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Promise Keepers
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Last saved: May 09 at 5:21 PM CDT
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Madhusudhan Kandula Team Lead RSVP Approved
Lead Technical Consultant at Easypost
I designed and built the full “Uni at Your Assistance” prototype end to end. I defined the retail customer support use case, created the order/tracking/service recovery demo scenarios, and implemented the dynamic 30% chat + 70% generative UI workspace.
I built the FastAPI + LangGraph backend, including intent detection, order data tools, carrier tracking/promise evaluation, policy retrieval, LLM-generated customer explanations, and an LLM UI planner that selects approved A2UI-style components. I also built the Next.js / React frontend with reusable components for order selection, promise dashboards, delivery proof, service recovery, wrong-delivery claim forms, and claim confirmation.
I integrated CopilotKit, AG-UI-style streaming progress events, and an official AG-UI endpoint using HttpAgent to connect the agent backend with the frontend experience. I also handled debugging, GitHub setup, deployment readiness, and the final demo flow.
I am a Senior Solution Architect and Technical Product Manager with 13+ years of experience in retail, eCommerce, OMS, fulfillment, and supply chain transformation. Based in the Austin area, I focus on bridging legacy enterprise systems with modern AI-driven product innovation.
I am currently building Allocation Intelligence, an agentic AI and RAG-driven governance layer that augments traditional OMS platforms to support profit-aware, explainable fulfillment decisions. Beyond enterprise architecture, I actively prototype AI applications across retail, productivity, and everyday consumer use cases. My work is driven by transforming static operational rules into dynamic, context-aware decision systems that create measurable business and user value.
Interested in building AI-driven decisioning systems for supply chain, OMS, and fulfillment, including agentic workflows, RAG-based policy retrieval, and evaluation frameworks. Also exploring consumer-focused AI apps like Invite Agent (event planning), in-store shopping navigation, and BMI health insights—solutions for everyday users. Looking to learn from builders of production AI systems and connect with collaborators across enterprise and consumer AI.
I am prototyping Allocation Intelligence, an Agentic AI and RAG-driven governance layer for Order Management Systems (OMS). Traditional engines excel at deterministic routing but often make decisions that cause profit leakage due to a lack of real-world context like localized inventory velocity or labor capacity. My project intercepts allocation traces and evaluates them against dynamic enterprise data to generate an explainable decision-quality report with margin-aware recommendations.