MathCo Spark AI 2026

What’s in it for you?

In this edition of Spark AI, we'll take a deep dive into Thinking Architecture - MathCo's breakthrough approach to solving one of the biggest challenges in Enterprise AI: achieving scale.

Thinking Architecture brings together AI, context, intelligence, and decision-making into a unified framework designed to help enterprises move from isolated successes to scalable impact. Inspired by the way human brain processes and connects information, it offers a new way to unlock enterprise-wide intelligence at scale. Gain early access to this bold new perspective on Enterprise AI and the principles shaping its evolution.

Join industry leaders, AI practitioners, and innovators as we explore the ideas, innovations, and possibilities shaping the future of Enterprise AI.

P.S. Submit your paper for Spark AI 2026 to get published and receive a job offer from MathCo.

What’s happening in Enterprise AI
10:30 a.m. - 11:00 a.m.

Keynote - Masterclass with Databricks
11:00 a.m. - 11:30 a.m.

Panel Discussion – Get to the other side with AI Leaders
11:30 a.m. - 12:00 p.m.

Tech Talk – Understanding the Thinking Architecture Framework
12:15 p.m. - 12:45 p.m.

Networking Lunch
01:00 p.m. - 02:00 p.m.

Paper Presentations & Mentorship Counselling
02:00 p.m. - 03:00 p.m.

Opening Note 10:30 a.m. – 10:45 a.m.

Welcome to Spark AI

Sayandeb Banerjee, Cofounder & CEO, MathCo

What does it take to build an AI-first enterprise? Sayan sets the stage for the day, before we dive into the challenges, the opportunity, and why the decisions made now will define the next decade of enterprise AI.

Keynote 1 10:45 a.m. – 11:05 a.m.

Intelligence That Compounds: Why Fragmented AI Never Scales

Aakarsh Kishore, Chief Product Officer, MathCo

Most enterprise AI models run in fragments: a copilot here, a forecast there, and the pieces never compound into one system. The result is familiar: high accuracy, low adoption; more AI, not smarter AI.

In this session, unpack Thinking Architecture: a supercharged brain for enterprises to build connected intelligence and achieve scale.

Keynote 2 11:10 a.m. – 11:40 a.m.

AI Tipping Point: The Age of Builders

Bryan Smith, Enterprise AI & Data Solutions Leader, Databricks

Global enterprise AI spend has crossed billions, yet most leaders still struggle to name the project that measurably changed their business.

This session is an honest look at that gap. The organizations that win won’t be the ones who move simply move fast, but the ones who build right.

Learn how Databricks equips AI builders with the right tools to help enterprises win.

Demos 11:40 a.m. – 12:30 p.m.

Thinking Architecture in Production

Shridhar Guntury, Chief Technology Officer, Varun Saraogi, Principal Data Architect, and Pranshu Agrawal, Lead AI Engineer, MathCo

Dive deep into live demos and real-world case studies of enterprise AI in production. See how Thinking Architecture plays out in practice for leading organizations: where it's been deployed, what changed, and what it took to get there.

Panel Discussion 12:30 p.m. – 1:15 p.m.

The Other Side of AI: Lessons from the Leaders

Bryan Smith, Aakarsh Kishore & Chandru R

This panel brings together leaders who've pushed AI past the pilot stage to talk about what scaling actually demands. The conversation digs into how AI is reshaping organizations like MathCo, Databricks, and what AI builders need to do right now to stay ahead of the curve.

Break 01:15 p.m. - 02:15 p.m.

Networking Lunch

Lunch is on MathCo! Take the break to connect with speakers, peers, and fellow attendees.

Breakout 02:15 p.m. - 03:00 p.m.

Build, Learn, Connect

A multi-track close to the day: Paper Presentations, 1:1 Mentorships, Demos & Exhibit exposure — running in parallel for you to choose your own path.

Call for Papers

Show Your Work, Leave A Mark

The next wave of Enterprise AI is being built by people who've shipped things, broken things, and figured out what works.

If that's you, we want to hear from you. Share your experience in form of a paper - stand a chance to get published and receive a job offer from MathCo.

Submission Guidelines

1. Duration: 10 mins

2. Format: Any format of your choice – PPT, Word, Video, etc

3. Submission deadline: July 12, 2026

4. Criteria: The submission must be based on real-life work done by you and not theoretical

Themes

1. Production Reliability

Making AI systems work dependably after deployment

WHAT MAKES A STRONG PAPER

Tell us about a specific moment in production: a failure you had to diagnose, a reliability gap you closed, or an operational call you made and had to live with. The more concrete the situation, the stronger the paper. War stories with real stakes are exactly what we're after.

2. Data Engineering for AI

The data infrastructure that determines what AI can and cannot do

WHAT MAKES A STRONG PAPER

We want the story of a specific data infrastructure challenge you hit while building or scaling an AI system: a pipeline failure, a broken data contract, a real-time design problem, a platform decision that didn't go as planned. The more specific the situation, the stronger the paper.

3. Agentic AI Design and Architecture

Designing agentic systems that hold up beyond the demo

WHAT MAKES A STRONG PAPER

Walk us through a specific architectural or design decision you made while building, deploying, or evaluating an agentic system in an enterprise setting: what you were trying to solve, what you chose, and what actually happened. Papers grounded in a real system, not a hypothetical one, will stand out.

4. Governance and Accountability

Building the structures that make AI trustworthy

WHAT MAKES A STRONG PAPER

Tell us about a governance, accountability, or data ownership challenge you navigated inside a real enterprise AI program — one where the stakes were operational, legal, or organisational. The messier and more human the situation, the more we want to hear how you worked through it.

5. Enterprise Value and Readiness

Closing the gap between AI investment and AI impact

WHAT MAKES A STRONG PAPER

Share a specific challenge you faced in measuring AI's business impact, managing its real costs, or getting an organisation genuinely ready for it, drawn from a program you were actually part of. First-hand experience with the gap between what was promised and what delivered makes for the most useful papers here.

Sayandeb Banerjee

Cofounder & CEO

Srivatsa Kanchibotla

Chief AI & Data Scientist

Ashwin Kumar

Head - Growth

Sourav Banerjee

Sourav Banerjee

Head - R&D and Platform

Kapil Raj S

Head - Talent Acquisition

Sahana Sreeja

Principal Solutions Architect

Hardik Bavishi

Principal Data Architect

Varun Saraogi

Principal Data Architect

Sumeet Pundlik

Delivery Unit Head

Pavan A

Delivery Unit Head

Dr Kiran Kumar-1

Dr Kiran Kumar

Delivery Unit Head

img_Sanjeet Mohanty

Sanjeet Mohanty

Data Science Delivery Lead

Tapas Das

Data Architect

img_Sukumaran Vaidyanathan

Sukumaran Vaidyanathan

Associate Principal

img_Amit Kumar Pal

Amit Kumar Pal

Lead AI Engineer

img_Pranshu Agrawal

Pranshu Agrawal

Lead AI Engineer

img_Jawaad Mushtaq

Jawaad Mushtaq

AI Delivery Lead

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