Built from the
real world up.

Anoop is an industrial engineer and repeat founder who has spent much of the last decade working at the intersection of manufacturing, industrial data and software.

Alongside building technology companies, he has been closely involved in a battery manufacturing business, giving him direct exposure to how factories actually operate — from production constraints and process variability to the everyday reality of disconnected systems, spreadsheets and human coordination.

He also built two startups in the industrial IoT and data space, working on the problem of connecting machines, capturing operational data and making that data useful to manufacturing teams.

Across those experiences, one pattern kept repeating: the business context connecting the data, systems and SOPs was missing.

What first looked like an industrial data problem eventually became an AI problem.

AI models are increasingly capable of reasoning, but they still do not inherently understand how a specific enterprise works — what its objects mean, how its processes connect, which rules matter, and what changes when something goes wrong.

PatternLab grew out of that insight.

Anoop leads the company’s product vision, customer discovery and strategy, with a focus on building enterprise AI from the operational reality of businesses rather than from the technology layer down.

Karthik studied Industrial Engineering at Penn State, a university with deep roots in the discipline.

He later worked as a process manager at a multinational company, where he experienced firsthand how large organizations actually operate — through systems, workflows, handoffs and constant coordination between teams.

That experience exposed the gap between how processes are designed and how work happens in reality.

At PatternLab, Karthik leads operations and execution, translating complex customer environments into repeatable deployments and scalable operating systems. His focus is making sure PatternLab is not only powerful technology, but something enterprises can actually adopt and depend on.

Jaisurya studied Computer Science at Arizona State University and has always had a deep interest in computers, software and building things from first principles.

At PatternLab, he leads the architecture and engineering of the platform.

His work focuses on turning complex enterprise environments into systems machines can understand — connecting business objects, relationships, rules, processes and live data into structured context.

How do you make enterprise complexity computable?

That question sits at the center of the technology Jaisurya and the engineering team are building.

Nithin holds a Master’s degree in Computer Science from Arizona State University.

Before PatternLab, he worked on SAP integrations, giving him direct exposure to one of the hardest parts of enterprise technology: getting fragmented systems, schemas and data to actually work together.

He saw firsthand how much time and effort goes into integrations, mappings and maintaining context between systems — often before any intelligence can even be applied.

At PatternLab, Nithin works on the AI systems designed to automate that last-mile work. His focus spans context compilation, agentic mapping, reasoning and grounding AI in the way a business actually operates.

The goal is to make enterprise AI useful without requiring teams to manually reconstruct their business context every time they build a new application or agent.