In 2014, Raj Koneru had a problem most founders would envy: he had already won. He had taken two companies public on NASDAQ and sold a third to a European banking-software giant. He could have stopped. Instead he started a company built on a premise that, at the time, sounded slightly odd - that the way people deal with big companies was about to change from clicking and typing to simply asking. He called it Kore.ai, and for the first few years he had to explain what “conversational AI” even meant, because the term barely existed.
Twelve years on, the explaining is over. Kore.ai now sits underneath the customer-service lines and internal help desks of more than 450 of the world’s largest companies - Pfizer, Morgan Stanley, Eli Lilly, Deutsche Bank, Coca-Cola, Johnson & Johnson. Most people have talked to one of its systems without ever hearing the name. That is roughly how Kore.ai likes it. It is an enterprise software company, and enterprise software companies get paid to be invisible and reliable, not famous.
What it actually doesSoftware that does the job, not just the small talk
The easiest way to misunderstand Kore.ai is to picture a chatbot. That was the 2016 version. The current pitch is narrower and more ambitious: it builds a platform for creating, deploying and - the word it leans on hardest - governing AI agents. An agent, in this telling, is not a box that answers a question. It is a piece of software that takes an action: books the appointment, files the claim, pulls the account, routes the ticket, updates the record. The chat is the easy part. Doing something without breaking something is the hard part.
Kore.ai splits its work into three jobs. AI for Service handles the outside world - the contact center, voice and chat, agent assist, quality checks, compliance. AI for Work faces inward, letting employees search across a hundred-plus connected systems and hand routine tasks to agents. AI for Process automates the machinery in between, at whatever level of autonomy a nervous compliance officer will allow.
The chat is the easy part. Doing something without breaking something is the hard part.
Who’s payingThe client list reads like a stock index
Kore.ai’s customers are not startups experimenting with a free tier. They are regulated, cautious, enormous organizations where a wrong answer can mean a fine or a headline. That shapes everything about the product. When Pfizer says it runs 60 agents, or Eli Lilly says roughly 70 percent of its internal tech requests now resolve without a human, or Morgan Stanley says its advisors save a quarter-hour a day, those are not demo-day numbers. They are the metrics a CIO uses to justify a renewal.
Reported production results from named customers
The problem it solvesEveryone can build a demo. Almost nobody ships
Here is the open secret of the enterprise AI boom: the demo always works, and the pilot usually dies. A model that dazzles in a sandbox has a habit of doing something unpredictable the moment it touches real customers, real money and real regulators. The gap between “impressive” and “allowed in production” is where most corporate AI projects quietly expire.
Kore.ai has aimed its whole company at that gap. Its argument to a bank or a drugmaker is not “our AI is smarter.” It is “our AI can be watched, controlled, audited and switched off.” In an industry drunk on capability, Kore.ai sells sobriety - observability, role-based access, guardrails enforced before an agent ever speaks to a customer. It is not the flashiest sales pitch. It is the one that survives a legal review.
How it’s differentThey wrote a language so agents behave like code
In May 2026 Kore.ai rebuilt its platform and named the new edition Artemis. The headline feature is unusual for an AI company: a programming language. The Agent Blueprint Language, or ABL, is a compiled, declarative way to define exactly what an agent is, what it can touch and how it must behave. The idea is to make an AI agent as reviewable as any other piece of enterprise software - version it, validate it, govern it - instead of hoping a well-worded prompt holds.
Artemis pairs that with what the company calls a Dual-Brain architecture. One engine reasons and improvises; the other follows deterministic, pre-approved flows. They share memory and run under a single runtime, so an agent can be flexible where flexibility is safe and rigid where a mistake is expensive. A third piece, an agent architect nicknamed Arch, translates a plain business goal into working ABL and keeps refining agents using real production traces. The pitch to buyers: production-ready multi-agent systems in days, with the governance switched on before launch rather than bolted on after an incident.
In an industry drunk on capability, Kore.ai sells sobriety.
Where it sitsA crowded market, and a deliberately unfashionable lane
The agent-platform field is loud and getting louder. Kore.ai competes with focused players like Cognigy and Yellow.ai, with LivePerson and SoundHound’s Amelia in customer engagement, and increasingly with the platform giants - Microsoft’s Copilot Studio, Google’s Vertex AI Agent Builder, Salesforce’s Agentforce. Against the nimble startups, Kore.ai leans on depth and governance rather than speed of setup. Against the giants, it leans on being AI-native and neutral - not tied to a single cloud or CRM.
The analysts have mostly rewarded the approach. Kore.ai has been named a Leader in Gartner’s Magic Quadrant for conversational AI platforms across multiple years, appears in Forrester’s Wave reports for customer service and cognitive search, and was placed as a Leader in Everest Group’s 2026 assessment of agentic AI products. It counts more than a dozen such rankings in total.
The moneyNVIDIA writes a check
In January 2024, Kore.ai raised a $150 million Series D led by FTV Capital, with NVIDIA joining as a strategic investor alongside existing backers. The round put the company at roughly a billion dollars and brought its total raised to around $223 million. NVIDIA does not sprinkle equity around casually; its presence is a read on where the value in enterprise AI is migrating - away from the raw model and toward the platform that makes agents safe to run at Coca-Cola scale.
Series D — $150M · January 2024
- FTV Capital — lead investor
- NVIDIA — strategic investor
- Vistara, Sweetwater, NextEquity, Nicola, Beedie
- ~$223M raised in total to date
The founderA fifth act, on purpose
Raj Koneru is not a first-timer with a lucky idea. Before Kore.ai he founded Intelligroup and SeraNova, both of which went public on NASDAQ, and Kony, a mobile-app platform later bought by the banking-software firm Temenos. He earned his master’s at BITS Pilani in 1991 and has spent three decades building and selling technology companies across every platform shift since. Kore.ai is, by that count, his fifth act - and rather than milk a decade-old chatbot business, he tore the platform down and rebuilt it for agents. One trait shows up across interviews: a refusal to rename the company every time the industry coins a new buzzword. Conversational, generative, agentic - he treats them as layers, not pivots.
The road hereFrom Orlando to the Bay Area
The bottom lineBetting on the boring part
The interesting thing about Kore.ai is not that it does AI. Nearly everyone claims that now. It is that the company decided early the hard problem was not making agents clever but making them trustworthy enough to hand a real job - and then built a language, an architecture and a business around that decision. Whether “governed and auditable” beats “fast and cheap” over the next few years is the open question. Kore.ai has spent twelve years and a NVIDIA-sized check wagering that, inside a Fortune 500, it will.