Kaya is the Governed AI Execution Platform for business-critical processes. It runs an enterprise’s most business-critical work on AI, governed by design, and proves exactly what it did. The result is operational advantage through governed AI execution.
Kaya separates reasoning from execution. AI designs and improves the plan for a process; business leaders review and approve it. The approved plan becomes a Deterministic Execution Plan (DEP): versioned, fingerprinted, and validated before production. Matching transactions then execute against it the same way every time. When something genuinely changes, whether a regulation, a policy, a new case, or a better approach, AI re-engages for that change alone, and the revised DEP re-passes validation and human approval before returning to production. Reason the plan up front, execute it the same way every time, re-reason only on genuine change.
AI agent platforms and orchestration tools coordinate individual AI actions: they decide what to do step by step and reason again on each run, so behavior can vary from one run to the next and there is rarely a complete record of why each action was taken. Kaya works differently. Rather than gating one action at a time, Kaya governs a versioned, replayable execution plan for the whole process. The distinction matters in four ways. First, execution is deterministic: AI designs and improves the plan, and once approved it becomes a Deterministic Execution Plan (DEP) that is versioned, fingerprinted, and validated before production, so the same governed input produces the same governed output every time. Kaya does not make AI deterministic; it makes enterprise AI execution deterministic. Second, reasoning is applied intentionally rather than on every transaction, so behavior and AI operating costs stay predictable as usage scales instead of being re-derived, and re-priced, on each run. Third, every execution produces an immutable operational record, a complete chain of custody covering the plan used, the information the AI saw, the policies applied, the approvals obtained, and the outcome, so you can show a regulator, an auditor, or a customer exactly what happened and why. Fourth, every run is not only a proof but a blueprint: execution knowledge compounds into an enterprise asset, so the process keeps improving over time rather than staying a fixed script. In short, orchestration tools help AI decide and act; Kaya governs how approved work executes, proves what it did, and improves how it is done, which is what makes AI dependable enough to run business-critical processes.
A Kaya Intelligence Flow (iFlow) is a governed AI workflow built and run on the Kaya platform. Intelligence moves across enterprise systems as iFlows: repeatable, governed execution rather than one-off automation, with governance traveling with the intelligence.
Yes. Kaya is SOC 2 Type II attested, ISO 27001 certified, and HIPAA compliant. Because security and compliance controls are enforced by execution, compliance is maintained continuously, not verified periodically.
Kaya makes the cost of running AI forecastable and controllable, even as usage scales. Because approved plans execute deterministically, AI isn’t asked to re-decide how work is done on every transaction. Thousands of transactions run identical governed logic, which eliminates unnecessary reasoning and keeps AI operating costs predictable.
Kaya is for enterprises that want to put AI on their most consequential work: the processes where the result has to be right every time and you have to be able to prove exactly what was done and why. Every company has business-critical processes. What defines Kaya’s fit is the stakes and the accountability, high-volume work where an outcome that varies run to run, or that no one can defend afterward, is not good enough, and where that bar is exactly what has kept AI stuck in pilots. The need is most concentrated in regulated industries such as financial services, insurance, banking, healthcare and life sciences, energy and utilities, and communications, media and technology, but it is not limited to them: any organization with work it cannot afford to get wrong, and must be able to stand behind, gains dependable outcomes, predictable costs, a defensible record, and continuously improving processes. Kaya serves the leaders accountable for that work, from the CIO and COO to the CFO and heads of risk and compliance.
Kaya Intelligence, Inc. is an enterprise AI company headquartered in Jersey City, New Jersey. Kris Canekeratne is Founder, CEO, and Chairman of the Board, Ashish Gupta is President, and Sundar Narayanan is COO. The full leadership team is listed on the Kaya company page.
Kaya Intelligence, Inc. was founded by Kris Canekeratne, who serves as Founder, CEO, and Chairman of the Board. He co-founded Virtusa in 1996 and led it as CEO and Chairman until 2021, when it was acquired by Baring Private Equity Asia. Earlier he founded edocs, which was acquired by Oracle.
No. Kaya (kayaintelligence.com) is the Governed AI Execution Platform for business-critical processes. Kaya Intelligence, Inc. is an enterprise AI company, and it is not affiliated with any similarly named supply-chain, wellness, or consumer company.
A Deterministic Execution Plan (DEP) is the approved, versioned plan a process executes against on the Kaya platform. AI designs and improves the plan; once it is reviewed and approved, the DEP is versioned, fingerprinted, and validated before production, and every matching transaction then runs the same governed logic the same way every time. Reasoning re-engages only when a genuine change requires a revised DEP, which re-passes validation and human approval before returning to production.
Traditional automation and RPA follow hard-coded rules: they repeat fixed steps and break when inputs vary or conditions change. Kaya uses AI to design and improve the plan for a process, then executes that approved plan deterministically, so it handles the variation a rules engine cannot anticipate while still producing the same correct outcome on every run. Every execution is captured as an immutable operational record, and every run becomes a blueprint to keep improving how the work is done, so the process compounds over time rather than staying static.
Kaya governs and runs business-critical work across the enterprise systems a process already touches. It sits between an enterprise’s AI and its systems of record and executes the whole workflow across those systems, so intelligence moves across systems as Kaya Intelligence Flows (iFlows) and the entire cross-system decision is captured as a single chain of custody, rather than breaking at the boundary between one system and the next.
The Kaya platform deploys in your own cloud environment (your VPC) or as a Kaya-hosted deployment that is isolated for each customer; no shared multi-tenant environment. In both modes, data is processed within the deployment boundary, and access permissions and policies are enforced at execution time on every transaction. What crosses that boundary is determined by the integrations your use case is designed and approved to make; it can be nothing at all. On model traffic specifically: in Kaya-hosted deployments, model calls are served through AWS Bedrock within the deployment, so data does not leave the boundary to reach a model provider. In your own environment, model serving can also run entirely inside your VPC. Data residency follows the residency options of your cloud provider, not limited by Kaya. Kaya is SOC 2 Type II, ISO 27001 certified, and HIPAA compliant.
No. Kaya is not an AI model. Kaya is a platform that leverages AI models and is agnostic to which models you use. What executes in production is a Deterministic Execution Plan (DEP): versioned, fingerprinted, validated, and human approved. The models a Kaya Intelligence Flow (iFlow) leverages may fall within scope of your MRM policy, and Kaya supports that governance rather than complicating it: validation before deployment, continuous monitoring, documentation, and change control are built into how the platform operates.
A named business leader reviews and approves every plan before it runs in production. Every run records who approved the plan, which policies were applied, and any human intervention that occurred. Accountability is explicit and recorded, never diffused into “the AI decided.”
Every run produces an immutable operational record created during execution: the plan used, inputs, policies applied, approvals, actions, outputs, and human intervention. It is replayable, audit-grade evidence per transaction.
A model update never reaches production on its own. Each iFlow and its DEP are pinned to specific models, so a provider releasing, updating, or deprecating a model changes nothing in production until your team deliberately adopts the change. Kaya provides the governed path for that adoption: re-validation in Test Studio, review, and human approval, applied under the change process you define, before the updated plan returns to production.
