Call for Speakers

Charlotte Data and AI Monthly Usergroup Meetings

in 4 days

Charlotte Data and AI Monthly Usergroup Meetings

planned future dates

20 Oct 2026, 17 Nov 2026, 15 Dec 2026, 19 Jan 2027, 16 Feb 2027, 16 Mar 2027, 20 Apr 2027, 18 May 2027, 15 Jun 2027, 20 Jul 2027, 17 Aug 2027

location

Virtual meetings for now Concord, North Carolina, United States


Charlotte Data & AI is a free, vendor-neutral user group for anyone in the Charlotte area who works with data — engineering, analytics, platform, governance, data science, and the AI work built on top of all of it. Beginners are welcome, and that is deliberate: a room where nobody asks a basic question has stopped teaching anyone anything.


This is a restart. Charlotte has had data user groups before, and each ended the same way — one organizer, one sponsor, one venue, then nothing. We are rebuilding with a bench of organizers so no single person going quiet can stop it.


Three commitments, in writing: no vendor's name in ours, and sponsors rotate; your details are never sold or handed to sponsors, who get audience numbers, never people; and it is free to attend, always.


Our first meetings are online. In-person nights follow, rotating around the metro.

open, 11 months left
Call for Speakers
Call opens at 12:00 AM

01 Sep 2026

Call closes at 11:59 PM

01 Sep 2027

Call closes in Eastern Daylight Time (UTC-04:00) timezone.
Closing time in your timezone () is .

We want to hear from anyone who works with data or AI, on any topic, in any stack. There is no house technology here: a talk about Postgres internals, a Databricks pipeline, a spreadsheet that quietly runs half a business, data governance, a model you shipped, or one you decided not to ship are all equally welcome. What we ask is that you teach something real — what you built, what broke, and what you would do differently. First-time speakers are especially encouraged; our early meetings are online, so you can present from wherever you are, and a well-told story about one hard problem beats a survey of everything. Talks are about ideas and experience rather than products: sponsors have their own slot, and a pitch dressed as a session is not one. Everyone who speaks agrees to our Code of Conduct at clt-data.org/code-of-conduct.


all submitted sessions

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75 submissions
Submitted sessions
Sridhar Muthineni
  • When Health Alerts Cannot Wait for the Cloud: Rethinking the Data Pipeline for On-Device AI
  • Fraud Signal or False Alarm? Data Lessons from Mobile Banking
Vishal Khadake
  • Building Trustworthy Enterprise RAG with Policy Grounding Verification
Sriramu Kundoor
  • Data a Regulator Can Trust: Quality Lessons from Seven FDA Submissions
  • When Daily Data Meets a Visit-Based Standard: Lessons from Reshaping Patient Diary Data
Kedarnath Goud Kothinti
  • AI-Driven Anomaly Detection and Risk Analytics for Digital Payments
Venkata Surya Sai Charan Lolla
  • Real-Time Data in Financial Services: Building Pipelines Regulators and Customers Can Trust
  • Many Agents, One Platform: What Multi-Agent AI Asks of Your Data Infrastructure
Venkateswara Rao Mekanaboina
  • Modernizing Manufacturing Data Flows with SAP O2C and EDI Integration
Venkat Ravi Gelle
  • Your Legacy System Is Blocking Your AI: Event-Driven Data as the Missing Link
  • Scattered Data, Smarter Fraud Detection: Grounding GenAI for AML, KYC, and Claims
Jaimin Suketu Patel
  • Who Owns the Data Once AI Starts Using It? Closing Governance Gaps Across AI Workflows
Joydip Basu
  • Engineering Data-Driven Workflows for Personalized Cancer Therapy
  • Building Trustworthy Healthcare Data with AI-Augmented Master Data Governance
Aditya Ranjan
  • The Data Quality Problem AI Agents Cannot Solve on Their Own.
show all submissions
Amrutha Satishkumar
  • Getting Your Data Ready for AI: From STRATA to Microsoft Fabric
Ayokunmi Sodamola
  • Building an AI-Powered Reporting Tool: Why the Hardest Problems Weren't About AI
Harshit Garg
  • Building AI-Ready Data Platforms: Architecture Patterns for Scale, Reliability, and Governance
Akrambaig Saudagar Saudagar
  • From Requirements to Results: Bridging Business and IT in Financial Services and the Public Sector
Tatiana Maes
  • The Internet Was Built for Humans. AI Agents Need a Different Web.
Brahmanand Reddy Bhavanam
  • From Fragmented Healthcare Data to Trustworthy AI: A Practical Architecture
  • Secure by Design: Building Healthcare APIs for Data and AI Workloads
Aishwarya Natarajan
  • From Paper Manuals to Plant-Floor Answers: Data Lessons from an AI Operator Assistant
Ankit Pathak
  • Who Fails Next? Using Graph Machine Learning to Model Cascading Defaults
Sarvendra Aeturu
  • Can AI Care? Engineering Empathy and Ethics into Real-World Data and AI Systems
  • Explain It or Don't Ship It: Practical Interpretability for Data and AI Teams
Naveen Kumar Govindaraju
  • Migrating a Legacy CRM Without Losing Data Trust: Lessons from a Siebel-to-Salesforce Move
Abhishek Kakkar
  • Building an AI-Ready Data Foundation: Lessons from Enterprise Data Modernization
Shashwat Pandey
  • Can an LLM Tell When It Is Wrong? Single-Pass Uncertainty with SELFDOUBT
Ajay Srinivas Kiran Gemidi
  • Architecting AI Analytics Without Breaking Mission-Critical Data Platforms
Soumya Gummalla
  • From 30+ ML Models to Production: Building Reusable MLOps Pipelines
Srinath Chintala
  • Building Trusted Healthcare Analytics from Fragmented Data
Nitesh Khapekar
  • Enterprise Analytics Beyond Dashboards: Intelligent Data Orchestration with LLMs
Mrugesh D. Kharwar
  • When the Pipeline Runs but the Number Is Wrong: Engineering Data Trust for BI
Gayathri Ramoju
  • From Data Mismatches to Trusted Reports: Practical Data Validation in the Real World
Abhishek Vangipuram
  • From Data to Strategy: Integrating Machine Learning with Business Intelligence
Mayank Sethi
  • Static Masking Doesn't Scale: An AI Driven Approach to Dynamic Data Masking
Scarlett Attensil
  • How Many Judgments Can One LLM Make Before It Fails?
Amish Desai
  • Measuring What Matters: A Data Framework for Sustainable Corrective Action
Vineel Bala
  • Zero Trust for the Data Layer: Federated vs. Centralized Lessons for AI-Era Platforms
Marquita Dunn
  • From Messy Tickets to Useful Data: What I Learned Building My First Python Analysis Tool
Vaidehi Sinha
  • When the Pipeline Runs but the Data Is Wrong: Engineering Data Quality Into Enterprise Workflows
Sandeep Gusain
  • From Enterprise Portals to Agent Surfaces: Designing for Human-AI Collaboration
Varija Singh
  • Can AI Reduce the Manual Work in Data Governance? Lessons from a Real-World Pilot
Alex Merced
  • The Open Agentic Lakehouse: Building an Open Data and AI Ecosystem
Pruthvi Tatikonda
  • From ETL to Modern Data Engineering: A Paradigm Shift in Enterprise Data Architecture
Innokenty Bodrov
  • From AI Recommendation to Human Decision: A Decision Ledger for Regulated Workflows
Onkar Khadke
  • Using AI to Build More Resilient API Contracts
Naga Ravindranatha Reddy Mallipeddi
  • Validation-as-You-Migrate: Quality Gates for Cloud Data Platforms
Ajibade Hammed
  • The Answer Looked Right: Building an Evidence Trail for AI-Assisted Engineering Decisions
Sagar Rijhwani
  • Debugging RAG: Tracing Wrong Answers Back to Your Data
Dr. JR Andrews
  • Ten Leads Nobody Called: What My Automation Shouldn't Have Decided
Naga Chand Putta
  • Modernizing 150+ Legacy Batch Jobs: Lessons from a Cloud-Native Data Journey
  • From ETL to Intelligent Pipelines: Putting LLMs to Work in Data Engineering
Ramakrishna Pittu
  • AI-Powered Database Observability: From Reactive to Predictive Operations
Hastimal Jangid, Harsh from RankRabbit
  • When Retrieved Data Gives Instructions: A Prompt Injection Case Study
  • From Laptop to Container: What It Takes to Run a Local AI Agent Reliably
  • A Citation Is Not a Quality Score: Evaluating Grounded AI Answers
  • Twenty Search Results, Eleven Places: The Data Work Behind an AI Recommendation
  • One Question, Many Searches: What Query Fan-Out Actually Changes
  • Beyond RAG: Building Agentic Healthcare Search with Evidence You Can Inspect
Viral Pala
  • Compliance as Code: Continuous Validation for AI and Data Platforms in Multi-Cloud Environments
Praful Konduru
  • The Answer Looks Right: Finding Retrieval Failures in a Small RAG App
Pratham Yadav
  • Building an AI-Ready Semantic Layer for Analytics and RAG
  • What Happens Between the Database and the Dashboard?
  • Schema Drift Is a Data Engineering Problem, Not an AI Problem
  • Data Engineering for AI: What Changes When Your Pipeline Feeds an LLM?
  • When the Data Pipeline Breaks: Designing for Data Quality, Not Just Data Movement
Nikhil Anu
  • Teaching an AI Your Product’s Design Language
  • Building an AI Agent That Answers from UK Government Guidance
Kandasamy Sellappan
  • Chasing Latency: What Actually Moved the Needle on a High-Volume API
  • What Broke When We Connected a Cloud Migration to Legacy Data Warehouses
Arun vivek Supramanian
  • Ethical Data Engineering: Building Privacy and Fairness into AI Pipelines
Blaize Stewart
  • RAGged and Compromised: Hacking Retrieval-Augmented Generation Systems
Jai Chandra Mouli Langoju
  • AI-Assisted Semantic API Contract Validation for Resilient Microservices