RAPTOR defines intent. AERIE governs execution. PromptSmith builds the systems that make it real.
The standard AI stack treats execution as a consequence of generation. Intent goes in. The model decides what to do. There are no enforced boundaries, no scoped permissions, no control between intent and action. This is the gap.
The PromptSmith model inverts this relationship. Intent is formalised before it reaches the model. RAPTOR converts natural language into a structured, signed task object — an Intent Envelope — that defines what the task is and, critically, what it is not permitted to do.
AERIE evaluates that envelope against policy before issuing any capability grants. Execution happens inside a constrained, time-bound scope. Every action is attributable. The audit trail is not an afterthought — it is the foundation the architecture is built on.
A methodology for translating raw user intent into structured, repeatable, machine-legible task specifications. RAPTOR enforces a consistent schema — Role, Aim, Parameters, Tone, Output, Review — across every task, making prompt engineering an engineering discipline rather than an art form. Portable across models. Teachable to teams.
The control plane between intent and execution. AERIE evaluates signed Intent Envelopes against declared policy, issues minimum-privilege capability grants, enforces execution boundaries mid-flight, and writes to an immutable, append-only audit ledger. Governance is structural, not observational. Control by design — not by monitoring.
A local-first, governed AI environment that integrates RAPTOR and AERIE into a complete agent runtime. The AI OS is the end state: a secure execution surface where agents operate with defined identity, scoped capability, and full auditability — deployed on your infrastructure, under your control, with no dependency on external API governance.
If you're building AI systems that need to operate where control, auditability, and execution boundaries are non-negotiable — this is the conversation to start.