GENAI & INTELLIGENT AGENTS
IN THE ENTERPRISE

AUSTIN | February 18, 2026

DSS AUSTIN | Schedule

Wednesday, Feburary 18

 

GRAB YOUR EARLY BIRD PASS

8:30 – 9:55am ET

Registration Opens

9:55 – 10:00am ET

Introduction
Anna Anisin - Founder, Data Science Salon

10:00 – 10:30am ET

Multi model approach to large scale information retrieval
Dushyanth Sekhar - Head of AI & Data Platforms - Enterprise Data Organization ( EDO) at S&P Global

As AI adoption grows, relying on a single LLM for data extraction can lead to inconsistency and bias. This session explores how using multiple LLMs in parallel—combined with an automated LLM-as-a-Judge framework—can boost accuracy, reduce bias, and improve trust in extraction pipelines. Learn how to build scalable, resilient systems for unstructured data extraction without heavy human oversight.

10:30 – 10:50am ET

Trust as an AI Enabler
Cal Al-Dhubaib - Responsible AI & ML Executive at Further

Abstract coming soon

10:50 – 11:20am ET

Coffee Break

11:20 – 11:50am ET

Practical Insights on Building Production Edge AI Systems
Samaresh Kumar Singh - Principal Engineer at HP Inc.
As AI adoption accelerates, many organizations discover that cloud-only inference cannot meet the latency, bandwidth, privacy, and availability requirements of real-time systems. This talk presents a practical, production-oriented blueprint for moving inference to the edge running AI where data is generated while keeping the cloud for training, governance, and continuous improvement. We introduce a three-tier cloud–edge–device architecture and discuss how to orchestrate heterogeneous hardware (CPU/GPU/NPU/TPU/DPU) using modern scheduling patterns: centralized control, distributed consensus, hierarchical federation, and increasingly, agent-based coordination for autonomous edge deployments. The session dives into low-latency strategies (quantization, distillation, hardware-specific runtimes, caching, dynamic batching, early-exit inference, and pipeline parallelism) and resilience-by-design techniques (offline-first operation, local persistence, eventual consistency, and graceful degradation using MQTT/NATS, CRDT-inspired state handling, and circuit-breaker patterns). Finally, we map these ideas to real-world smart-city and industrial scenarios predictive maintenance, healthcare monitoring, autonomous perception, and smart-building optimization highlighting the trade-offs and implementation decisions that enable dependable edge AI at scale. Attendees will leave with actionable architectural patterns, optimization tactics, and reliability practices to build secure, portable, low-latency AI systems across the cloud–edge continuum.

11:50 – 12:20pm ET

From Prompts to Production: Building Reliable AI Agents for the Enterprise
Hamed Alikhani - Senior AI Engineer | Founder at Austin AI Hub

Agentic AI is no longer just a proof of concept. This session dives into how enterprises can design and deploy intelligent agents that are reliable, scalable, and safe in real-world environments.

Drawing from real production rollouts, we’ll unpack key architecture patterns—from task decomposition and tool orchestration to memory and routing logic. You’ll learn how to move past brittle prompt chains, implement robust validation layers, and design agents that deliver measurable outcomes while aligning with enterprise governance and cost constraints.

Perfect for technical leaders and practitioners ready to operationalize AI agents at scale.

12:20 – 12:40pm ET

Building a Culture of Evidence: Data Science, Experimentation, and Business Impact
Deeksha Mishra - Data Science Manager at Meta
As data-driven decision-making becomes a core strategic advantage, organizations are embedding experimentation—especially A/B testing—across product and business lifecycles. Unlike observational data, experimentation reveals causality, helping teams validate ideas, reduce risk, and drive evidence-based alignment. In this talk, I’ll share how structured experimentation helped the Instagram Delivery Data Science team connect system performance improvements to user engagement and revenue impact. Learn how to build rigorous, scalable experimentation programs that turn data into confident, high-impact decisions.

12:40 – 1:40pm ET

Lunch

1:40 – 2:25pm ET

Panel: From Prototype to Production: Building AI That Actually Works in the Enterprise

Cal Al-Dhubaib - Responsible AI & ML Executive at Further, Akshay Mittal - Member of Technical Staff Software Engineer at PayPal, Shivika Bisen - Senior Data Scientist, Gen AI Products at Viasat, Brent Schneeman - SVP of Artificial Intelligence at The SSI Group, Fatma Tarlaci - Chief AI Officer at Soar.com

2:25 – 2:55pm ET

Stop Building Generalists: Architecting Task-Specific Agents for Real-World Enterprise ROI
Dippu Kumar Singh - Leader Of Emerging Data Technologies at Fujitsu North America Inc.
Generic LLMs often fall short in complex enterprise workflows. This session introduces AI Integration—a framework for replacing one-size-fits-all bots with task-specific agents that plug directly into high-value business processes like audits, supply chain ops, and SaaS platforms.

We’ll explore how to use Prompt Chaining, Semantic Routing, and Evaluator-Optimizer loops to design agents that are accurate, composable, and embedded into real systems. You’ll leave with a clear taxonomy of agent types and a practical roadmap for architecting AI systems that drive measurable ROI.

2:55 – 3:15pm ET

Scaling Responsibly: Building Data & AI Governance Frameworks for Fintech Startups That Grow into Enterprises
Reema Gill - Data/AI Governance Specialist at Wealthsimple Technologies
Fintech startups move fast—but as data access, AI adoption, and regulatory exposure grow, governance can’t wait. This session explores how emerging fintechs can implement right-sized AI and data governance frameworks that evolve with scale—without slowing innovation.

Drawing from experience across banks and high-growth startups, we’ll discuss how to operationalize global standards (EU AI Act, NIST AI RMF, OSFI E-23) to build trust with regulators, investors, and customers. The goal: make governance a catalyst, not a constraint.

3:15 – 3:45pm ET

Transforming Business with Agentic AI
Joyjit Roy - Lead Principal Technical Program Manager at USAA
This session explores how Agentic AI architectures move beyond rule-based workflows to enable enterprise-scale decision automation through adaptive planning, contextual memory, and closed-loop evaluation. It outlines key architectural components—guardrails, task decomposition, execution control, and feedback-driven refinement—and shows how these systems maintain auditability while operating under uncertainty. Real-world examples from eCommerce, insurance, and healthcare highlight measurable improvements in personalization, underwriting, fraud detection, and clinical coordination, with a focus on governance, human oversight, and production-ready deployment.

3:45 – 4:05pm ET

Coffee Break

4:05 – 4:35pm ET

Inside Agentic Architecture: Real-Time Voice Automation Powered by RAG and Sentiment AI
Hari Kishan - Director of Cloud Engineering at Manulife John Hancock Retirement
While many enterprises talk about conversational AI, few have deployed it at scale—let alone in a highly regulated financial setting. At Manulife, that’s exactly what we’ve done.

This session dives into how a legacy Avaya IVR system was reimagined into an agentic, self-optimizing platform powered by Amazon Connect and RAG pipelines. Learn how we built SSML models that adjust tone and phrasing in real time, reduced AHT, and introduced an orchestration layer that updates strategies in minutes—not weeks.

If you’re scaling GenAI, modernizing CX, or looking to deploy agentic architectures in enterprise environments, this is your blueprint.

4:35 – 4:55pm ET

AI for Protecting Patient Data (PHI Security)
Yukti Goyal - Advanced Software Engineer at FM
As healthcare data grows in volume and value, protecting PHI and ePHI has never been more critical—or more complex. In this session, we’ll explore how AI is transforming cybersecurity in healthcare, with advanced monitoring, anomaly detection, and adaptive defenses tailored for compliance-heavy environments like HIPAA.

Learn how techniques like UEBA, unsupervised learning, and AI-driven threat detection are being used to detect insider threats, stop credential misuse, and build resilient, compliant frameworks for modern healthcare systems.

If you’re working at the intersection of healthcare, AI, and data protection—this session is for you.

4:55 – 5:25pm ET

Using LLM to improve email marketing
Preetham Kaukuntla - Staff Data Scientist at Glassdoor

Traditional rule-based notification systems fall short at scale. In this session, Preetham Reddy Kaukuntla shares how Glassdoor rebuilt its email and push infrastructure using ML models for both content and timing optimization. By integrating transformer-based subject line generation, LSTM and Prophet models for send-time prediction, and uplift modeling, Glassdoor now delivers 32M+ daily messages with measurable engagement gains. The session covers architecture, feature engineering, and lessons learned on model evaluation and long-term user impact.

5:25 – 5:35pm ET

Closing Remarks
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