NeuralOps — AI-Powered Incident Analysis Platform
AI-driven incident response platform that reduces MTTR by autonomously triaging production errors. Built with a scalable event-driven microservices backend (Kafka, Redis), integrating advanced LLMs for code-level diagnostics and pgvector for semantic knowledge retrieval.
Architecture
CQRS Dual-Stack
Data Pipeline
Transactional Outbox
Observability
Prometheus
Architected a multi-tenant AI debugging platform in FastAPI/Django to speed up root-cause analysis by leveraging intelligent LLM-powered diagnostics.
Engineered a zero-data-loss, asynchronous event streaming pipeline using the Transactional Outbox pattern with Debezium CDC and Apache Kafka to synchronize state across distributed PostgreSQL databases.
Deployed LangGraph agents with RAG pipelines and WebSockets for real-time AI diagnostics with JWT/RBAC-based multi-tenant security.
Developed a zero-dependency, fault-tolerant Python SDK featuring circular memory buffering, edge traceback extraction, and an exponential-backoff retry queue to securely stream crash context without impacting host application performance.
Automated Infrastructure as Code with Terraform and deployed Prometheus/Grafana observability stack on AWS and Kubernetes to ensure high availability and distributed systems reliability.
Tech Stack
