.png)
To enable a leading Indian data intelligence organization to increase awareness and accessibility of government welfare schemes by building a high-performance, inclusive, and scalable digital platform. The goal was to connect millions of households with relevant, localized information in multiple languages while leveraging AI, Flutter, and cloud-native technologies.
The client is a prominent Indian data intelligence organization supporting citizen engagement and business decision-making through data-driven insights. The organization sought to address the low utilization of government social welfare schemes caused by awareness gaps, limited accessibility, and the digital divide between urban and rural populations.
To meet these challenges, the client envisioned a digital super-app capable of serving up to 250 million households with authentic and localized information in 13 regional languages, including voice-based access to ensure inclusivity.
BootLabs partnered as the primary cloud-native and platform engineering provider to translate the client’s generative AI and data-driven vision into a robust and high-performing digital platform.
Key aspects of the engagement included designing a microservices-based architecture capable of serving millions of users simultaneously, developing a single maintainable Flutter codebase optimized for low-end Android and iOS devices, integrating automated speech recognition for voice-based regional language queries, implementing cloud-native deployment using Google Kubernetes Engine and Terraform, and strategically caching frequently accessed data using Redis and AlloyDB to deliver low-latency performance.
.svg)
Large-scale government welfare schemes were underutilized due to fragmented access and lack of awareness among target populations.
.svg)
Key users relied on budget-tier devices, limiting reach and adoption with traditional application designs.
.svg)
Serving hundreds of millions of households required a robust, fault-tolerant, and highly scalable platform architecture.
.svg)
Providing real-time support for 13 regional languages, including voice interaction, was critical for widespread adoption.
Flutter was used to create a responsive and maintainable application with low resource consumption, optimized for budget-tier devices.
Automated Speech Recognition and Vertex AI enabled voice-based queries, real-time translation, and transliteration across multiple regional languages.
Google Kubernetes Engine and Terraform were used to provision scalable and resilient cloud infrastructure with service-level isolation to reduce latency.
Redis and AlloyDB were implemented to ensure low-latency access for 80 to 90 percent of requests, reducing backend load and improving response times.
Support for multiple languages, voice interaction, and optimized application performance ensured broad adoption and improved citizen engagement.
Between 80%-90% of platform requests were served from cache, significantly reducing database load and response times.
The platform successfully supported 13 regional languages with real-time translation and voice interaction.
Consistent performance was delivered across low-end Android and iOS devices.
Independent scaling of high-traffic modules was enabled through a microservices-based architecture.
The platform connected millions of households with government welfare information across both urban and rural regions.
Low-latency responses, cross-platform performance, and voice-based access maximized adoption on low-end devices.
Cloud-native deployment enabled seamless scaling of high-traffic services without infrastructure oversizing.
Real-time translation and voice interaction enabled inclusive access for diverse regional language speakers.
.png)
.png)
.png)
.png)
.png)
.png)
.png)
.png)