How boAt delivers personalized fitness coaching with Amazon Bedrock

How boAt delivers personalized fitness coaching with Amazon Bedrock | AWS Architecture Blog

How boAt delivers personalized fitness coaching with Amazon Bedrock

This post is co-written with Sridhara Seshadri from KaHa Technologies Pvt Ltd, a wholly-owned subsidiary of Imagine Marketing Limited (IML).

Recognizing that users need more than raw fitness data, boAt launched the “AI Coach” feature. It uses a combination of user history, preferences, and generative AI to deliver personalized coaching plans in real time. Using AWS, boAt built a solution that delivers personalized AI coaching. The AI Coach feature has been in production for over six months, reliably serving millions of boAt smartwatch users across India. In this post, we explore how boAt built the AI Coach on Amazon Bedrock and AWS Lambda, and the design choices that made it scalable and cost-effective.

Imagine Marketing Limited primarily operates under the brand boAt, launched in 2015 in the consumer electronics industry, focused on mass-premium, lifestyle-oriented products for India’s young, tech-savvy consumers at accessible price points. Starting with audio, boAt has expanded into adjacent categories such as wearables and charging solutions, selling through online marketplaces, D2C, and offline retail. In FY 2025, boAt led India’s branded personal audio with strong value and volume shares, sold over 34 million units in India as reported in FounderPin , and ranked among the top global audio brands by volume. Its agile, digital-first model drives rapid innovation, wide reach, and faster time-to-market aligned with evolving consumer preferences.

Opportunity

With the emergence of advanced AI capabilities, boAt saw an opportunity to deliver a highly personalized fitness coaching experience for its smartwatch users. The core objective was to help users build and sustain fitness and wellness habits aligned with their lifestyles. These recommendations needed to be straightforward for users to follow while clearly communicating their value.

The three primary design principles were personalization, affordability, and scalability.

Solution architecture

The following shows a simplified high-level architecture of the AI Coach components deployed on AWS.

Figure 1: High-level architecture of the boAt AI Coach on AWS

The following sections provide a brief overview of the architecture and its key components.

• Data source: The boAt smartwatch serves as the primary data source, capturing metrics such as steps, user activities (for example, running or yoga), and sleep data. All data is encrypted at rest and in transit. The boAt Crest companion app for iOS and Android also captures additional data points, such as the user’s preferred communication style and fitness goals for a predefined period.

• Historical data: Historical user data, when available, plays a key role in personalizing plans. These data points are transmitted by the companion application to backend servers and stored in Amazon Relational Database Service (Amazon RDS) for MySQL and Amazon DocumentDB (with MongoDB compatibility) . When the AI coach creates a plan, this historical data provides important context about what the user has done earlier.

• AWS Lambda : This component plays several roles: It acts as a translation layer, transforming input data into a format suitable for large language models (LLMs) and converting model outputs into a format the companion application can parse and interpret. This layer also abstracts the underlying AI coach implementation. On the input side, the data sent through the API is in a legacy format optimized for database operations. This is mapped to a more verbose format so that LLMs can better interpret what each key means. On the output side, the system handles missing keys in the generated plan by substituting safe defaults and verifying that the response format remains consistent with the companion application’s requirements. For production deployments, consider placing the Amazon Elastic Compute Cloud (Amazon EC2) instance in a private subnet and exposing it through an Amazon API Gateway private integration using a VPC link .

The AI Coach feature has two main logical pieces:

• Personalized plan generation.

• Nudges to aid adherence to the plan.

We use the Meta Llama 3.3 70B Instruct model on Amazon Bedrock to implement the AI Coach feature. For model availability by Region, see Region availability at a glance .

AWS Lambda retrieves historical user data for a predefined period in accordance with applicable privacy laws from the boAt Crest Companion app (full historical data is stored in Amazon RDS and Amazon DocumentDB), supplies it as context to the LLM for personalized plan generation, and parses the model response for the client. The team uses Amazon Bedrock on-demand inference exclusively, with structured data retrieval and prompt construction handled within Lambda. As adoption grows, they plan to transition to Amazon Bedrock batch inference to optimize costs at scale.

The plan is personalized to each user based on the inputs provided by the user during onboarding to this feature and historical user data patterns. The architecture was designed in such a way that future changes in user inputs don’t need a change in the backend components.

Relevant nudges are sent to the user at different times of each day, based on their actual metrics compared to the recommended ones. The style and tone of the nudges are offered as a configuration for the end user. These range from informational and objective to pop-culture and sarcastic, with a wide variety of style packs to choose from.

After evaluating options, the team selected the AWS Lambda and Amazon Bedrock combination for three reasons:

• Model-agnostic access: The unified API in Amazon Bedrock let the team evaluate multiple foundation models (including Meta Llama 3.3) through a single integration. This avoided the need to build separate pipelines for each LLM and accelerated model selection.

• Built-in observability and cost governance: Amazon Bedrock publishes per-invocation metrics (token counts, latency, and error rates) directly to Amazon CloudWatch , and it supports application inference profiles with cost allocation tags to attribute usage by tenant, feature, or environment. Combined with the average Lambda execution-time metrics, this gave the team precise per-user unit economics from day one, without building custom metering infrastructure.

• Serverless scalability with zero infrastructure overhead: The on-demand inference mode in Amazon Bedrock requires no provisioned compute. It scales automatically with usage and charges only per token processed. As AI Coach adoption grows, the team can extend throughput through cross-Region inference profiles that dynamically route requests across AWS Regions, without changes to their Lambda-based backend or impact to the end user experience.

Throughout development, the team applied responsible AI practices—including output validation, safe default substitution for missing response keys, and prompt constraints limiting recommendations to watch-supported activities—to deliver a trustworthy and safe user experience.

The following screenshots illustrate the AI Coach onboarding experience, where users set their fitness goals, preferred activities, workout frequency, sleep schedule, and stress levels to generate a fully personalized coaching plan.

Beyond the user-facing experience shown in the preceding screenshots, the development journey involved several technical and design challenges worth highlighting. The following are notable challenges the team encountered during development and real-world testing.

• Personalizing plans effectively: During the initial testing phase, the generated plans were not personalized enough to meet our expectations. The data the companion app provides is in JSON format, made of key-value pairs. We found that the keys needed to be more descriptive for the LLM to interpret the data effectively. For example, weight became weight_in_kg , and target became step_goal . We spent considerable time verifying that each legacy key was expanded and made as descriptive as possible. This higher level of descriptiveness significantly improved plan personalization.

• Smartwatch capability matching: Recommended activities in the plan, such as swimming, had to align with the user’s onboarding preferences, their overall goals, and the smartwatch’s capability to support those activities. We added a dedicated section to the prompt that lists watch-supported activities, which reduced the likelihood of unsupported recommendations. However, because of the non-deterministic nature of LLMs, prompt optimization alone did not eliminate all errors. We therefore added a parsing layer and suitable defaults to handle exception cases.

Conclusion

The boAt AI Coach demonstrates how generative AI can transform raw smartwatch data into personalized, actionable wellness guidance at scale. By combining Amazon Bedrock, AWS Lambda, and thoughtful prompt engineering, the team shipped a production-grade feature that serves millions of users without sacrificing personalization or reliability. To learn more about the boAt AI Coach and explore the technologies behind it, check out the following resources:

• Read about how the AI Coach in boAt smartwatches can improve your fitness

• Explore Amazon Bedrock for building generative AI applications

• Explore the boAt Chrome Eon smartwatch featuring AI Coach

About the authors

'"`