The missing layer between human intent and model response.

AI mirrors the one whose hand holds it.

Enterprises deploying large language models today are not deploying a consistent system. They are deploying a population of uncoordinated, session-level interactions — one per employee, per task, per day. Each interaction varies with how input is constructed. The resulting variability in output is structural, not incidental, and it accumulates into measurable operational cost.

Most mirrors need a filter. In most enterprise LLM deployments, no such filter exists.

Input control, enforced before the model runs.

The Gravity Word Translator (GWT) is a deterministic AI input control engine. It intercepts natural language input, applies a mode-specific constraint transformation through an eight-step ordered pipeline, and returns a structurally governed input before inference begins.

This is not prompt engineering, a wrapper, or a UX feature. It is a runtime layer and protocol for input control — the primary layer that enforces deterministic structural constraints at the input boundary, prior to inference.

Triadic Constraint Theorem.

Three structural properties required for stable input-driven systems. Removing any one results in measurable degradation in output consistency.

Model‑agnostic, deterministic, dependency‑locked.

GWT operates entirely at the input boundary. It does not access model weights, embeddings, fine-tuning data, or internal representations. It works across current and future LLMs — GPT-4o, Claude, Gemini, Llama, Mistral, Grok, local enterprise fine-tunes — and across multiple model environments deployed by enterprise partners.

Identical inputs always produce identical structured outputs. That determinism is the foundation of the patent architecture and the source of the measurable performance guarantees.

Built. Running. Filed.

A fully functioning implementation under attorney work product. Not a concept.