🤖 Wyzen Multilingual Customer Support APP
Executive Summary
Wyzen is a rapidly growing IT consulting and services startup with operations across India, Europe, and Southeast Asia. With around 100+ employees, Wyzen supports public and private entities in IT transformation, audits, and digital modernization.
As the company expanded globally, its customer base became linguistically diverse. The firm needed to provide real-time, multilingual customer support without dramatically scaling headcount or cost. The existing system relied heavily on manual support, leading to delays, inconsistencies, and customer dissatisfaction in regional markets.
Solution Overview
Zaptoz designed and implemented a Generative AI–powered multilingual chatbot built entirely on AWS native services. The solution leverages Amazon Bedrock (Claude and Titan models), AWS Lambda, API Gateway, and EC2 Auto Scaling to deliver fast, accurate, and context-aware responses in over five languages.
By integrating Amazon Translate, Comprehend, and Langfuse, the chatbot dynamically detects user language, generates responses, and continuously improves based on usage patterns. This allowed Wyzen to achieve 24×7 customer engagement at minimal operational overhead.
Architecture Explanation
1. Frontend & Access Layer
Customers interact via the company's website and mobile application. Requests flow securely through Amazon API Gateway, which manages routing, authentication (IAM roles + Cognito), and usage throttling.
2. Application Logic Layer
AWS Lambda functions orchestrate the conversation flow, detect language, and manage prompt engineering for Amazon Bedrock. Amazon Bedrock (Claude for reasoning + Titan for embeddings) handles multilingual generative responses and contextual understanding. Lambda also integrates with CRM and analytics tools for workflow automation.
3. Data & Storage Layer
Amazon DynamoDB stores chat context, metadata, and user preferences. Amazon S3 acts as the long-term log and transcript repository. Amazon OpenSearch Serverless stores vector embeddings for Retrieval-Augmented Generation (RAG). All data encrypted via AWS KMS with enforced IAM least privilege.
4. Integration & Intelligence Layer
Amazon Translate automates bidirectional translation between supported languages. Amazon Comprehend performs sentiment and entity detection to tailor responses. Langfuse provides analytics and model quality monitoring for Bedrock usage metrics.
5. Monitoring, Security & Governance
Amazon CloudWatch monitors latency, throughput, and token consumption. AWS GuardDuty, WAF, and Security Hub ensure proactive security threat detection. AWS CloudTrail logs API activity for audit purposes. Macie monitors S3 for data exposure risks.
Scalability and High Availability
Elastic Scaling
Lambda functions automatically scale based on invocation rate. EC2 Auto Scaling Group ensures backend instances expand or shrink with traffic spikes. Amazon S3 and OpenSearch Serverless scale automatically as data increases. This architecture ensures steady performance and low latency even during customer surges.
High Availability
Multi-AZ deployment ensures fault tolerance across multiple availability zones. Application Load Balancer (ALB) distributes requests evenly. Cross-region S3 replication maintains redundancy for log data. DynamoDB on-demand capacity mode allows seamless scaling without manual intervention.
Failure Handling and Resilience
- • Lambda retries failed invocations automatically.
- • Amazon SQS Dead Letter Queues (DLQs) capture unprocessed events.
- • CloudWatch alarms notify on service errors or unusual latency.
- • Daily backups to ensure quick recovery from service disruptions.
Performance & Observability
CloudWatch dashboards track real-time KPIs such as request latency, error percentage, and Bedrock token usage. X-Ray traces monitor end-to-end call flows. Langfuse analytics score AI model performance across languages. Regular health reports and optimization sessions ensure proactive improvements.
Security and Compliance
Security and compliance are built into every layer:
- • Encryption: All data in transit (TLS 1.2+) and at rest (KMS).
- • Identity Management: Strict IAM roles, MFA, and Secrets Manager for credentials.
- • Monitoring: Macie for PII detection and GuardDuty for anomaly detection.
- • Governance: CloudTrail and Config enforce compliance with ISO 27001 and GDPR.
Key AWS Services Used
Lessons Learned
Prompt Optimization is an Ongoing Process
Early model outputs showed inconsistent tone in regional languages. Iterative tuning of prompts using Langfuse analytics improved accuracy and conversational fluency by over 20%.
Auto Scaling Thresholds Require Continuous Calibration
Initial scaling policies led to temporary throttling under heavy traffic. Introducing dynamic scaling metrics (using RequestCountPerTarget) stabilized latency.
Data Preparation Quality Impacts Response Accuracy
Cleaning historical FAQs and normalizing multilingual datasets significantly improved embedding retrieval accuracy in Amazon Bedrock Titan.
User Feedback Loop Accelerates Model Adaptation
Incorporating end-user feedback into retraining cycles improved chatbot satisfaction scores from 82% to 94% within two months.
Security by Design Simplifies Compliance
Early integration of GuardDuty, Config, and IAM boundary policies eliminated compliance gaps and reduced audit remediation time by 35%.
Human-AI Collaboration is Key
The best customer experiences occurred when human support agents complemented AI responses during complex queries — leading to a 15% improvement in overall support efficiency.
Summary
The Wyzen Multilingual Support Chatbot represents a production-grade, AWS-native Generative AI implementation that blends scalability, automation, and intelligence. Zaptoz's architecture leverages Amazon Bedrock, Lambda, and Auto Scaling EC2 to ensure elasticity, while AWS-native security and monitoring services guarantee reliability and compliance. The project not only delivered 24×7 multilingual assistance but also positioned Wyzen to scale globally with minimal operational costs.
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