Rakesh Navale
Exploring, researching AI, LLM, Security, MCP, and Developer productivity
Exploring how AI, LLMs, and platform engineering come together to build safer, more productive developer experiences. I work on model-context protocols (MCP), secure knowledge workflows, and tooling that turns complex systems into reliable, explainable workflows for engineers.
Latest Insights
View All Posts →AI Security Is No Longer Optional
AI moved from cute demos to critical infrastructure in under three years. Along the way, it picked up a real attack surface: data leakage, deepfakes,...
Read ArticleBeyond Basic HTTP MCP: Production Security Enhancements
The Model Context Protocol specification provides HTTP transport basics. This article shows the critical security enhancements needed for production AI knowledge systems: MISE authentication replacing...
Read ArticleSecuring AI Knowledge Access: HTTP MCP Server Architecture for Enterprise
AI assistants accessing corporate knowledge bases introduce security challenges that traditional API architectures don't address. This article explores production-tested patterns for building HTTP MCP servers...
Read ArticleBuilding STDIO MCP Servers: What I Discovered
A practical guide to building production-ready STDIO MCP servers. Learn the technical patterns, architectural decisions, and gotchas that emerged from real implementations, complete with working...
Read ArticleBeyond the Tutorial: STDIO MCP Servers
Why most MCP servers fail in production, and the architecture that actually works. Learn the patterns that emerged from building production-grade STDIO MCP servers in...
Read ArticleTechnical Expertise
Distributed Systems
Designing and implementing large-scale distributed architectures with focus on reliability, consistency, and fault tolerance.
AI/ML Platforms
Building production-grade machine learning infrastructure and platforms for model deployment, serving, and monitoring.
Performance Engineering
Optimizing system performance through profiling, bottleneck analysis, and implementing efficient algorithms and data structures.
Cloud Architecture
Architecting cloud-native applications with modern DevOps practices, containerization, and orchestration platforms.
Featured Work
Knowledge & Documentation Hub
Built a vector-based knowledge hub with LLM-driven retrieval, integrated into developer tools to surface contextual documentation and recommendations.
Model Context Protocol (MCP)
Designed a secure, role-aware protocol to provision context to LLMs and agents, enabling safe, contextual AI interactions across tools.
Cloud-Ready Developer Environments
Delivered Windows/Linux dev VMs and a lifecycle CLI to automate provisioning, customization, and onboarding for engineering teams.
Copilot Connectors
Built integrations that expose semantically indexed code and knowledge context to Copilot and other LLM interfaces for in-flow assistance.