About Kaisek
Kaisek exists because the infrastructure layer for AI systems is missing.
Most AI systems today are built around the model. The application calls the provider, the model responds, the output returns. There is no layer in between that owns execution, persists constraints, or governs how the workflow runs. The system works until it doesn't — and when it breaks, it breaks in predictable ways.
This is not a model problem. The models are capable. The problem is the absence of an execution boundary — a runtime layer that controls how LLM operations are admitted, executed, and verified.
Traditional software has this. Databases have transaction boundaries. Operating systems have process isolation. Networks have routing and enforcement layers. AI systems have none of it. Every constraint lives in a prompt. Every workflow is stateless. Every failure requires a human to intervene.
Kaisek builds the infrastructure layer that closes this gap. Not applications. Not tools. The layer underneath — the one that makes AI systems run reliably at scale.
Context Layer
Context Layer is our first product. It is a runtime execution layer that sits between applications and LLM providers. It enforces execution rules, persists constraints across workflow steps, controls provider invocation, and produces auditable execution evidence.
The benchmark result that motivated its existence: GPT-4o mini succeeds on a strict multi-step agentic workflow ~7% of the time without Context Layer. With Context Layer, the same model succeeds 70% of the time. Same model. Same task. Same verifier. The only difference is the execution infrastructure around it.
What we believe
AI systems fail because of the infrastructure around the model, not the model itself.
The right execution layer belongs between the application and the provider.
Infrastructure should be invisible until you need it, then unambiguous.
We build for the long term. The systems problems in AI are not going away.