Project Summary
Creating a Marketing website using ReactJS and AWS for the client to showcase what they do and how they do.
Feature enhancement in an existing web application where people with disabilities can request a communication facilitator or a support service provider and providers can accept a request and receive payment.
Problem Statement
The client divided the project into several MVPs.
As part of MVP-1, the client wanted to create a marketing website that is fast, secure, and allows people to understand what Mizaru is and how it can benefit them. They wanted a website that performs operations faster, is secure from the bots, and is cheaper to maintain.
MVP-2 involved enhancing the client’s existing web application, which was previously very basic. They wanted to implement features like admin dashboard management, QR code-based check-in and check-out of providers to provide service, etc.
In MVP-3 they wanted us to create a mobile application to perform the same functionality.
Our Solutions
1) We created a marketing website for the users using ReactJS. This provides us with a faster way to create and serve the application.
2) For deployment and maintenance, we used AWS. It reduced our cost and maintenance efforts.
3) For enhanced security from bots, we’ve implemented google ReCaptcha v3.
4) Once the user has a clear understanding, they are moved to a web app or a mobile App.
5) Through the web app customers (People with disability) can create a request based on their requirements (e.g. Need a communication facilitator or support service provider). Our application provides a way for people with disabilities to connect with service providers. This request will be visible to multiple service providers in the network and they can choose to accept or reject the request.
6) We integrated a payment gateway for processing the payment. Also, both customers and providers get notified of the multiple events. We created a dashboard for Admins to see the track of various requests and generate reports as per their needs.
Technologies
Express JS, React JS, Redux, AWS, GIT, Hubspot, Google Recaptcha v3
Success Metrics
- Created and delivered marketing website within the given timeframe.
- Created report generation feature for admin.
- Implementation of QR code based check-in and check-out of provider.
- Email reminders for customer and providers before service.
Mizaru- Online Platform for Specially Abled People To Get Support Services
Executive Summary
A leading provider of SaaS solutions for OTT video delivery and media app development partnered with us to modernize their backend infrastructure and enhance their analytics platform on AWS. The project aimed to transition core APIs, user analytics, and media streaming orchestration to a cloud-native, serverless architecture. This modernization significantly improved platform scalability, reduced latency for global users, and enabled real-time analytics across content, user interactions, and overall performance.
Challenges
The customer faced several challenges that hindered scalability, performance, and operational efficiency:
- API Modernization: Legacy, monolithic API services caused inefficiencies and limited scalability.
- Global Latency: Content delivery, particularly video, had inconsistent performance for users across different regions.
- Real-Time Analytics: A lack of real-time data insights made it difficult to track user engagement and optimize content.
- Operational Complexity: High operational overhead due to manual processes and limited automation.
- Disaster Recovery: Ensuring high availability and data redundancy was a top concern.
Scope of the Project
We were tasked with modernizing the SaaS platform by migrating core APIs and backend services to AWS microservices architecture. This involved microservice decomposition using Amazon API Gateway and AWS Lambda to replace monolithic API services. We also optimized video content delivery by utilizing CloudFront, S3, and MediaConvert for low-latency streaming and global delivery. A serverless analytics pipeline was built to process and analyze user events through Kinesis, Lambda, Redshift, and Glue. We ensured high availability by implementing multi-region failover with Route 53 and Aurora Global. Finally, CI/CD workflows were automated using CodePipeline, with monitoring and observability ensured through CloudWatch and X-Ray.
Partner Solution
We designed a fully serverless, scalable architecture using a combination of AWS Lambda for event-driven API services, CloudFront for global content delivery, and Redshift for analytics. The solution leverages Kinesis for real-time data ingestion, and Glue for data transformation and storage in S3.
The architecture includes:
- API Gateway for routing requests to Lambda and ECS/Fargate microservices.
- CloudFront for content delivery with S3 and MediaConvert integration.
- Redshift for querying and analyzing user interaction data.
- Aurora Global Database for cross-region failover and high availability.
- AWS Backup for disaster recovery and cross-region replication of data.
Our Solution
API Modernization
- Microservice Decomposition: We migrated monolithic APIs to a microservices-based architecture on AWS using API Gateway to manage routing and AWS Lambda to handle serverless execution.
- ECS/Fargate: Containerized components are managed through Amazon ECS (Fargate) for flexible, cost-efficient compute.
- API Gateway: Securely exposed the APIs to the frontend, validating requests and integrating with backend services using IAM roles for access control.
Streaming Optimization
- CloudFront CDN: Used CloudFront to cache content at the edge, reducing latency and speeding up content delivery globally.
- S3 & MediaConvert: Leveraged Amazon S3 for storage and MediaConvert for adaptive bitrate transcoding, enabling smooth video streaming on various devices.
- Global Distribution: Ensured optimal performance and reduced buffering by using CloudFront to serve video content efficiently to users across the globe.
Real-Time Analytics Pipeline
- Data Ingestion: Kinesis Data Streams was used to ingest user interaction events (e.g., play, pause, share) in real time.
- Data Enrichment: AWS Lambda processed and enriched the data streams before being stored in S3.
- ETL with Glue: AWS Glue performed ETL (Extract, Transform, Load) processes, converting data into an analytical format for consumption by Redshift.
- Analytics: Amazon Redshift was used for fast querying and reporting, enabling real-time insights into user behavior.
High Availability and Fault Tolerance
- Multi-AZ Deployment: Deployed critical services like Redshift and Aurora Global in multiple Availability Zones to ensure high availability.
- Route 53 Failover: Set up Route 53 with latency-based routing and health checks to ensure automatic failover between regions if one region faces issues.
- Auto-Scaling: Configured Auto Scaling Groups and ALB to automatically scale compute resources based on demand.
Benefits
- Enhanced Streaming Performance: By implementing CloudFront and MediaConvert, video buffering was reduced by >90% and latency minimized across regions.
- Real-Time Analytics: The Redshift + Glue pipeline enabled real-time analytics, empowering the customer to optimize user engagement based on live data insights.
- Operational Efficiency: Automating CI/CD with CodePipeline and using serverless components significantly reduced manual intervention, lowering operational overhead by 40%.
- High Availability: Route 53 routing and Aurora Global deployment ensured 99.95% uptime across regions, offering the customer peace of mind.
- Scalable Storage: Using S3 Intelligent-Tiering and Redshift Concurrency Scaling provided optimal storage management, ensuring cost efficiency as data grew.
Outcome (Business Impact)
- Enhanced User Satisfaction: Reduced video buffering to <3%, improving content delivery speed and user experience globally.
- Improved Match Accuracy: Real-time Redshift + Glue pipelines improved engagement tracking, reducing query times from 30s to <5s.
- Operational Efficiency: Automation through CI/CD and serverless components cut manual intervention by 40%, increasing overall team productivity.
- High Availability: Route 53 and Aurora Global ensured 99.95% uptime, even during regional outages.
- Cost Efficiency: Optimized storage with S3 Intelligent-Tiering and Redshift Concurrency Scaling drove down operational costs.
Executive Summary
BeNotable is a platform dedicated to connecting music students with colleges. To stay ahead in a competitive landscape, BeNotable aimed to leverage Generative AI to enrich students’ audition experience and differentiate their service. We assessed their existing AWS-based data infrastructure (Amazon S3 and DynamoDB), technical maturity, and business objectives. The assessment highlighted an opportunity to introduce the “Aria Audition Lab Coach”, giving students instant, AI-generated feedback on tone, rhythm, and expressive quality. This case study outlines how we implemented a secure, scalable, and cost effective Generative AI workflow on AWS.
Challenges
- Provide high quality AI feedback on large volumes of audio while maintaining low latency.
- Protect student data and intellectual property with robust security controls.
- Ensure end to end observability and graceful failure handling across asynchronous workloads.
- Integrate seamlessly with BeNotable’s existing AWS foundations without disrupting live users.
Scope of the project
- Discovery & Readiness : Assessed data quality, security posture, and AI objectives.
- Architecture & PoC :Designed an event driven, serverless architecture and validated model choice in Amazon Bedrock.
- Implementation : Built secure upload, processing pipeline, AI inference, and feedback delivery using API Gateway, Lambda, S3, DynamoDB, SQS/SNS, and EventBridge.
- UAT & Launch : Performance, security, and user acceptance testing with staged rollout.
- Enablement:Delivered IaC templates, runbooks, and a roadmap for multilingual expansion.
Partner Solution
- Cloud native platform - That matches music students with colleges via audition submissions.
- Web and chatbot interfaces - For students to upload recordings and receive feedback.
- Existing AWS foundations - Amazon S3 for raw audio and Amazon DynamoDB for metadata storage.
- Key business goals - Deepen student engagement, enrich learning experience, and stand out from competitor platforms.
- Secure Upload – Students authenticate with Amazon Cognito; requests are filtered through AWS WAF and served via Amazon API Gateway to a “PUT /upload audio” Lambda function.
- Storage Layer – Raw recordings land in an Amazon S3 bucket; Lambda captures metadata (student, instrument, timestamp) and writes to Amazon DynamoDB.
- Processing Pipeline – An SQS queue triggers a processor Lambda that transcribes audio and invokes Amazon Bedrock (Anthropic Claude or AI21) to generate feedback. Events are coordinated with Amazon EventBridge.
- Messaging Layer – Results are published through Amazon SNS. A Dead Letter Queue retains failed messages for replay and root cause analysis.
- Observability & Monitoring – Amazon CloudWatch Logs, metrics, and AWS X Ray traces provided full visibility, while AWS Config & IAM manage compliance and least privilege access.
- Scalability & Resilience – The design is serverless and fully managed, automatically scaling with usage and isolating faults through queue based decoupling.
Solution Architecture Diagram
Metrics Used to Measure Success & Lessons Learned
- Engagement: +30 % increase in average session duration; 2× rise in audition uploads.
- Latency: p95 feedback delivery < 4 s.
- Reliability: < 0.2 % message failure, all captured in DLQ
- Cost Efficiency: ~40 % reduction in operational overhead via serverless pay per use.
Lesson Learned
- Prompt engineering with few shot and chain of thought examples is key to nuanced music feedback.
- RAG with Titan Embeddings grounds generative output in music theory references for factual accuracy.
- Comprehensive observability accelerates latency tuning and error resolution.
- Early educator feedback loops refine model prompts and sustain content authenticity.
Outcome (Business Impact)
- Students receive immediate, high quality feedback, increasing practice frequency and quality.
- Colleges gain richer audition insights, improving talent fit decisions and placement rates.
- BeNotable differentiates as an AI driven innovator, attracting new users and institutional partners.
- Serverless architecture scales elastically with peak audition seasons while aligning costs to usage.
Executive Summary
Inclusive+ aimed to elevate its culturally sensitive healthcare directory by developing an intelligent, AI-driven provider matching system. Leveraging Amazon Bedrock alongside a suite of AWS services, the platform delivers personalized, inclusive, and conversational search experiences tailored to diverse user needs. This end-to-end solution ensures low latency and high-accuracy recommendations while adhering rigorously to AWS security and compliance best practices.
Challenges
- Limited Personalization: The original directory search lacked tailored results, leading to generic recommendations.
- Missing Conversational Flow: Users couldn’t ask clarifying questions or refine their preferences interactively.
- High Sensitivity: Working with LGBTQIA+ health data required robust security and cultural sensitivity.
- Data Management: Integrating real-time user data with historical provider data required scalable, resilient data pipelines.
Scope of the Project
The project covered the end-to-end design, implementation, and operationalization of an AI-powered, culturally sensitive provider matching system for Inclusive+. It involved assessing platform limitations, creating a scalable, serverless AWS architecture (using Bedrock, Lambda, API Gateway, SQS, RDS, OpenSearch, Glue, and AWS security services), and integrating Retrieval-Augmented Generation (RAG) for real-time, inclusive recommendations. Key activities included developing conversational search flows with prompt engineering, building automated data pipelines for continuous data accuracy, and implementing real-time monitoring and security best practices such as KMS encryption, IAM least-privilege access, and Bedrock Guardrails. The project concluded with delivering a comprehensive runbook and knowledge transfer to enable the Inclusive+ team to confidently maintain and scale the solution, ensuring inclusivity and continuous improvement.
Partner Solution
We designed a fully serverless, secure AWS-native architecture leveraging Amazon Bedrock, Lambda, API Gateway, SQS, RDS, OpenSearch, and Glue. By combining Retrieval-Augmented Generation (RAG) with advanced prompt engineering and Bedrock’s generative AI, we created a conversational search experience that is culturally competent and inclusive. Data pipelines powered by Glue ensure fresh, accurate provider data, while monitoring and observability layers (CloudWatch, CloudTrail) ensure operational excellence.
Solution Architecture Diagram
Our Solution
- User Authentication & Access
- Amazon Cognito authenticates users securely, managing user identity and access to the personalized search experience.
- IAM roles enforce least-privilege access across all AWS services, maintaining strict security and governance.
- API Orchestration & Event-Driven Compute
- Amazon API Gateway acts as the secure entry point for user requests from the frontend, validating the identity token from Cognito and routing requests to AWS Lambda functions that orchestrate provider search and recommendation workflows.
- AWS Lambda(Initial Invocation) is responsible for initial request handling, where it validates the input, sends the query to Amazon SQS for async processing, and optionally calls Amazon Personalize to prefetch user recommendations.
- AWS Lambda (Bedrock Invocation) : This Lambda worker consumes messages from SQS, retrieves relevant provider data, and dynamically builds prompts that are sent to Amazon Bedrock Agents to generate a conversational and personalized provider response for the user.
- Amazon SQS decouples tasks, buffering search and enrichment workloads to ensure reliable scaling and fault-tolerant message processing, with downstream Lambda functions polling the queue to process provider matching jobs in parallel.
- Data Storage & Enrichment
- Amazon RDS (Provider Mapping ): Hosts structured provider profiles including identifiers, qualifications, and tags, serving as a source-of-truth that feeds into the Glue-based ETL pipeline and supports accurate retrieval and provider data linking.
- Amazon OpenSearch Service (Offers Index) : Indexes structured provider data such as specialties, inclusive practices, and locations, enabling real-time filtered search results that can be embedded into prompts or used directly for matching logic.
- Amazon S3 : Acts as a durable and scalable storage layer for unstructured provider data such as bios, documents, and metadata, which are accessed by AWS Glue for ETL processing and may be referenced in Bedrock responses or OpenSearch indexes.
- AWS Glue : Extracts and transforms provider data from S3 and RDS, formats and chunks it for embedding and indexing, and catalogs it for querying, with outputs feeding directly into Bedrock Knowledge Bases and OpenSearch indexes to ensure GenAI is operating on fresh, clean data.
- Generative AI & Personalization
- Amazon Bedrock : Stores indexed provider information in a Knowledge Base, processes natural language queries using Bedrock Agents, and enforces safety and inclusivity via Guardrails to ensure the AI responses are accurate, empathetic, and aligned with LGBTQIA+ values.
- Prompt engineering ensures that every response is contextually relevant and reflects the nuances of LGBTQIA+ healthcare needs.
- Retrieval-Augmented Generation (RAG) integrates real-time data from RDS and OpenSearch into Bedrock prompts, delivering dynamic, inclusive recommendations.
- Amazon Personalize (Provider Segmentation) : Analyzes user behavior and historical data to provide tailored provider recommendations, which are combined with GenAI results to further personalize suggestions based on click patterns, preferences, and feedback.
- Monitoring, Observability, and Security
- Amazon CloudWatch continuously tracks system performance, including Lambda errors, API latencies, and SQS queue depth, to ensure system health and reliability. It captures logs, metrics, and alarms across all services providing full observability, error tracking, and performance insights.
- AWS CloudTrail logs all API activity for auditing and compliance purposes, ensuring a full record of interactions and changes.
- KMS encryption secures data at rest across RDS, OpenSearch, and S3, while TLS encryption ensures data in transit remains protected.
- Bedrock Guardrails provide an additional layer of protection, filtering out unsafe, biased, or culturally insensitive outputs to maintain trust and inclusivity.
Benefits
- Personalized Matching: Tailored recommendations aligned with user preferences and cultural needs.
- Conversational Flow: Interactive, inclusive conversations that guide users to the right care.
- Operational Efficiency: Serverless components (Lambda, API Gateway) ensure low latency and cost efficiency.
- Resilient Data Flows: Automated Glue pipelines and robust monitoring prevent data gaps or mismatches.
- Security & Compliance: KMS encryption, IAM best practices, and Bedrock Guardrails ensure privacy and trust.
Metrics Used to Measure Success & Lessons Learned
To evaluate the effectiveness of the Inclusive+ Generative AI platform and ensure continuous improvement, we tracked several key performance indicators (KPIs):
- Relevance and Accuracy: Regularly measured the accuracy of AI-generated provider matches against verified RDS data and user expectations, ensuring personalized and culturally competent results.
- User Engagement: Monitored click-through rates, interaction depth, and return visits to gauge user satisfaction and trust in the platform.
- Response Latency: Targeted sub-second response times for Bedrock-generated answers, ensuring a seamless, real-time conversational experience.
- Error Rates: Tracked Lambda errors, API Gateway 4xx/5xx responses, and SQS queue backlogs through CloudWatch dashboards to maintain system stability.
- User Feedback: Captured explicit feedback through in-app ratings and qualitative surveys to guide prompt and response refinements.
- Cost Efficiency: Analyzed Bedrock token usage and Lambda/SQS metrics to maintain cost-effective scalability.
- Bias and Inclusivity: Conducted periodic reviews to ensure recommendations remain unbiased and culturally affirming for LGBTQIA+ users.
From these metrics, key lessons learned included the importance of iteratively refining prompt structures to align with evolving user needs and conversational nuances. Real-time feedback loops also highlighted the value of continuous prompt optimization and proactive monitoring of data integrity to sustain trust and engagement. These insights have driven ongoing enhancements, ensuring that Inclusive+ remains a leader in inclusive, culturally competent healthcare recommendations.
Outcome (Business Impact)
- Enhanced user satisfaction and trust through personalized, culturally sensitive provider recommendations tailored to LGBTQIA+ identities and healthcare needs.
- Improved match accuracy by 40% compared to the previous static directory search, driven by dynamic Bedrock-powered conversations and RAG-enhanced relevance.
- Reduced search-to-decision time by 35%, enabling users to find culturally competent providers more efficiently and confidently.
- Strengthened brand trust, with 92% of surveyed users indicating they felt more seen and supported by the culturally sensitive, inclusive platform.
- Eliminated manual intervention for prompt and data updates by leveraging fully automated data pipelines and real-time observability.
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