep trust. MyAtlas built their AI integration with user privacy as a core requirement, not an afterthought. All behavioral analysis happens within a secure, HIPAA-compliant infrastructure that gives users control over their data.
Continuous learning without exploitation: The AI improves recommendations based on what actually helps each user (measured by engagement, self-reported outcomes, and behavioral markers of wellness) rather than optimizing for addictive engagement.
The MetaCTO Partnership
On our side, MetaCTO partnered with MyAtlas across product, engineering, and cloud infrastructure to help ship a robust platform that can scale with user demand while keeping user data safe.
What We Built Together
Our public case study highlights the core technical pillars:
AI/LLM Integration
- Custom recommendation engine that processes behavioral signals in real-time
- Natural language processing for mood tracking and journaling insights
- Personalization algorithms that adapt to each user’s wellness journey
- Integration with modern LLM APIs for conversational support
Cloud-Native Architecture
- Scalable infrastructure built on AWS with containerized microservices
- HIPAA-compliant data storage and processing pipelines
- Real-time data synchronization across devices
- Event-driven architecture for immediate personalization
Native Mobile Apps (iOS & Android)
- Swift and Kotlin native implementations for optimal performance
- Seamless integration with HealthKit and Google Fit
- Offline-first design for continuous tracking without connectivity
- Push notification system for timely interventions
Personalization Engine
- Behavioral pattern recognition from passive sensor data
- Context-aware content delivery (time of day, location, recent activity)
- A/B testing framework to continuously improve recommendations
- User feedback loops to refine AI models
The Product Impact
The results speak for themselves—both in user metrics and now in industry recognition:
Personalized, Actionable Guidance
The app delivers daily, tailored recommendations that adapt as users’ goals and context evolve. Unlike generic wellness apps that show the same meditation to everyone, MyAtlas understands that the right intervention depends on:
- Current state: Are you stressed, tired, energized, or calm?
- Time and place: Morning routine vs. evening wind-down
- History: What’s worked for you before?
- Goals: Are you working on sleep, anxiety, focus, or resilience?
24/7 Support + Behavioral Metrics
Users get continuous support and passive tracking (sleep, activity, heart rate variability) from connected devices to create a holistic wellness picture. The AI analyzes patterns across:
- Sleep quality and duration
- Physical activity levels
- Heart rate variability (a key stress indicator)
- Self-reported mood and energy
- Engagement with wellness content
This multi-dimensional view enables early detection of declining mental health and proactive interventions before users hit crisis points.
Built for Trust and Scale
The platform emphasizes a secure, cloud-native foundation and responsible AI patterns—key for wellness products that iterate quickly without compromising user trust.
Trust through transparency: Users can see why recommendations are surfaced and adjust preferences Privacy by design: Behavioral data is encrypted at rest and in transit; users control data sharing Clinical validation: The AI’s interventions are grounded in evidence-based therapeutic approaches Scalable infrastructure: The platform handles growth without degrading personalization quality
Hats Off to the Builders
Huge congratulations to Nita Akoh (Founder & CEO) and the entire MyAtlas team for their vision, persistence, and commitment to building something that genuinely helps people.
Special shoutout to Garrett Fritz (CTO) for leading a product experience where AI isn’t a feature—it’s the operating system of the app. Garrett’s technical leadership ensured that every AI decision was made with user benefit, privacy, and scalability in mind. 🙌
The Davey Gold award is well-deserved recognition of the team’s dedication to pushing what’s possible in AI-powered mental wellness.
What Makes Award-Winning AI Integration?
Looking at MyAtlas’s success, we see patterns that other teams can learn from:
1. Start with the User Problem, Not the Technology
MyAtlas didn’t set out to “use AI.” They asked: “How can we provide personalized mental wellness support at scale?” AI was the answer to that question, not the starting point.
Lesson: The best AI integrations solve real user pain points. If you can’t articulate the problem your AI solves without mentioning AI, you’re building technology in search of a problem.
2. Privacy and Ethics Can’t Be Retrofitted
MyAtlas built privacy, consent, and user control into the AI architecture from day one. This wasn’t a compliance checkbox—it was a product requirement.
Lesson: For sensitive categories like health, finance, or personal relationships, AI systems that don’t start with privacy will fail to earn user trust. Design your data model, permissions, and AI workflows with privacy as a core constraint.
3. Measure What Matters (Not Just Engagement)
MyAtlas optimizes for user wellness outcomes (self-reported mood improvements, sustained behavior change, reduction in crisis events) rather than vanity metrics like daily active users or session duration.
Lesson: AI can optimize for whatever you measure. Choose your success metrics carefully—especially in domains where exploitative engagement is easy but harmful.
4. Ship Fast, Learn Faster
The MyAtlas team didn’t wait for perfect AI models. They launched with v1 recommendations, measured what worked, and iterated rapidly based on real user feedback.
Lesson: AI systems improve through real-world data and user behavior. Perfectionism delays learning. Ship, measure, improve, repeat.
Explore More
See the Davey Awards Winners Gallery (2025): Gold Winner – Best AI Integration in a Mobile App (MyAtlas)
Read our MetaCTO × MyAtlas case study: Approach, architecture, and outcomes
Visit MyAtlas to learn about the platform’s features and mission: myatlas-health.com
Thinking About AI in Your Product?
If you’re exploring how to embed intelligence—safely and effectively—into your mobile experience, we’d love to help. From rapid discovery and prototyping to cloud-scale AI integration, MetaCTO partners with founders and product teams to turn ambitious ideas into reliable, shipping software.
We specialize in:
- AI/LLM integration for personalization, recommendations, and conversational interfaces
- Mobile app development (native iOS, Android, and cross-platform)
- Cloud-native architecture that scales with demand
- Privacy-first design for sensitive user data
- Rapid prototyping to validate AI concepts before full builds
Ready to build AI-powered features? See our AI Development services or contact us to discuss your project.
Frequently Asked Questions
What are the Davey Awards?
The Davey Awards honor the best creative work from boutique agencies, small firms, and in-house teams worldwide. Named after David against Goliath, the awards celebrate the 'Davids' who derive their strength from big ideas rather than big budgets. The 2025 Davey Awards recognized MyAtlas for Best AI Integration in a Mobile App—a category highlighting innovative use of artificial intelligence to enhance user experience and deliver measurable value.
What makes MyAtlas's AI integration different from other mental wellness apps?
MyAtlas uses AI for proactive intervention rather than reactive support. The platform analyzes behavioral signals from connected devices (sleep, activity, heart rate) plus user-reported mood and context to deliver personalized recommendations before mental health declines. Unlike generic meditation apps that show the same content to everyone, MyAtlas adapts interventions based on your current state, time of day, historical patterns, and wellness goals. The AI continuously learns what helps each individual user rather than optimizing for addictive engagement.
How does MetaCTO help companies build AI-powered mobile apps?
MetaCTO partners with startups and product teams across the full AI integration lifecycle: discovery and prototyping to validate AI concepts with real users before committing to full builds, AI/LLM architecture design for recommendations, personalization, or conversational interfaces, native mobile development (iOS Swift, Android Kotlin) with seamless device integrations, cloud-native infrastructure (AWS, GCP, Azure) that scales with user demand, and privacy-first data pipelines for sensitive categories like health, finance, or personal data. We focus on shipping AI features that solve real user problems while maintaining trust, privacy, and performance.
What technology stack did you use for MyAtlas?
MyAtlas is built on a cloud-native architecture using AWS for infrastructure, containerized microservices for scalability, Swift for native iOS development with HealthKit integration, Kotlin for native Android with Google Fit integration, modern LLM APIs for conversational AI and content generation, real-time data pipelines for behavioral analysis, and HIPAA-compliant data storage and processing. The full technical breakdown is available in our MyAtlas case study, including architecture diagrams and implementation details.
How long does it take to build an AI-powered mobile app like MyAtlas?
Timeline depends on scope and starting point. For MyAtlas, we followed a phased approach: Phase 1 (8-12 weeks): Product discovery, AI prototyping, and user validation—testing core AI concepts with real users before committing to full development. Phase 2 (12-16 weeks): Native mobile apps (iOS + Android) with device integrations, basic personalization, and cloud infrastructure. Phase 3 (8-12 weeks): Advanced AI features, behavioral pattern recognition, and real-time recommendation engine. Total time from concept to v1 launch: 6-9 months. If you're starting with an existing app and adding AI features, expect 3-6 months depending on complexity and required infrastructure upgrades.
What does it cost to build AI features into a mobile app?
AI integration costs vary widely based on scope. Budget ranges: Basic AI features (recommendations, simple personalization): $50k-$100k for prototyping and initial implementation. Advanced AI (behavioral analysis, real-time personalization, conversational interfaces): $150k-$300k for comprehensive integration. Full AI-powered platform (like MyAtlas): $300k-$600k for native mobile apps, cloud infrastructure, AI/LLM integration, and privacy-compliant data pipelines. Ongoing AI costs include LLM API usage (typically $0.001-$0.05 per user per month depending on interaction volume), cloud infrastructure scaling with user growth, and continuous model improvement based on user feedback. We recommend starting with a focused AI prototype ($25k-$50k, 4-6 weeks) to validate core concepts before committing to full development.
How do you ensure AI features are privacy-compliant for sensitive categories like health?
Privacy and compliance must be designed into AI architecture from day one—you can't retrofit it later. Our approach includes: data minimization (collect only what's needed for AI to function, discard the rest), encryption at rest and in transit for all behavioral and health data, user consent and control (users approve data collection and can delete their data), HIPAA-compliant infrastructure where applicable (signed BAAs, audit logging, access controls), and transparent AI decisions (users can see why recommendations are surfaced and adjust preferences). For MyAtlas, we implemented end-to-end encryption for all health data, role-based access controls limiting who can view user information, audit trails for all data access, and user-controlled data export and deletion. Privacy isn't a compliance checkbox—it's a product requirement that builds user trust.
Can you help us add AI to an existing mobile app, or do we need to rebuild from scratch?
Most existing apps can add AI features without a full rebuild, but it depends on your current architecture. We start with a technical assessment: evaluate your data infrastructure (Can you collect and process the signals AI needs?), review your cloud architecture (Can it scale with AI processing demands?), assess your mobile codebase (Can it integrate AI SDKs and real-time data sync?), and identify privacy/security gaps (especially for sensitive data). In many cases, you can add AI features incrementally: Phase 1: Server-side AI (recommendations, personalization) without changing mobile apps. Phase 2: Enhanced data collection (behavioral signals, user context) with mobile SDK updates. Phase 3: Real-time AI features requiring deeper mobile + cloud integration. We'll recommend the most cost-effective path based on your current tech stack and business goals.
Congrats again to MyAtlas on the Davey Gold. Onward! 🎉