CogniTune Technical Docs

Overview

CogniTune provides an enterprise-grade pipeline for adapting compact language models to compliance and security workloads. The platform is designed for private-cloud deployment with strict data residency requirements.

Architecture

The architecture keeps model adaptation and inference execution inside your cloud, enabling secure handling of sensitive evidence and policy data.

Text-Based Architecture Diagram

1Data Sources

2Fine-Tuning Pipeline (PEFT/LoRA)

3Model Registry

4Triton Inference Server

5CogniAudit API

Data Sources → Fine-Tuning Pipeline (PEFT/LoRA) → Model Registry → Triton Inference Server → CogniAudit API

Model Tier	GPU Requirement	Fine-Tuning Mode	Recommended Usage
CogniTune-7B	NVIDIA A10G 24GB	QLoRA 4-bit	Real-time compliance classification and API responses
CogniTune-35B	NVIDIA A100 80GB x2	LoRA 16-bit	Deep reasoning, batch analysis, multi-framework synthesis

Sample Inference API Request (Python)

import requests

payload = {
    "compliance_text": "Audit log indicates privileged access without MFA enforcement.",
    "framework": "SOC2"
}

response = requests.post(
    "https://api.cogniwiss.com/v1/cognitune/classify",
    json=payload,
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    timeout=10
)

print(response.json())

Fine-Tuning Guide

Prepare compliance corpora with framework labels and citation metadata.
Run LoRA/QLoRA jobs with PEFT under controlled GPU quotas.
Validate benchmark sets for ISO 27001, SOC 2, HIPAA, and PCI DSS.

Inference API

CogniTune exposes REST endpoints via Triton-backed service pods with per-tenant throttling, signed request validation, and response trace IDs for auditability.

Model Cards

Model cards include framework coverage, latency profiles, known edge cases, and evaluation deltas against general-purpose baselines.

NVIDIA Integration