Many large companies have moved past the experimentation stage with artificial intelligence (AI), and their leadership teams want to adopt it at scale while keeping the advantages that made them competitive. That goal has placed AI sovereignty at the center of enterprise technology discussions, and DotKonnekt, an enterprise AI company with operations across the US, India, and the Middle East, has built its approach around it.
Chandan Mahajan, the company’s co-founder and chief growth officer, says sovereignty covers who controls a company’s technology, its business context, and its ability to execute, with data location as one piece of that.
Sovereignty Has Three Parts
In Mahajan’s view, sovereignty matters now because “AI has moved from a tool people use to a capability the business runs on.” Enterprises have always owned their operating capability, he says, and AI should follow the same rule.
As companies scale AI, dependence on a single vendor, model, cloud provider, or technology stack creates lock-in. The result, in his words, is costs that rise with every success and a roadmap set on someone else’s timeline. He highlights institutional knowledge, the know-how a company builds over years. “Every rule and exception your people teach an AI system is institutional knowledge,” he said. “If it lives inside a vendor’s product, you are outsourcing the very intelligence that should become your competitive advantage.”
DotKonnekt divides sovereignty into three dimensions, beginning with technology, meaning the systems a company runs on. Context covers its business data, workflows, processes, and institutional knowledge, while capability covers the skills that set it apart from competitors that use the same AI models.
Kompass Studio, the company’s AI product, works with multiple cloud providers and AI models, so switching models requires only a configuration change and leaves the rest in place. Its domain intelligence, meaning knowledge of a client’s industry, and its forward-deployed engineers, who work inside the client’s business, build AI around that business and then transfer it to the client.
The aim is to give enterprises the freedom to evolve their AI stack, control over their institutional knowledge, and ownership of the capabilities that differentiate them. Models may become interchangeable, but the business context built around them can become an increasingly valuable competitive asset.
Why the Old Technology Stack Is Breaking
The traditional enterprise technology model separates strategy, software, and implementation, so a consultant writes the plan while a vendor and an integrator supply and build the system. That arrangement often leaves enterprises with expensive software and disconnected proofs of concept, small trial projects that deliver limited business impact, Mahajan says. With no one owning the handover, many companies end up with AI systems they cannot run themselves.
Enterprises now need one accountable team that brings together strategy, domain expertise, software, and engineering, from choosing the right business problem to running and improving the solution in production, meaning daily use. DotKonnekt built Kompass Studio for that purpose, and its Enterprise Context Graph connects industry knowledge with a client’s systems, data, and processes. The product includes reusable components and the forward-deployed engineers who work alongside business teams.
Rather than asking enterprises to buy another disconnected tool or outsource their thinking, DotKonnekt combines the platform, domain expertise, and engineering needed to build AI around the business itself.
DotKonnekt follows a build, operate, and transfer model, with the handover written into the contract. The test, he says, is whether the client’s own team can run and extend the system once DotKonnekt steps back.
Proof That Ownership Changes the Business
Sovereignty shows up in three places: speed, cost, and enterprise value, Mahajan says, citing two clients. At Bretting Manufacturing, a 136-year-old, fifth-generation industrial manufacturer, DotKonnekt delivered in 12 weeks what typically takes 12 months from concept to production, according to Mahajan. He adds that the solution carries Bretting’s own name and belongs to the company.
For cost, he cites Pearl, the AI wedding planning product DotKonnekt built with David’s Bridal, the 76-year-old bridal retailer. Pearl went live in under four months and now serves more than 150,000 users without AI costs rising in step, he says. He credits cost controls built in from the start, including model routing, which sends each request to the best-suited model, and context engineering, which controls the information a model receives.Together, these measures were designed to help manage AI costs as the platform scales.
Enterprise value arrived on October 22, when David’s Bridal announced a proposed spin-off of Pearl as a standalone public company. The agreement is a non-binding letter of intent to combine Pearl with K&F Growth Acquisition Corp. II, a special purpose acquisition company (SPAC) that raises money to merge with a private business. The deal would value Pearl at about $350 million in pro forma enterprise value, an estimate of the whole business’s worth. The companies expect it to close in the first half of 2027 but cannot assure completion.
Mahajan says that option exists because David’s Bridal owns Pearl outright, and “you cannot spin off a capability you rent,” he said.
As AI models become increasingly interchangeable, the institutional knowledge and capabilities built around them can become a more durable source of competitive advantage. As Mahajan puts it: “Models commoditize. Context compounds. Enterprises should own what compounds.”
