Beyond mimicry. Beyond prompting. The LexicalMachines Lexical Specification Framework introduces a declarative, model-agnostic governance architecture for institutional behavioral specification. Stability is governed, not instructed.
Model-agnostic and provider-portable. A behavioral specification defined against GPT-4o transfers directly to Claude, Gemini, or any successor model — because the target is dimensional geometry, not prompt syntax.
Tools versus Targets. Most enterprise AI programs focus on the implementation stack — mechanics without architecture is directionless.
The measurement constructs underlying the primary governance axes. These quantify how behavioral targets are captured — distinct from the governance axes configured in the Console.
Each lever represents an independently measurable construct. The primary governance axes (Authority, Directness, Risk Sensitivity, Certainty, etc.) are configured in the Behavioral Console. These levers are the structural capture layer beneath them.
View full dimension taxonomy →Note on Scope — Dimensions shown here represent the primary behavioral governance surface. The full dimensional scope is calibrated to each engagement. The complete behavioral dimension taxonomy covers additional measurable dimensions across epistemic and institutional tiers.
Full Taxonomy →Behavioral dimensions are not independent variables. Modifying Authority calibration propagates downstream pressure through Certainty expression, Closure Force, and Risk Framing. Raising Risk Sensitivity inversely modulates Certainty. Adjusting Social Distance affects how Authority lands in context.
LexicalMachines maps these interdependencies as part of every governance specification — because isolated tuning without system-level modeling produces unpredictable drift. This is what distinguishes a behavioral governance specification from a tone guideline, and why the work requires a principal-level architect rather than prompt iteration.
Four levels of behavioral control. Only one defines institutional architecture.
Advisory context: Levels 1–3 are implementation mechanisms your engineering team selects. Level 4 — the behavioral governance specification — is what LexicalMachines defines. We operate upstream of the stack, not within it.
Maturity Model →Six layers mapped to stability, governance, and enterprise control.
Architectural specification (Level 4) is a prerequisite for any implementation layer to hold under scale. Without dimensional targets, each layer below is calibrating against nothing.
API Parameter Reference →Minimum configuration for behavioral stability, by organization type. See full maturity model →
| Organization Type | Recommended Implementation Stack |
|---|---|
| Mid-Market Enterprise | Prompt Engineering + RAG + Re-Ranking |
| Enterprise Retail / Aviation | Structured Prompts + RAG + Scoring + LoRA |
| Regulated Industry | Full Governance Layer + Alignment + Dimensional Specification |
| Global Multi-Market Brand | Multi-Model Routing + Drift Resistance Architecture |
Prompting describes behavior. Constraint modeling specifies it. The LexicalMachines simulation methodology predicts drift, measures entropy, and models system behavior under interaction pressure — before deployment risk manifests.
Three companion references forming the technical substrate of the behavioral architecture methodology.
Each document is independently useful as a technical reference and cross-linked to contextualise how the behavioral dimension taxonomy, governance maturity model, and API parameter database relate to the framework.
The LexicalMachines Lexical Specification Framework and the associated dimensional dependency logic are the intellectual property of LexicalMachines. Unauthorized replication, reverse-engineering, or redistribution is strictly prohibited.
Every engagement begins with an empirical baseline across the dimensional framework. You see exactly where your AI drifts before committing to any advisory scope.