Overview
AI models are not static. The data they were trained on drifts away from the data they encounter in production. User behaviour changes. Business conditions shift. Without monitoring, you discover model degradation when users complain — or worse, when a regulator or auditor finds it for you. Our Model Monitoring & Drift Detection service provides the continuous performance surveillance your AI systems need. We monitor for data drift, concept drift, prediction drift, and output quality degradation — alerting your team to issues and triggering remediation before business impact occurs.
How It Works with a21
Monitoring Architecture Design
Define what to monitor for each model — input distribution, prediction distribution, ground truth labels, and output quality metrics. Design the monitoring stack and alerting thresholds.
Instrumentation & Baseline
Instrument production systems to capture the data needed for monitoring. Establish statistical baselines for all monitored metrics using historical production data.
Alert Management & Remediation
Deploy alerting with tiered severity levels. Define and test remediation playbooks for each alert type. Provide ongoing monitoring operations and monthly drift reports.
What We Offer
Data Drift Detection
Monitor input feature distributions for statistical drift using population stability index, KL divergence, and Wasserstein distance — detecting when the real world diverges from training data.
Prediction Drift Monitoring
Monitor model output distributions for changes that indicate concept drift — catching model degradation even when ground truth labels are unavailable.
Ground Truth Labelling & Accuracy Tracking
Design ground truth collection workflows and track model accuracy over time — providing the evidence base for retraining decisions.
LLM Output Quality Monitoring
Monitor GenAI system outputs for quality metrics — relevance, faithfulness, format adherence, and safety — using automated LLM-based evaluation.
Alerting & Escalation
Tiered alerting — warning, critical, emergency — with defined escalation paths and SLAs for each severity level.
Monthly Drift Reports
Structured monthly reports summarising drift metrics, alert history, model accuracy trends, and recommendations for remediation or retraining.
Why Choose a21
GenAI and Classical ML Coverage
We monitor both traditional ML models and GenAI systems — with specialised metrics for each. Most monitoring tools cover one or the other, not both.
Early Warning System
We detect issues at the statistical level before they manifest as user complaints or business errors — giving you time to remediate without impact.
Regulated Industry Standards
Our monitoring documentation satisfies model risk management requirements — providing the ongoing performance evidence that SR 11-7 and similar frameworks require.
Actionable Alerts
Our alerts include context — what drifted, by how much, and what the likely cause and remediation options are. Not just alarm bells.
Success Stories
Problem
A lender’s credit scoring model had no formal monitoring — the only signal of degradation was an uptick in bad debt six months after it occurred, by which time significant losses had accumulated.
Solution
Implemented input drift monitoring, prediction distribution tracking, and a monthly accuracy review process using delayed ground truth labels from loan performance data.
Problem
A healthtech company had no visibility into whether their clinical NLP models were performing consistently across different hospital sites and patient populations.
Solution
Deployed site-level input drift monitoring, prediction distribution tracking, and LLM-based output quality evaluation across 12 hospital deployments.
Tech Stack & Tools
Evidently AI
WhyLabs
Fiddler AI
MLflow
Prometheus / Grafana
Great Expectations
Custom LLM evaluators
Get Started
Stop discovering model problems after the damage is done. Talk to a21 about model monitoring.
