Nan Jayaram wants AI to understand the business

Before there was a dedicated headquarters, there was Starbucks. And Panera. Nandagopal (Nan) Jayaram’s company dates its escape from the coffee-shop office to 2010, four years after its founding. It is an appealing detail in a business devoted to modernizing other businesses: even the strategists once needed somewhere to put the laptop. The coffee came with the premises; permanence had to be built.

Today, the founder and CEO of CoStrategix spends much of his public working life discussing a more complicated accommodation problem. Companies have welcomed artificial intelligence into their operations. Now they must decide what it may touch, what it needs to understand, and who is responsible when enthusiasm outruns judgment. The new arrival can be articulate. It still needs an introduction to the business.

Jayaram approaches that problem with an engineer’s interest in how things work and a strategist’s interest in whether they pay their way. His career has carried him from computer science studies in India to a long-established life in the Cincinnati area. The technologies have changed repeatedly. His recurring concern is recognizably practical: what useful thing can this make possible?

A name made easier, a problem made harder

He calls Nan his “user-friendly name.” Anyone who has spent time with enterprise software may appreciate the courtesy. In a January 2024 introduction to fellow entrepreneurs, he described a quarter-century in Cincinnati, a computer science engineering degree from Bangalore University, and an MBA from Xavier University. The pairing is pertinent: one discipline asks how a system functions; the other asks why an organization should want it.

That same introduction contained a revealing ambition. Jayaram wanted help scaling CoStrategix, finding the right management team, and strengthening its marketing. There is something refreshing about a technology founder publicly asking such ordinary, consequential questions. A company can understand artificial intelligence and still need to improve the way it hires, delegates, and explains itself.

The ambition also gives his public story a human scale. He is interested in what advanced technology can do for clients, while facing the familiar work of making his own organization more capable. Knowing how to build a system does not remove the work of building a company. It merely supplies another set of tools with which to attempt it.

The inventor beneath the job title

One way to understand Jayaram is to look beneath the CEO designation, at the engineering problems associated with his name. He is a named co-inventor on a database-conversion patent granted in February 2006, originally assigned to Convergys CMG Utah. Its subject sounds modest enough: moving business information from one system into another. Anyone who has attempted that exercise knows how much mischief the word “moving” conceals.

The invention addresses differences in the way systems organize information. A name stored as one field, for instance, may need to become several fields elsewhere. The conversion engine uses mapping instructions and descriptions of the target structure, allowing different conversions without rebuilding the engine each time. Validation forms part of the process. The point is to make migration more reusable and manageable.

This is a useful piece of career continuity. Long before today’s conversations about AI agents, Jayaram was involved in the difficulty of making business information usable across systems. Meaning, structure, and trustworthy movement were already engineering concerns. A database may contain the answer and still require considerable help getting it into a form another system can use.

Curiosity gets a company address

CoStrategix began in 2006 developing custom software platforms. A dedicated engineering team in Bangalore followed in 2007. The company later broadened from product engineering into digital transformation consulting, and built a data analytics practice. Those changes describe a business extending its scope as customers’ problems changed, from individual products toward the systems and information around them.

Its name contains its working proposition: collaboration joined to strategy. For a founder who likes understanding business models, that is a useful place to stand. The commission starts before the code, with a discussion of what an organization wants to accomplish. Implementation then has to survive contact with existing processes, competing priorities, and people who have a day’s work to finish.

By 2023, CoStrategix appeared on the Inc. 5000 at No. 4,184. The listing identifies Jayaram as its leader and places the company in Blue Ash, Ohio. It is a concrete marker in a career whose public language often concerns possibilities. A business that began in 2006 had earned a place on a national growth list seventeen years later.

Keeping company with the people who do the work

Jayaram’s public appearances often bring him into conversation with people responsible for technology inside established organizations. In February 2024, he moderated a Cincinnati discussion with Ryan Kean of Total Quality Logistics, Rob Golden of Great American Insurance Group, and Brett Starr of Cincinnati Insurance Companies. The subject was the trends affecting enterprise data and analytics.

The participants’ responsibilities help explain the territory he occupies. Logistics and insurance bring concrete operational questions to a conversation that can otherwise float away on predictions. Data work has to fit the organization using it. The person speaking about a new capability and the person accountable for its consequences need a common language.

There is a local dimension to this career, too. Cincinnati is more than an address on a company listing. It is a setting for repeated professional exchanges, regional technology events, and relationships with other practitioners. Jayaram’s work reaches into enterprise technology’s larger debates while keeping a recognizable home base in southwest Ohio.

The unglamorous virtue of checking the data

His 2024 presentation with CoStrategix engineering leader Jeremy Henry was about proactive data quality management and observability. They were scheduled to explore anomaly detection in data pipelines at the University of Cincinnati Analytics Summit. The intention was to identify problems before those problems affected decisions and damaged trust.

It is a telling choice of subject. A demonstration generally has the good manners to use the data supplied for it. A working business generates fresh information continually, along with fresh opportunities for inconsistency. Jayaram and Henry’s proposed approach used machine learning to help detect quality problems as information flowed through the system.

He returned to related territory at the 2025 CDOIQ Symposium in Boston, discussing reliability patterns including anomaly detection, adaptive rules, and proactive approaches to data quality. The continuity matters. His interest in AI includes using it to improve the information on which AI and human decisions depend. There is a certain economy in asking the new machinery to help inspect the foundations.

When a fluent answer meets an actual business

By July 2026, the emphasis had become context. At the CDOIQ Symposium in Cambridge, CoStrategix featured its Enterprise Context Accelerator, with Jayaram presenting on the gap between raw data and business understanding. The offering is intended to bring terminology, procedures, policies, and organizational knowledge into a governed layer that AI systems can query.

“Access to data is not the same as understanding the business.”Nandagopal Jayaram, July 2026

The distinction has ordinary consequences. Finding a document does not automatically explain how a company applies it. Retrieving a number does not settle what the organization means by it. Jayaram’s argument gives institutional knowledge a central role in AI deployment. The information people carry about definitions and operating rules has to become available to the system doing the work.

His presentation on active metadata develops that concern further: autonomous agents need context that keeps pace with operations. The framework includes semantics, entity resolution, knowledge graphs, and cognitive maps and playbooks. These are technical terms for connected tasks: establishing meaning, recognizing entities across records, expressing relationships, and supplying working knowledge about how to proceed.

Six things between a demo and a working colleague

In another keynote, Jayaram sets out a six-factor approach to deploying trusted AI agents. The factors begin with scope and people, then move through data, design, reliability, and AgentOps. That order gives the organization a place in the conversation before the architecture takes over. Someone must choose useful work and understand how automation will affect the people doing it.

Reliability and ongoing operations make the framework particularly revealing of its author’s priorities. Getting an agent to perform a task once leaves plenty of work for the people who must depend on it tomorrow. His approach treats autonomy as something to earn progressively, with operating arrangements that continue after the presentation screen goes dark.

Alongside chief growth officer Mark Johnson, Jayaram has also discussed how to measure AI initiatives against revenue, costs, and customer acquisition. It is the MBA returning to the conversation with a pencil. A technical accomplishment needs an explanation that a finance leader can assess. The useful question is what changed for the business, and whether the investment justified that change.

There is still a canyon to walk into

Outside the formal vocabulary, Jayaram’s company biography describes a reader with a continuing appetite for learning. It also records an early engineering achievement involving an airplane black box, and a hike to the floor of the Grand Canyon. These details give the executive portrait some welcome texture: books, physical exploration, and an interest in machines that predates the current AI conversation.

He speaks about curiosity as something shared across a team. CoStrategix calls its employees Gixers, giving an unusual name to the people whose skills make the strategy executable. The founder’s task, as he describes it, includes turning their questions and expertise into possibilities for clients and the wider community. Curiosity requires company if it is going to become useful at scale.

The coffee-shop beginning and the present interest in enterprise AI belong comfortably in the same story. Both concern making an ambitious undertaking workable. Jayaram has spent years building a business around that task, with patents, conversations, and successive technical problems along the way. The questions have grown more elaborate. There is still something pleasingly direct about asking what the machinery will actually do.