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CogniTuring Assure · QE for AI

AI Quality, Safety, and Compliance. Automated.

CogniTuring Assure is the evaluation platform built for AI systems. Test what your AI says, validate what your AI does, and produce the evidence your compliance team needs, before launch, in CI, or as a recurring production guardrail.

Book a Consultation → See how it works

AI evaluation supports quality decisions; human review remains part of the sign-off process.

01Two Evaluation Pillars

One platform. Both sides of an AI system.

Every AI system has two surfaces that need testing: what it generates, and what it does. Assure evaluates both, the quality and safety of text outputs, and the accuracy and safety of actions, through two purpose-built pillars.

PILLAR.01

Foundational QA

For chatbots, assistants, Q&A systems, and any AI that generates text.

Evaluates response quality across nine dimensions
  • →Accuracy, does the response match verified ground truth?
  • →Coherence, does it stay consistent and on-topic across a conversation?
  • →Hallucination, is it grounding claims in sources, or inventing?
  • →Safety, does it comply with content and harm boundaries?
  • →Fairness, is it consistent and unbiased across user groups?
  • →Transparency, does it represent its capabilities and limitations honestly?
  • →Privacy, does it handle sensitive input appropriately?
  • →Robustness, does it hold under adversarial inputs and prompt manipulation?
  • →Instruction Following, does it do what it was asked, reliably?

Adversarial testing runs across ten injection and manipulation techniques, covering prompt injection, encoding manipulation, role-play exploitation, context distraction, and more.

PILLAR.02

Functional QA

For AI agents and tool-using assistants.

Validates both what the agent says and what it does
  • →Function-call accuracy, did it call the right tool with the right arguments?
  • →State-machine conformance, did it follow the intended flow, or deviate?
  • →Excessive-agency detection (OWASP LLM-08), five rules: scope exceeded, unrequested actions, unexpected function calls, unauthorized data access, and irreversible actions without confirmation. Each severity-scored with remediation guidance.
  • →Agentic attack resistance, does it hold under six categories of multi-step attack: tool response injection, deceptive schemas, auth boundary bypass, state poisoning, parameter manipulation, and data exfiltration attempts?
  • →Error recovery, when something fails mid-flow, does it recover correctly?
02Evaluation Workflow

Seven stages, endpoint to evidence.

A repeatable pipeline you can run before launch, wire into CI, or schedule as a production guardrail. Each stage produces structured output the next stage uses.

1
Connect
Point Assure at your AI endpoint, any provider, any architecture. No vendor lock-in, no connector required.
2
Dataset
Build or import an evaluation set, authored, imported, or AI-assisted, to establish ground truth.
3
Configure
Select the dimensions that matter, set pass/fail thresholds, and choose how many judges score each response.
4
Execute
An independent panel of AI judges scores every response, with panel agreement surfaced alongside the scores.
5
Analyze
Failures are clustered by pattern and root cause, surfacing which dimensions are weak and why.
6
Compliance
Results map to your frameworks at the clause level, with status, evidence coverage, and gaps for review.
7
Compare & Track
Every run is stored and compared, with a green/amber/red drift alert when quality moves past thresholds.
03Key Capabilities

What does the heavy lifting.

C.01

Independent Judge Panel

Multiple AI judges evaluate each response independently, drawn from different providers so no single model's bias decides the verdict. How the panel agrees, not just the score, tells you how much confidence to place in the result.

C.02

Red-Team Campaign Engine

Run structured adversarial campaigns, not random noise, but categorized attack scenarios. For text-generating systems: ten manipulation techniques across injection, encoding, role-play, and distraction. For tool-using agents: six multi-step agentic attack categories designed to test for privilege escalation, state corruption, and data leakage.

C.03

Agentic Flow Validation

Validate the full plan-act-observe loop of a tool-using AI. Every function call, argument, and state transition is checked against what the agent was designed to do. Excessive-agency violations, actions taken without being asked, or beyond scope, are classified by severity with remediation guidance.

C.04

Evidence-Grade Audit Trail

Every evaluation run produces a signed, tamper-evident evidence package, per-finding traceability, judge-level reasoning, and a compliance scorecard with clause-level citations. Exportable in formats suited to developer toolchains, audit submissions, and executive review.

C.05

Drift Detection and Release Gates

Monitor AI quality over time with tiered drift alerts. Set release gate thresholds, when a model regresses past the defined limit, the gate blocks the deployment and surfaces the exact dimensions that caused it.

Mapped to the frameworks you answer to.

Evaluation results map to these 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 25059AI Quality Model
NIST AI RMFAI Risk Management Framework
IEEE 7000Trustworthy AI Series
EU AI ActHigh-Risk AI Requirements
OWASP LLM Top 10AI Security Risks
04Explore the platform

The other half of the CogniTuring Platform.

CogniTuring Platform

CogniTuring Velocity

From User Story to Executed Test. At AI Speed.

Before you need to evaluate an AI system, Velocity helps you build and test the software around it, from requirements engineering through automated script generation, API testing, and execution analytics.

Explore Velocity →
  • →AI-guided story generation with built-in quality scoring
  • →From test design to executable automation scripts, automatically
  • →AI agent that browses your app to discover and validate UI elements before scripting
  • →API testing from specification to execution, no manual setup
See it work on your AI systems.

Prove your AI is ready to ship.

Book a 30-minute walkthrough. We'll run Assure against your specific AI endpoint and show you the evidence package, quality score, compliance map, and any red-team findings, before it costs you in production.

Book a Consultation → Explore Velocity