CogniTuring Assure · QE for AI

AI Quality, Safety, and Compliance. Automated.

CogniTuring Assure is the QA harness built for AI systems. Evaluate your AI endpoints before launch, gate deployments in CI, and run recurring guardrails in production.

Note: AI may misinterpret data; human validation ensures accuracy.

01Overview

QA rigor, built for AI systems.

CogniTuring Assure brings the discipline of software testing to language models and agents — evaluating responses, tool-use patterns, and agentic workflows across quality, safety, fairness, and compliance, with tamper-evident audit trails mapped to international standards.

7Evaluation stages
2Evaluation pillars
8Standards mapped
1Evidence package
02Two Evaluation Pillars

One platform. Both sides of an AI system.

Assure evaluates what your AI says and what your AI does — the foundational text layer and the functional agentic layer.

PILLAR.01

Foundational QA

For chatbots, LLMs, Q&A systems, and assistants.

Evaluates text responses across
  • Response accuracy and ground-truth alignment
  • Safety, bias, and fairness dimensions
  • Multi-turn coherence and context retention
  • Hallucination detection and factual grounding
  • Robustness under adversarial and edge-case prompts
  • Red-team campaigns and prompt-injection resistance
PILLAR.02

Functional QA

For agentic AI and tool-using assistants.

Evaluates actions and text together
  • Function-call accuracy and argument fidelity
  • State-machine conformance against expected flows
  • Excessive-agency detection (OWASP LLM-08)
  • Agentic flow analysis and recovery behavior
  • Combined scoring of tool-use and text quality
03Evaluation Workflow

Seven stages, endpoint to evidence.

A repeatable pipeline you can run before launch, wire into CI, or schedule as a production guardrail. Select a stage to see what happens.

1
Connect
Connect to your AI endpoint — OpenAI, Gemini, Claude, or any Custom REST API.
2
Dataset
Build or import an evaluation dataset with ground truth.
3
Configure
Set evaluation dimensions, thresholds, and judge-panel configuration.
4
Execute
Run evaluations using a multi-judge platform architecture.
5
Analyze
AI-powered analysis of failure patterns and root causes.
6
Compliance
Standards-mapped scorecard with clause-level traceability.
7
Compare & Track
Historical comparison, trend tracking, and export.
04Standards Alignment

Mapped to the frameworks you answer to.

Evaluation results map to the following standards out of the box — clause-level, not logo-deep.

ISO/IEC 42001:2023AI Management System
ISO/IEC 23894AI Risk Management
ISO/IEC 25010 · 29119Software Quality & Testing
ISO/IEC 5259 · 25012Data Quality
NIST AI RMFAI Risk Management Framework
IEEE 7000Trustworthy AI Series
EU AI ActHigh-Risk AI Requirements
OWASP LLM Top 10AI Security Risks
05Key Capabilities

What does the heavy lifting.

C.01

Multi-Judge Evaluation Architecture

Independent AI judges score each response, reducing single-model bias. Judge independence is surfaced as a tri-state indicator for every evaluation run.

C.02

Red-Team Engine

Run structured adversarial campaigns — prompt injection, jailbreak attempts, role-play exploits. Results are classified by severity and mapped to OWASP LLM Top 10.

C.03

RAG Grounding & Hallucination Detection

Evaluate retrieval-augmented generation pipelines against source fidelity and grounding accuracy.

C.04

Evidence-Grade Compliance Reports

Each run produces a tamper-evident audit trail with clause-level traceability and supporting evidence — exportable in HTML, JSON, Markdown, and SARIF.

C.05

SARIF Export

Export findings in SARIF for integration with developer security workflows and CI toolchains.

C.06

Historical Comparison & Trend Tracking

Compare runs across model versions, prompt changes, or dataset updates. Track quality trends over time.

No pitch deck. Just a conversation.

Prove your AI is ready to ship.

Book a 30-minute walkthrough. We'll run Assure against your specific endpoint and show you the evidence package — before it costs you in production.