kalpesh Rathod
Trustworthy AI in High-Stakes Enterprise Workflows & Automation.
Abstract:
AI is rapidly moving from experimentation to production across enterprise systems—yet high-stakes workflows demand more than accuracy.From Document Intelligence to Audit-Ready Automation processes where decisions impact compliance, legal exposure, and financial outcomes, AI must be measurable, traceable, and governable. This keynote presents a practical blueprint for deploying trustworthy AI in real workflows, using document intelligence as the most common entry point: extraction, confidence scoring, validation, human-in-the-loop review, and safe fallbacks. It also highlights what makes AI “audit-ready,” including evidence logging (inputs/outputs/decisions), versioning of models and prompts/configurations, and exception handling that supports accountability. A brief industry example—AI-assisted patent drafting—illustrates how AI can accelerate work while humans retain responsibility for defining the inventive concept, verifying support, and ensuring quality. Attendees will leave with an actionable 30/60/90-day roadmap to ship AI systems that scale reliably and withstand governance and audit expectations.
Profile:
Kalpesh Rathod is a Director of Engineering and technology leader specializing in building enterprise Legal IP SaaS platforms and automating high-stakes workflows using cloud, data, and AI. He has over 15+ years of experience leading engineering teams across modern architectures, including workflow automation, document intelligence, scalable cloud systems, and governance-ready delivery practices. Kalpesh is a core contributor at Lecorpio LLC (an Anaqua company), where he has led critical initiatives that support global IP operations through automation, reliability engineering, and intelligent systems design. He is also a named inventor on a U.S. patent application focused on best-practice-based budgeting systems, reflecting his interest in applying data-driven methods to complex enterprise decision-making. Kalpesh writes and speaks about practical, production-grade AI—especially how organizations can deploy intelligent systems with confidence scoring, human-in-the-loop validation, traceability, and audit-ready evidence. His work bridges engineering execution with real-world requirements around compliance, accountability, and measurable business impact.