QAI
Lifecycle Trust in AI

Monitoring, Attribution, and Reporting for AI Systems in Production


Monitoring, Attribution, and Reporting for AI Systems in Production


The gap between AI deployment and AI accountability. That's what we close.

Evidence infrastructure

Your AI is Changing. Constantly. Silently.
At cost !


QAI detects when, how much, and why the AI system breaks.

Automatically, continuously and with evidence your auditors can use.

Your AI is Changing. Constantly. Silently.
At cost !

Drift metrics

DETECT

Identify statistically significant deviations from the established behavioral baseline. Most teams discover this through customer complaints, not monitoring.

Behavioural Drift / Factual Drift / Hallucination Risk / Semantic Drift

ALERT


DETECT

Root cause

ATTRIBUTE

Link drift to specific changes: model update, prompt edit, RAG config, or data shift. Not just an alert - visibility.

Configuration change /Model upgrade/

Seasonal Query Patterns

Correlation

ATTRIBUTE

REPORT

Generate evidence

Dashboard with drift timelines, attribution reports, lifecycle logs. Ready for engineering, compliance, and regulators. No manual preparation., download anytime.


Verifiable / Audit Ready

Dashboard


REPORT

LADDD

Lifecycle-Aware Distributional Drift Detection

A new class of monitoring built specifically for production AI systems that change over time

Step 1

Connect

No changes to your inference pipeline. No added latency. No deployment risk. Full observability from the outside in.

Analyzing current workflow..

API endpoints

Log streams

Out-of-band

Model-agnostic


Step 2

Baseline

QAi establishes a versioned behavioural baseline for every monitored system. Each baseline is explicitly linked to a lifecycle event.

  • { "event": "drift_detected", "system": "credit-scoring-v2", "drift_type": "semantic", "severity": "high", "attributed_to": "model_update", "lifecycle_event": "GPT-4o upgrade — Jan 10", "evidence_pack": "ready" }
  • { "event": "drift_detected", "system": "credit-scoring-v2", "drift_type": "semantic", "severity": "high", "attributed_to": "model_update", "lifecycle_event": "GPT-4o upgrade — Jan 10", "evidence_pack": "ready" }

Step 3

Detect

Statistical deviations from baseline are detected continuously. Drift is identified 14–21 days earlier than manual review.

Our solution

Your stack

Step 4

Report

QAi generates structured evidence packs - drift timelines, attribution reports, lifecycle logs - ready for engineering, compliance, and regulators. No manual preparation

Chatbot system

Significant increase in formal language and regulatory terminology

Workflow system

Changes in document retrieval distribution

Sales system

Average response length increased

WHY QAi

Not another dashboard. AI Infrastructure.

Built for the way production AI actually works — continuously changing, difficult to observe, and increasingly regulated.

Lifecycle Root Cause attribution

Every change is traced back to a cause - model update, prompt edit, RAG refresh, config change. Root cause in minutes, not days.

Lifecycle Root Cause attribution

Every change is traced back to a cause - model update, prompt edit, RAG refresh, config change. Root cause in minutes, not days.

Out-of-band by design

Zero impact on inference performance. QAi observes from outside your pipeline - no deployment risk, no latency added.

Continuous, not episodic

Unlike manual audits, QAi runs permanently. Drift detected in hours, not weeks. Evidence generated automatically.

Audit-ready by default

Evidence packs structured for three audiences: engineering, compliance, and regulators. One system, three outputs

Compliance
infrastructure

Lifecycle attribution

Every change is traced back to a cause - model update, prompt edit, RAG refresh, config change. Root cause in minutes, not days.

Audit-ready by default

Evidence packs structured for three audiences: engineering, compliance, and regulators. One system, three outputs

Preserve accountability, transparency, and public trust


QAI was founded because production AI systems change constantly -
through model updates, prompt edits, and RAG refreshes -
but the tools to monitor that change didn't exist. We built the evidence layer that compliance teams and regulators need, without touching your inference pipeline.

Our mission is to provide a compliance -native monitoring infrastructure
tailored for production AI.

Serving regulated enterprises across Europe.

Preserve accountability, transparency, and public trust

Book demo

Book demo


QAI was founded because production AI systems change constantly -
through model updates, prompt edits, and RAG refreshes -
but the tools to monitor that change didn't exist. We built the evidence layer that compliance teams and regulators need, without touching your inference pipeline.

Our mission is to provide a compliance -native monitoring infrastructure
tailored for production AI.

Serving regulated enterprises across Europe.




Preserve accountability, transparency, and public trust

Book demo

Book demo

At QAI, our mission is to provide a compliance-native monitoring infrastructure tailored for production AI.
Built for regulated and high-impact environments

FAQs

What types of AI systems can QAi monitor?

How long does integration take?

Does QAi require access to our data or models?

Which regulations does QAi address?

What does an evidence pack contain?