PASADHI

The next business to benefit from AI may already own its first piece of infrastructure: the computers sitting in its office. PASADHI is an open-source, shared-compute, and hybrid-orchestration prototype designed for organizations unable to invest heavily in dedicated GPU fleets upfront. Our initial workflow transforms noisy Thai and English office notes into structured tasks, owners, and deadlines—while strictly preserving ambiguity for undecided details. For small and medium enterprises (SMEs), our pilot goal is to significantly reduce missed handoffs and make AI accessible in the languages teams actually use daily. The working application bridges a Meta text backend with a policy-controlled gateway. To ensure data sovereignty, users must explicitly permit cloud processing per request. The interface provides full observability—displaying the active backend, model, and elapsed time—while transparently surfacing inference failures. Furthermore, a local llama.cpp backend is integrated into our codebase, laying the technical groundwork for distributed, physical multi-machine inference in our next phase. We have successfully demonstrated live Meta Muse Spark Open API responses and independently benchmarked 50 to 100 Docker compute-fleet containers on a single host. Our rigorous model evaluations intentionally retain failures, distinguishing mere nonempty outputs from true task quality. By doing so, phenomena like overload rejections, output-budget exhaustion, and excerpt-scoring limits are transformed into actionable engineering findings. We document these methods and limitations transparently in our public white papers, favoring empirical data over simulated production claims. Looking ahead, our proposed business model focuses on managed deployments and enterprise support for SMEs, ensuring organizations retain absolute control over their hardware and data policies. Our immediate roadmap includes piloting the core workflow, measuring human-verified task accuracy, and validating our physical shared-compute fleet. Ultimately, PASADHI’s ambition is to democratize AI, making it highly useful and immediately reachable through the resources businesses already own.