AWS provides the infrastructure and the AI services. Kaya provides the execution platform that turns them into production-grade business processes: designed with AI, approved by your people, and run the same way every time, inside your own AWS environment.
Most enterprises now have the infrastructure for AI and a set of pilots that work. What they do not yet have is AI running the processes the business depends on, at scale, in production. The barrier is repeatability: a system that reasons anew on every transaction produces outcomes that cannot be predicted, costs that cannot be forecast, and decisions that cannot be defended to a regulator or an auditor.
Kaya and AWS close that gap together. AWS supplies the secure, scalable foundation; Kaya supplies the execution platform that makes AI dependable enough to carry business-critical work.
AI reasons over your process and designs the plan for how the work should run; your team reviews and approves it.
Approved plans run the same way on every transaction, across the systems and people the process actually touches, with model calls placed inside steps only where the work genuinely requires them.
Synthetic scenarios test the process before deployment; monitoring detects drift after it, and replanning happens under approval, not by accident.
Every execution carries its full chain of custody: what information was used, what rules applied, who approved what, and why. The audit answer exists before the question is asked.
For execution at enterprise volume.
For model access where Kaya uses it: at design time, where frontier models earn their cost, and inside individual process steps that call for probabilistic work.
Underpinning access control and isolation.
Kaya deploys into your own AWS account, so your data and your execution stay inside the perimeter you already govern.
Kaya complements AWS rather than replacing any part of it. Every process Kaya runs deepens the value of the AWS foundation beneath it.
A large US health insurer runs its provider data management on the Kaya platform, in production, with roughly 90% of provider-roster changes processed autonomously.
A global financial-services firm is running trade fail resolutions, powered by the Kaya platform, with early results showing a 70% reduction in costs.
These are the environments where production AI is hardest to earn: regulated industries, high transaction volumes, and outcomes that must stand up to audit. The same platform, on the same AWS foundation, is available to every industry.
Prior authorization, utilization review, claims operations, and provider data management: high-volume decisions that must be consistent, explainable, and defensible under regulatory scrutiny.
Trade-fail resolution, settlement and reconciliation, payments exceptions, client onboarding and KYC, and credit and underwriting workflows: the same inputs must produce the same decision every time.
Wholesale interconnect settlement, order fallout and service-delivery exceptions, and enterprise bid and site-data normalization across fragmented operational systems.
The mechanism is industry-independent. If a process runs thousands of times a month and needs the outcome correct, auditable, and affordable every time, it is a candidate for Kaya on AWS.
Request a discovery workshop through your AWS account team, or contact Kaya directly. In one session we will identify one or two processes currently stalled between pilot and production and show you what they look like running on Kaya, on your own AWS infrastructure.
A large US health insurer runs its provider data management on the Kaya platform, in production, with roughly 90% of provider-roster changes processed autonomously.
of provider-roster changes processed autonomously, in production on live data.The customer’s own figure.
A global financial-services firm runs trade fail resolutions powered by the Kaya platform, early results showing a 70% reduction in costs.
reduction in costs in early results.Client-stated, measured on live data.
