Building agentic AI systems that reason, retrieve, and act.
I'm Pranesh Kannan — an AI Engineer and Data Scientist currently at IIT Madras, designing production LLM pipelines, RAG systems, and multi-agent workflows for enterprise supply chains and data platforms.
From electrical engineering to agentic AI.
I started out with a B.E. in Electrical & Electronics Engineering from Kongu Engineering College, then moved into data science and generative AI — first through document-intelligence and computer-vision work, later into large-scale defence imaging systems, and now into agentic AI research at IIT Madras.
My focus today is on LLM-powered agents: designing orchestration logic, tool-calling with ReAct reasoning, and RAG pipelines that turn unstructured enterprise data into grounded, autonomous decisions — for supply chain management and data engineering platforms.
Along the way I've picked up MLOps discipline (MLflow, Docker, Kubernetes), a habit of writing responsibly governed AI, and — through my TA work — a real interest in explaining this stuff clearly to other people.
Selected work.
Seven production and research projects spanning agentic orchestration, RAG, computer vision, and document intelligence — across IIT Madras, DRDO‑CVRDE, and ELAIT.
Supply Chain Decision Agent
An LLM-powered, agent-based model for autonomous supply chain decision-making — combining orchestration logic, MCP tool-calling, and ReAct reasoning to plan and execute multi-step workflows without manual intervention.
Agentic Data Product Layer
A modular pipeline that automates data preprocessing, annotation, ETL, and enrichment on Flink/Kafka, grounded by Databricks and Azure AI Search + RAG — with built-in model evaluation, versioning, and production monitoring.
Image Classification & Detection using Transformers
Fine-tuned Vision and Detection Transformers for real-time image classification and object detection, deployed on an NVIDIA Jetson Nano for on-device live video analysis with OpenCV.
Vehicle Health Monitoring
ML models for defence vehicle fault classification and remaining-useful-life prediction, using sensor-data preprocessing and anomaly detection to catch issues before failure.
Profile Analytica
An AI-driven document ingestion pipeline combining OCR and NLP for automated resume data extraction, validation, and LLM/BERT-based parsing, summarization, and semantic matching on a scalable MongoDB backend.
Guardrailed LLMs for Test Automation
Fine-tuned, privacy-preserving LLMs (Mistral, LLAMA3) with guardrail configurations and RAG–BM25 retrieval, enabling local, token-efficient inference for SQL and Python code understanding.
Product Recognition System
A visual product-recognition system using ANN and OpenCV for image similarity, with BERT-based vectorization of product names to recommend and match related products from an uploaded photo.