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Aktivo Labs is a Singapore-based health-tech startup founded in July 2017 by Gourab Mukherjee, Dr. Meng-Han Kuok, and the late Professor David Lai, with a mission to combat the global rise in chronic diseases. Its platform delivers personalized health metrics and insights that improve long-term health outcomes by leveraging data from devices like smartphones and wearables combined with AI-driven analytics.

Since its inception, Aktivo Labs has grown significantly, expanding its user base across Asia, Africa, and the Middle East. It manages the data of over 1.5 million users on the platform through the AI and Data solutions from MongoDB.

Avinash Johnson, Chief Technology Officer of Aktivo Labs discusses his company’s adoption of MongoDB, how his company overcame the initial challenges onboarding these solutions, and his advice for other companies, especially SMEs, who are looking to incorporate AI tools into their operations.

What were the primary considerations that led Aktivo Labs to adopt MongoDB?

One of the key reasons we chose MongoDB was its ability to handle large volumes of raw timeline data from various sources, which was critical for processing and delivering health scores reliably and in real time to our users.

Early on, we operated with a small team of specialists and needed to prioritize app functionality over managing infrastructure. MongoDB Atlas, allowed us to focus on building features while it handled scaling, backups, and maintenance. This approach helped us keep technology costs low—we could start with a small configuration and scale up seamlessly as user demand grew.

As data processors, we also had to cater to data residency requirements, and MongoDB’s ability to be seamlessly deployed across various regions and cloud platforms was a huge factor. Features like VPC peering allowed the database infrastructure to remain separate yet behave as part of our own cluster, ensuring low latencies and high performance.

Could you elaborate on the examples of how deployment of MongoDB has enabled Aktivo Labs to optimise its operations? What are some of the measurable outcomes achieved?

One of the most significant advantages is that we don’t spend much time on database administration or deployments. With MongoDB Atlas, a fully managed service, tasks like scaling, backups, and maintenance are handled seamlessly. This allows our team to focus on building functionality and delivering value rather than managing infrastructure. Over the years, upgrades across MongoDB versions have been smooth and disruption-free, ensuring we remain up-to-date without operational hiccups.

We’ve also seen a 20% reduction in development cycles thanks to MongoDB’s flexible schema design. This flexibility allows us to adapt quickly to changing customer needs and introduce new functionality in a timely manner, which is critical for a fast-moving, customer-centric platform like ours.

What were the key technical challenges Aktivo Labs encountered during the initial phases of using MongoDB? How were these challenges addressed to ensure a smooth implementation?

During the initial phases of using MongoDB, we came in with a SQL mindset—and while things worked initially, we gradually discovered the tweaks needed to embrace a true NoSQL approach, unlocking far greater efficiencies and performance improvements as we scaled.

A breakthrough came when we started leveraging MongoDB’s change streams to listen for real-time updates. This allowed us to re-architect components like our leaderboard processor, which now listens to changes in raw activity data, such as step counts and dynamically updates leaderboards in real time. This observability-driven approach greatly improved responsiveness and system efficiency, enabling us to deliver features that scale seamlessly.

Another key improvement came after a few architecture sessions, where we realized we could direct read-heavy operations—like analytics and reporting—to unused secondary nodes. This offloaded pressure from the primary node, improved read performance, and allowed us to scale far more efficiently while maximizing the value of our existing infrastructure.

What technologies or innovations does Aktivo Labs plan to adopt in the future?

We continue to be excited by the rapid advancements in technology, mainly the ease of implementing AI for impactful use cases. At Aktivo Labs, our expertise in processing real-time health data and delivering actionable insights has driven significant success in preventive health. By creating tools that engage users and encourage healthier behaviors, we’ve added real value for individuals and insurers.

Our ambitious roadmap focuses on enhancing our digital health solutions and expanding product offerings. This includes advancing our AI/ML capabilities to deliver personalized health insights, developing advanced predictive health analytics, and enhancing real-time health risk assessment capabilities. By leveraging AI, we aim to refine real-time, actionable feedback and address modifiable risk factors for chronic diseases.

We also plan to advance our data science capabilities by developing sophisticated algorithms for predicting and managing chronic diseases through digital biomarkers. New modules targeting diabetes, heart health, weight loss, and smoking cessation will offer tailored insights and recommendations supporting healthier lifestyles.

What is your advice for other companies, especially SMEs, looking to incorporate AI tools into their operations?

My advice to other companies, especially SMEs, looking to incorporate AI tools into their operations is to start with a clear purpose and take it one step at a time. AI can be incredibly powerful, but its success depends on how well it’s applied to solve real problems or deliver tangible value.

Start small and focused—identify areas where AI can make the most immediate impact, like automating repetitive tasks, improving processes, or delivering better insights from your data. You don’t need to reinvent the wheel; plenty of ready-to-use tools and platforms are out there, like cloud-based AI services and pre-built APIs, which can reduce costs and complexity.

At the same time, remember that data is key. Good AI relies on clean, reliable data, so make sure you’re collecting and organizing it effectively. It’s also essential to build a culture of experimentation—AI isn’t perfect on day one, so encourage your team to test, measure, and refine as they go.

Don’t be afraid to collaborate with experts or technology providers who can help you design and implement solutions that align with your goals. Upskilling your team is equally important—AI tools are becoming easier to use, but giving your team the confidence and knowledge to leverage them effectively will make a huge difference.

Finally, always focus on the value AI brings, not the hype. AI should help you improve efficiency, reduce costs, enhance customer experiences, or create new opportunities, so tie every initiative back to a measurable outcome. By starting small, staying practical, and learning along the way, SMEs can adopt AI in an impactful and sustainable way.

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