Case Studies

Salesforce-to-S3 Data Pipeline

Source One Spares needed a secure, cost-effective way to back up Salesforce data to AWS, without managing complex
infrastructure. They required encryption, auditing, compliance controls, and a solution their on-prem team could use with
their existing stack.

Goal:

Build a production-ready AWS backup pipeline, no compute overhead, no ongoing management, no complexity.

Key results

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Challenge

No infrastructure overhead

Source One Spares didn't want to manage servers, Lambda functions, or complex pipelines. They needed a backup solution that just works, set it and forget it.

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Enterprise-grade security


Salesforce data is sensitive. They required encryption at rest and in transit, strict access controls, and full audit trails for compliance.

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Cost efficiency at scale


Storing years of Salesforce backups can get expensive fast. They needed intelligent tiering to minimize costs without sacrificing access when needed.

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What We Built

A production-ready AWS backup pipeline; zero compute, zero complexity.

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Cost Optimization

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Deliverables

• Architecture diagrams ( VPC + S3 backup flow)
• IAM & Access Runbook with sample code
• Validation Report with security verification
• Live handoff session with connectivity test

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7 Security Controls Enforced

• SSE-KMS encryption
• HTTPS-only
• Least-privilege IAM
• CloudTrail logging S3 access logs
• Versioning enabled
• Blocked public access

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Want Similar Results for your business?

Schedule a call with the Cloudtech team to scope a voice-agent pilot for your use
case. We typically go from baseline to production in four weeks.

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A family-owned travel company in Myrtle Beach built an outbound voice agent that qualifies leads naturally, then hands off to
human bookers , cutting cost-per-call 67% while keeping their U.S. based phone team at the center of every booking.

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Key results

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Customer Snapshot

Monster Reservations Group has booked vacations for thousands of families across 50+ destinations since 2006.

Their competitive edge has always been their U.S.-based phone team — fast, friendly, and the reason customers come back. The challenge was scaling this team without losing that quality.

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The Challenge

Slow responses broke the flow

The existing AI agent took 1.5 seconds to respond; long enough for callers to notice, interrupt, or lose

confidence. Conversations felt robotic, not human.

Manual outreach was inefficient

Agents spent time on initial calls gathering basic preferences before they could focus on actually booking

vacations. This limited how many customers they could serve.

The Bar for Success

Agents spent time on initial calls gathering basic preferences before they could focus on actually booking vacations. This limited how many customers they could serve.

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The Solution

CloudTech built an outbound AI voice agent designed for one job: qualify leads efficiently. Built on AWS using Amazon

Bedrock for reasoning, Amazon Connect for telephony, and ElevenLabs for natural voice synthesis.

Outbound calling

The AI agent initiates calls to customers and engages in natural conversation about their vacation preferences.

Preference gathering

Captures destination interests, travel dates, group size, and budget through guided dialogue.

Seamless handoff

Once preferences are gathered, the call transfers to a human agent via Amazon Connect with full context. No need for the customer to repeat anything.
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Scope & timeline

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Outcomes

• Projected 67% reduction in cost-per-call by automating initial outreach
• 500ms response latency, conversations feel natural, not robotic
• 95%+ accuracy in gathering customer preferences
• Human agents now start conversations with full context, ready to book
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What we tuned

Model selection

Amazon Bedrock for reasoning, ElevenLabs for natural voice

Conversation flow

Refined dialogue to gather preferences naturally

Handoff timing

Smooth transfer via Amazon Connect with full context
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Where it can be replicated

Outbound sales & lead qualification at scale
Teams that need to make hundreds or thousands of initial contact calls where the goal is to qualify, not close.

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Information gathering before human conversation
Businesses where every human conversation is more valuable when it starts with full context
healthcare intake, financial pre-qualification, service scheduling.

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Automated outreach with human closing
Any team that wants to automate the first 30 seconds of every call without losing the human touch on
the booking, sale, or decision moment.

Want Similar Results for your business?

Schedule a call with the Cloudtech team to scope a voice-agent pilot for your use case. We typically go from baseline to production in four weeks.

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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.

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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.

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Solution Architecture Diagram

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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.

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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.

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Customer Profile

Klamath Health Partnerships provides accessible, culturally sensitive, affordable, quality-driven, responsive, patient-centered health services to the community, with an emphasis on those who need it most. These underserved populations typically include individuals from low-income families, the elderly, disabled, children, mentally ill, developmentally disabled,immigrants, the working poor, and those unable to work. Klamath Health Partnership is the second largest medical provider in the region, offering residents access to culturally appropriate, high quality, and affordable primary and preventive health care from a network of local clinics.

Customer Pain Points

Klamath Health’s on-premise infrastructure and data center are on an active fault line, so secure backup and disaster recovery was a primary concern. They also wanted to migrate their EHR and other data to AWS, using Tableau Cloud for business intelligence and analytics. Their managed services provider, who had begun the planning process for a migration, was removed and they were left understaffed and questioning their plan. Additional factors included:

• Legacy software and infrastructure
• Data retention standards
• Structured file share permissions
• Antivirus, malware protection, anti-phishing
• Data auditing
• Security
• HIPAA compliance

Cloudtech Solution:

Cloudtech began the engagement with a one-day workshop to capture the desired technical and business outcomes.Importantly, Klamath Health leaders were present and active to provide a holistic view of what the organization needed and wanted from their architecture. Based on the identified goals, Cloudtech proposed technical solution options and, together with Klamath Health, prioritized the work required and created a detailed roadmap.

Following the roadmap, the Cloudtech team built an AWS presence, comprising Organizational, Security, Infrastructure, and Workload OUs managed through AWS Organizations.


Beyond account creation and AWS Control Tower implementation, the heavy lifting of the engagement was the data migration and hydration of the Klamath Health data lake housed on S3. To accomplish the requirements of a hybrid system with file share capability and multiple data sources of both structured and unstructured data, the team used Amazon S3 File Gateway, AWS Site to Site VPN, AWS Storage Gateway, and Amazon S3.

Throughout the engagement, Cloudtech provided continuous knowledge share and extensive hands-on training and advisory services to the Klamath Health team, instilling confidence to operate the solution going forward.

Customer Outcome

With this solution in place, Klamath Health has a HIPAA-compliant, resilient data lake to house their EHR data as well as otherbusiness-critical datasets. They also have a structured data lifecycle and a highly available data store to ensure the securityand integrity of their patient and physician data as well as a backup policy that meets their RPO and RTO requirements. Withthe balance of the cost savings of the removal of their managed services provider and the dialed in TCO of their new solution,Klamath Health is saving 77% on their infrastructure costsYoY. On top of that, Cloudtech has trained their team to be fullycapable of maintaining and growing their infrastructure as their patient base grows and their organization expands.

Solution Architecture Diagram

AWS Services Used:

• AWS Control Tower
• AWS IAM Identity Center
• AWS Cost Explorer
• Cost & Usage Report
• AWS Organizations
• AWS Directory Service
• Amazon CloudWatch
• AWS CloudTrail
• AWS Config
• AWS Key Management Service
• AWS Security Hub
• Amazon Macie
• Amazon S3 File Gateway
• AWS Site to Site VPN
• AWS Storage Gateway
• Amazon S3

Testimonial

Project Summary

PXL is an open source social network platform for content creators. It enables users to create public or private spaces for any use such as any particular task base space or any other. Furthermore, users can take advantage of social features such as building connections, posting projects that pique the interest of other users, adding team members, notifications, project participation, and more. They can also manage their profiles and conduct a global search. This social network tool offers an online version where anyone can experience this free tool. PXL’s user interface is very logical, and users can easily navigate through various elements.

Problem Statement

The client’s requirement was to build a full-fledged backend application that can easily integrate with their prebuilt front-end application, and he later asked us to integrate the backend with the front-end.

We had to design and create a social platform where users can showcase their inventions and gain exposure. One can post any software project, categorize them, invite team members, and also participate in other projects.

Additionally, to meet the need for significant content uploads, a solution had to be developed that could easily handle the upload of media files while still being affordable and effective.

We also had to create a real-time notification system that monitors all network activity such as accepting requests, declining requests, and being removed from one’s network.

Our Solution

1. With thorough testing, responsive design, and increased efficiency and performance, we concentrated on completing each task as effectively as we could.
2. Based on the client’s requirements, we used S3 bucket, RDS, EC2, and flask microservice for media files and SES for emails.
   - Amazon S3 was used for file hosting and data persistence.
   – Amazon Relational Database Service (RDS) was used for database deployment as it simplifies the creation, operation, management, and scaling of relational databases.
   – Amazon EC2 was used for code deployment because it offers a simple web service interface for scalable application deployment.
3. We sent emails using Amazon SES because it is a simple and cost-effective way to send and receive emails using your own email addresses and domains.
4. Django-graphQL was used for the backend, and Next.js was used for the front end. Django includes a built-in object-relational mapping layer (ORM) for interacting with application data from various relational databases.
   – GraphQL aims to automate backend APIs by providing type-strict query language and a single API Endpoint where you can query all information that you need and trigger mutations to send data to the backend.
   – Next.js offers the best server-side rendering and static website development solutions. We utilized the flask microservice to help with high content uploads since flask upload files give your application the flexibility and efficiency to manage file uploading and serving.
5. Using Github’s automated CI/CD pipeline we have triggers for code lookup and deployment.

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Technologies

Django-GraphQL, Next.js, PostgreSQL, AWS S3, EC2, SES and RDS

Success Metrics

• All deliverables were completed on time and exceeded expectations.
• Met all the expectations of the client and with positive feedback.
• The client was constantly updated on the status of the project.

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