Sravan Modugula
Co-founder & CTO, AIONDATA
Building enterprise AI that actually works.
About
I help organizations transform complex data into competitive advantages, architecting high impact AI strategies for global leaders like Autodesk, Delta Dental, and First Citizens Bank. My focus is on ensuring that machine learning initiatives drive both top-line revenue and long-term client loyalty.
Before AIONDATA, I served as Executive Director at JPMorgan Chase, where I led the modernization of large-scale data infrastructures, pioneering AI/ML and cloud-native solutions across Data Lakes and MPP environments. At First Republic Bank, I spent nearly eight years progressing from Director of Software Engineering to Senior Director of Technology, driving cloud migration, digital banking, and enterprise data platform strategy.
I hold a Wharton Executive Program certification in Organizational Leadership, I am an ACM Fellow, IEEE Senior Member, and IEEE Standards Association member.
Career
2024 - Present
Co-founder & CTO
AIONDATA
Building AI-first enterprise software: Lexicon (semantic data intelligence), Agentic Studio (document AI), and SynergyOS (modular ERP). Architecting end-to-end AI and data strategies for enterprise leaders including Autodesk, Delta Dental, and First Citizens Bank.
2023 - 2024
Executive Director
JPMorgan Chase & Co.
Led strategic data and analytics initiatives, modernizing large-scale data infrastructures. Pioneered AI/ML and cloud-native solutions across Data Lakes and MPP environments, delivering transformative results across diverse banking sectors.
2015 - 2023
Senior Director of Technology
First Republic Bank
Progressed from Director of Software Engineering to Senior Director of Technology over 8 years. Drove cloud migration, digital banking, and enterprise data platform strategy using Snowflake, Denodo, and AI/ML.
2021
Executive Program, Organizational Leadership
The Wharton School
Executive education in organizational leadership at the University of Pennsylvania.
2001 - 2005
B.Tech, Electronics & Communications Engineering
Jawaharlal Nehru Technological University
Bachelor of Technology in Electronics and Communications Engineering.
Products Built
Enterprise AI products I have architected and shipped at AIONDATA.
Lexicon
Semantic data intelligence platform. Natural language queries, knowledge base RAG, 35+ connectors, embeddable SDK. Ask your data anything.
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Agentic Studio
AI-powered document processing. Multi-model orchestration that automates extraction, classification, and routing.
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SynergyOS
Modular AI-native ERP. Financials, supply chain, HR, CRM, and governance unified in one platform. 50K+ active users.
Learn moreLatest Writing
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Data Mesh Architecture: A Practical Playbook for Enterprise Data Leaders
Data mesh promises to fix slow, centralized data platforms by pushing ownership closer to the business. But most enterprises struggle to move from slideware to a workable implementation. This guide breaks down data mesh into practical steps, with concrete recommendations for financial services, healthcare, insurance, and infrastructure organizations.
C‑Suite Scorecard: The 10 KPIs That Actually Matter for Enterprise AI
Most enterprise AI dashboards are cluttered with vanity metrics that don’t help executives make decisions. This scorecard focuses on 10 practical KPIs that connect AI investments to revenue, risk, and operational performance across financial services, healthcare, insurance, and infrastructure. Use it to align your AI strategy, platform roadmap, and delivery teams around measurable business impact.
Building Scalable ML Pipelines for Regulated Industries: A Practical Blueprint
Financial services, healthcare, insurance, and infrastructure firms are moving from isolated models to production-grade machine learning at scale. This post walks through how to design ML pipelines that are resilient, compliant, and efficient across teams and business lines. We focus on practical patterns, architecture choices, and operating models that technical and business leaders can use today.
Building Responsible AI in Regulated Industries: From Principles to Practice
Regulated industries cannot afford experimental AI. They need systems that are accurate, auditable, and aligned with evolving regulation across jurisdictions. This post outlines a practical approach to responsible AI implementation for financial services, healthcare, insurance, and infrastructure organizations, with concrete steps for CXOs, data leaders, and AI platform teams.
From Notes to Insight: How Large Language Models Are Reshaping Healthcare Analytics
Large Language Models (LLMs) are moving from experimentation to real impact in healthcare analytics, unlocking value in unstructured clinical text, patient communication, and operational data. For leaders across healthcare, insurance, financial services, and critical infrastructure, the opportunity is to combine LLMs with governed data platforms and safe integration patterns to drive measurable outcomes, not prototypes.
Designing an AI‑Ready Enterprise Architecture for 2026
By 2026, AI will be embedded into the core of financial services, healthcare, insurance, and infrastructure operations—not just bolted onto existing systems. This post outlines a practical reference architecture and concrete design choices enterprises can make today to become AI‑ready, resilient, and compliant at scale.
What People Say
“Sravan is one of the most talented technologists I have worked with. His ability to translate complex data challenges into actionable enterprise solutions is exceptional. He combines deep technical expertise with genuine business acumen.”
James Hughes
Chief Information Officer, First Republic Bank
“Working with Sravan, you quickly realize he operates at a different level. He does not just build technology; he thinks about how technology creates lasting competitive advantage. His work at AIONDATA reflects that philosophy.”
Ted Lifset
Managing Director, JPMorgan Chase
Let's Connect
Interested in enterprise AI, data platform strategy, or what AIONDATA is building? I am always happy to connect.