Every company has data.
Few have shared understanding.
Most organizations have built data infrastructure optimized for storage and reporting. But AI needs something different: data that is fully contextualized, trustworthy and ready to use. Kanlet is the system that closes that gap.
Most AI efforts in companies focus on the obvious fixes: better prompts, larger context windows, more metadata, and more powerful models. These advances matter, but they miss the fundamental problem.
The best analysts, operators, and domain experts are not valuable simply because they are intelligent. They are valuable because they understand context. They know how the business works, what matters, which metrics can be trusted, where exceptions exist, and why decisions are made. Their intelligence is grounded in a deep understanding of the environment around them.
AI has never been given the foundation of institutional knowledge, shared understanding and contextual memory.
As a result, companies continue to struggle to turn AI experiments into business outcomes. Models are increasingly capable, yet most AI initiatives fail to reach production or deliver measurable value. The challenge is no longer model intelligence. The challenge is organizational context.
Intelligence is never abstract. It is always applied in a specific situation, for a specific purpose, within a specific environment.
That's why we're building the Semantic Context layer for AI.
The Semantic Context creates a shared layer of context across an organization's data, systems, knowledge, and business logic. It captures how the company thinks, operates, and makes decisions, then makes that understanding available wherever data is used by AI.
This semantic context layer covers three kind of context:
- Knowledge
entities, metrics, relationships - the ontology of how the company talks to itself. - Expertise
playbooks, workflows, tasks - the practical skills of running the business. - Norms
policies, permissions, scoping, approval paths - the guardrails that keep agents safe
The right knowledge. The right know-how. The right guardrails.
Our Founder
Satish Patil is a serial entrepreneur, AI/ML and knowledge graph expert, and Founder & CEO of Kanlet. With 15+ years of experience across machine learning, knowledge graphs, semantic engineering, and enterprise information architecture, he specializes in turning fragmented enterprise information into structured, connected knowledge. Satish holds a Ph.D. from the University of Minnesota, USA.
As an entrepreneur and technology advisor, Satish has worked with leading enterprises including Dassault Systèmes, Deloitte, Persistent Systems, and GEP, helping them solve complex challenges in data and AI. He previously founded Crysagi Systems, an AI and data science company that was acquired by CoreView Systems.
His expertise in knowledge graphs, semantic engineering, and enterprise data shapes Kanlet's vision to give AI systems the context they need to understand enterprise data, meaning, relationships, and organizational knowledge.
BACKED BY
Early access. Built with customers
Kanlet is in early access. We are building the semantic context layer for AI by working directly with a small group of mid-market and enterprise organizations.
If you are building AI agents, or deploying AI across your organization and believe context is the missing piece, we'd love to collaborate.
Let's build the future of AI together.
Building the foundation for
reliable and useful AI.
See how Kanlet gives your AI agents the meaning they need to deliver answers you can trust.