PROJECTS
CALEPOFT
A production-grade full-stack fitness platform built on a microservices architecture with event-driven async communication, observability tooling, and a normalised relational database — live at calepoft.com.
- Built the platform using Next.js 16 App Router with TypeScript, structuring authenticated routes via route groups, implementing server and client components, and using Next.js API route handlers as a proxy layer between the React frontend and backend microservices.
- Designed a normalised PostgreSQL schema with ACID-compliant transactional guarantees (BEGIN/COMMIT/ROLLBACK), leveraging complex JOIN-based aggregations and nested JSON responses to efficiently serve structured API workloads across services.
- Built an event-driven system using Apache Kafka for asynchronous communication, implementing idempotent event processing alongside schema validation (Zod) to ensure correctness under at-least-once delivery, retries, and message replays.
- Orchestrated a multi-service Docker Compose environment with Nginx reverse proxying, enforcing service isolation, health checks, and secure non-root container execution to improve deployment reliability and operational robustness.
- Implemented an observability stack using Prometheus and Grafana, instrumenting services with metrics and logs to support performance monitoring, debugging, and reliability analysis across the distributed system.
CONVERSA
A real-time business conversation assistant leveraging AWS serverless infrastructure and LLM-powered suggestion generation to deliver timely, contextual insights during live business calls via a chatbot interface.
- Integrated AWS Transcribe and Lex for speech-to-text processing, with Lambda and API Gateway powering serverless backend computations for low-latency transcription and intent detection.
- Orchestrated LLM calls on Groq Cloud using prompt templates to generate timely, actionable, and impactful suggestions in a business context from live transcript data.
- Trained a HuggingFace multi-class text classifier (Zero-Shot Classification) to label buffered transcript segments, driving the trigger logic for delivering contextual suggestions on the chatbot interface.