Author: Neka Everett
Academic Basis: BA Biologial Anthropology, Columbia University
Thesis Reference: Langhorne (2015) - DNA and the Consumer
The NEnterprise AI Forensic Suite is a specialized diagnostic engine designed for Mechanistic Interpretability and Neural Archaeology. By utilizing a proprietary 0.0054 Basal Accountability Gradient™, this framework provides a mathematical chain of custody for neural weights, ensuring data sovereignty and bias mitigation in high-stakes institutional AI environments.
This repository contains the Python-based Observation & Orchestration Layer. The high-performance mathematical core remains proprietary (C++).
- Basis: Welford's Algorithm for Online Variance.
- Function: Establishes biological 'Homeostatic Baselines' to prevent systemic shock from volatile data inputs.
- Basis: Mathematical Chain of Custody.
- Function: Links every evolutionary step of the neural substrate into an immutable cryptographic sequence.
- Basis: Dynamical Systems Stability.
- Function: Identifies and secures 'Fixed-Point' attractors to prevent dead-end logic loops.
- Basis: Negative Feedback Loops.
- Function: Regulatory layer that dampens extreme outputs to maintain systemic equilibrium.
- Basis: Directed Acyclic Graph (DAG) Traversal.
- Function: Ensures model learning trajectory remains aligned with the NEnterprise Root Baseline.
- Basis: Distributed Neural Representation.
- Function: Analyzes weight distribution across the neural population to prevent single-point bias.
- Basis: Hebbian Learning Refinement.
- Function: Real-time rectification of logic drift using the 0.0054 gradient.
- Basis: Data Sovereignty Protocols.
- Function: Isolation and encryption of critical forensic signatures.
- Basis: Cladistics and Forensic Neural Archaeology.
- Function: Traces the 'genetic' history of model weights to identify the origin of specific knowledge sets.
- Basis: Cross-Domain Data Fusion.
- Function: Consolidates validated forensic nodes into comprehensive institutional reports.
- Basis: Predictive Forensic Modeling.
- Function: Extrapolates validated past narratives to assess future model reliability.
An Evolutionary Intelligence Substrate for Enterprise Voice AI.
This framework provides a deterministic "Forensic Layer" for Large Language Models (LLMs). It audits conversational data in real-time to ensure compliance, safety, and brand alignment for regulated industries (Finance, Healthcare, Legal).
- Real-time Compliance Auditing: Prevents hallucinations in high-stakes environments.
- Evolutionary Lead Genotyping: Scores lead probability based on historical interactions.
- Agnostic Integration: Compatible with Vapi, Retell AI, and Twilio.
This repository hosts the analysis engine.
Voice agents (deployed separately) send transcripts to the /audit-call endpoint for validation.
The NEnterprise AI Forensic Suite utilizes a dual-layer architecture to maximize transparency while protecting core trade secrets:
- Python Orchestration Layer (Public): High-level logic and reporting models (01-11) are provided for institutional audit and integration verification.
- C++ Neural DNA Core (Proprietary): The high-performance mathematical engine responsible for raw substrate extraction remains offline. This core is available only via enterprise licensing.
© 2026 NEnterprise, LLC. All Rights Reserved.
© 2026 NEnterprise, LLC. All Rights Reserved. The logical thresholds, specifically the 0.0054 Basal Accountability Gradient™, are the intellectual property of NEnterprise, LLC. This public repository serves as a portfolio of the architectural logic. Reverse engineering of the underlying C++ substrates is strictly prohibited.
Contact: LinkedIn
Portfolio: NEnterpriseAI.com
This repository exposes a FastAPI forensic engine designed to audit voice agent conversations in real-time.
POST /audit-call
Accepts a JSON payload containing the call transcript from Vapi, Retell, or Twilio.
Request Structure:
{
"call_id": "call_12345",
"transcript_text": "Agent: This call is recorded...",
"metadata": {}
}
**Response:**
```json
{
"call_id": "call_12345",
"forensic_audit": {
"compliance_status": "PASS",
"risk_flags": [],
"lead_sentiment": 0.85
}
}