Raghu Charan Vanaparthy — AI Researcher

Few years ago, I started my journey in AI believing that the future of enterprise software isn't about replacing systems—it's about making them intelligent. Today, at Capgemini's CTO Team, I prove this thesis daily by building multi-agent AI platforms that process 90,000+ weekly transactions while reducing processing times from 30 days to 5–3 days—all without requiring complete infrastructure overhauls. The Problem I Solve Most enterprises struggle with a dilemma: their legacy systems work, but they're slow, manual, and can't keep pace with modern demands. Complete replacements are risky, expensive, and often fail. My approach? Build intelligent layers on top of existing infrastructure using autonomous AI agents that understand context, make decisions, and orchestrate complex workflows. What This Looks Like in Practice Multi-Agent Orchestration Leading a team of 8 developers, I architected a serverless platform with autonomous pilots for warranty claim processing—intake, validation, fraud detection, decisioning, and settlement. The result? 90K+ claims/week at enterprise scale with 10× faster resolution cycles. Production RAG Systems Built hybrid search pipelines across 2M+ documents using pgVector. Achieved 35% accuracy improvement with sub-200ms latency. Intelligent Pre-Validation Developed agents using LangChain and LangGraph that detect incomplete submissions and generate explainable rejection reasoning. Reduced monthly rejections from a 300K baseline. Conversational AI That Works Transformed traditional travel APIs into context-aware conversational agents delivering hyper-personalized itineraries. The impact: 60% reduction in manual effort, 45% CSAT improvement, and 40% faster response times. I believe in: Production-first thinking → 99.9% uptime SLA, not just demos Cost-conscious architecture → 25% infrastructure cost reduction while scaling Security by design → Encryption, guardrails, auditability from day one My Stack: Frameworks: LangChain • LangGraph • CrewAI • PyTorch • Transformers Cloud: AWS Bedrock • Azure AI Foundry • SageMaker • Lambda Data: PostgreSQL • pgVector • Redis • Vector DBs MLOps: MLflow • Docker • CI/CD • Kubernetes Development: Python (Advanced) • FastAPI • React.js • gRPC/REST Beyond the Code 📜 AWS Certified Cloud Practitioner 📜 Azure Data Scientist Associate (DP-100) ✍️ Gen AI Content Creator sharing production insights on LinkedIn I'd love to connect and explore how we can collaborate. 📧 raghucharanv609@gmail.com 💬 DM open for consulting, strategic partnerships, and impactful projects

Stackforce AI infers this person is a Fintech and AI Automation specialist with a focus on enterprise solutions.

Location: Hyderabad, Telangana, India

Experience: 2 yrs 10 mos