We are introducing Aestimor as the home for our work on AI-native applications. The focus is on researching and developing how AI becomes deployable software, with a path toward an applications platform through early access, beta, and general availability in 2026.
AI-Native Applications
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Who We Are
Aestimor is an applications platform company. We research, design, and build human-centered AI that works alongside people to support real work. The platform is meant to live inside day-to-day workflows, where teams and individual practitioners already spend their time. Our systems are designed to be scalable and human-in-the-loop, with people kept in control of decisions and outcomes. The work focuses on tools that fit into existing project environments and help organizations modernize carefully, blending legacy project systems with secure AI workloads to produce repeatable, measurable results. Aestimor operates as a privately held, practitioner-led venture. The same people who research and build the platform review each deployment and share progress through build notes. Early access is invitation-only for teams and individual practitioners with real workloads that match this direction, and applications are reviewed on a rolling basis. We focus on deployable building blocks that support production work. Operational copilots provide decision-support surfaces with auditable context. Data scaffolding connects legacy project systems, on-premises data, and modern AI services so teams can deploy securely without rip-and-replace. Workflow programs capture how high-performing teams already operate and express those methods as programmable playbooks.
End to End
We are developing how large language models are integrated into AI-native applications, defining what that means in practice for the platform. The framework centers on human-in-the-loop, augmented workflows that apply AI safely to production work. Models are deployed on privately hosted infrastructure so we can monitor and control end-to-end interactions with users. Training is targeted to begin in the fall, following the build-out of our hardware stack and training applications.