Prasad V.

I bridge the gap between autonomous AI agents and scalable SaaS products. Most developers deliver an AI script; I deliver a production-ready AI business. I architect and deploy "Agentic SaaS"—software platforms where the core value is driven by autonomous, self-healing agent workflows. From designing complex LangGraph orchestration to building the Next.js 15 frontend and multi-tenant backend, I provide the end-to-end infrastructure required to launch a commercial AI product. I specialize in moving beyond "wrappers." My systems are designed for high-stakes environments where agents must reason, use tools, self-correct, and interact with real-world enterprise data—all wrapped in a polished, secure, and user-centric SaaS interface. Core Specialized Capabilities 1. Agentic SaaS Infrastructure I build the "Body" that powers the "Brain." I handle the full-stack complexity of AI productization: Scalable SaaS Foundations: Implementing multi-tenancy, secure Authentication (Clerk/NextAuth), and complex Billing (Stripe) to turn AI logic into a viable subscription product. Production-Grade Stack: High-performance web applications built with Next.js 15, FastAPI, and PostgreSQL (pgvector). Deployment & Ops: Containerizing AI workloads with Docker/Kubernetes and deploying to AWS/Vercel for 99.9% uptime. 2. Autonomous Agent Orchestration (LangGraph & MCP) I design the "Brain" using state-of-the-art agentic frameworks: Self-Healing Workflows: Agents that don’t just fail; they critique their own output, perform autonomous query expansion, and re-attempt tasks until successful. Multi-Agent Swarms: Designing supervisor/worker architectures where specialized agents collaborate on end-to-end business lifecycles (e.g., from raw data ingestion to finished SEO reports). Tool-Use & MCP: Integrating agents with proprietary data and third-party APIs via the Model Context Protocol (MCP) for deep ecosystem integration. 3. Advanced Knowledge Intelligence Eliminating hallucinations and context limits for enterprise-level accuracy: GraphRAG & Hybrid Search: Combining Neo4j Knowledge Graphs with Vector databases for deep, multi-hop reasoning that standard RAG misses. Evaluation & Guardrails: Implementing RAGAS and Arize Phoenix to quantify faithfulness and ensure your SaaS provides facts, not noise. Technical Stack AI/Agents: LangGraph, CrewAI, LangChain, DSPy, MCP, LangServe. SaaS Full-Stack: React.js , TypeScript, FastAPI, TailwindCSS, ShadCN/UI. Models: Claude 3.5 Sonnet, GPT-4o, Gemini 2.0, DeepSeek, Llama 3.1. Data & Vector: Pinecone, Weaviate, Neo4j, Supabase, PostgreSQL. Infrastructure: Docker, Kubernetes, AWS, Vercel, GitHub Actions.