Most production AI failures are silent. Models drift, accuracy erodes, and latency spikes — often without raising an exception. Perimattic builds AI model monitoring solutions that continuously track performance, detect data and concept drift, and trigger automated alerts before degradation impacts your customers or compliance posture.
AI Model Monitoring Tools and Platforms — Evidently AI, WhyLabs, Fiddler AI, Arize AI, Prometheus, Grafana, Datadog
AI model monitoring is the continuous measurement of a deployed model's accuracy, performance, and input/output data distributions over time. Unlike application monitoring — which fires alerts when code throws an error — model monitoring catches the silent failures that happen when real-world data drifts away from training distributions, when the relationship between inputs and targets changes, or when serving infrastructure changes cause latency to climb.
The business cost of unmonitored models is significant. A fraud detection model quietly degrades as new fraud patterns emerge. A credit scoring model trained on pre-recession data becomes unreliable as economic conditions shift. A recommendation engine loses accuracy as product catalogues and customer behaviour evolve. Without monitoring, each of these failures surfaces only after the damage has already affected customers, compliance posture, or revenue.
Perimattic builds AI model monitoring platforms that track the full spectrum of model health: accuracy metrics, data and concept drift, bias and fairness across demographic groups, inference latency, and the business KPIs downstream of each model. Our monitoring stacks integrate with your existing MLOps pipelines and route alerts to the right people before degradation reaches users.
Detection method
Reactive threshold alerts fired only after failures occur
Detection method
Proactive statistical drift detection before failures reach users
Coverage scope
Infrastructure errors and application exceptions only
Coverage scope
ML-specific metrics: accuracy, drift, bias, latency, and business KPIs
Model visibility
Application logs only — no visibility into predictions or features
Model visibility
Full tracking of predictions, feature distributions, and explanations
Retraining triggers
Manual discovery required — retraining happens reactively or on a fixed schedule
Retraining triggers
Automated drift-triggered retraining integrated with your MLOps pipeline
Compliance evidence
Basic error logs — model decisions are difficult to reconstruct or audit
Compliance evidence
Full model lineage, explainability reports, and audit trail logging
The gap matters most in regulated environments — financial services, healthcare, and legal — where model failures carry compliance consequences and where regulators expect documented evidence of model oversight.
Seven specialist monitoring capabilities, each designed to address a specific dimension of production model health.
Real-time accuracy, precision, recall, and F1 dashboards across all production models with cohort breakdowns, trend visualisation, and configurable alert thresholds.
Statistical monitoring for data and concept drift using PSI, KS tests, and distribution comparison across input features and model outputs — detecting shifts before accuracy drops.
Continuous demographic parity, equalised odds, and disparate impact monitoring across protected attributes and feature subgroups to meet regulatory and ethical AI requirements.
P50, P95, and P99 inference latency tracking with SLA enforcement and automatic alerting on throughput degradation — keeping your model serving contracts intact.
Configurable alert pipelines to Slack, PagerDuty, email, and webhook endpoints with severity tiers, on-call routing, and escalation policies for critical model failures.
SHAP and LIME integration for production explanation tracking — enabling root cause analysis when performance drops and providing the explainability artefacts required for regulated industries.
Drift-threshold-based retraining triggers that connect directly to your MLOps pipeline, closing the model maintenance loop automatically without manual intervention.
A structured six-stage process from free monitoring assessment through full instrumentation, dashboard build, and ongoing model portfolio support.
We audit your current models, pipelines, and alerting gaps to define which metrics, features, and thresholds matter most for your business. This session is free and carries no obligation. You leave with a clear monitoring blueprint.
We design your monitoring schema: which statistical tests to apply, which business KPIs to track alongside ML metrics, how to handle ground truth delay, and how to structure dashboards for your team's workflow.
We instrument your inference endpoints, feature stores, and data pipelines, integrating with your existing MLOps stack using FastAPI middleware or Kafka stream consumers — without disrupting production.
We build and configure monitoring dashboards in Grafana, Datadog, or Evidently AI, and set up alert routing to your incident management system with severity tiers and on-call escalation.
We run drift simulations, injection tests, and alerting drills to confirm detection accuracy and alert routing before go-live. Load and failure-scenario testing are included.
We deploy the monitoring stack, train your team on dashboards and runbooks, and provide ongoing support as your model portfolio grows and monitoring requirements evolve.
We audit your current models, pipelines, and alerting gaps to define which metrics, features, and thresholds matter most for your business. This session is free and carries no obligation. You leave with a clear monitoring blueprint.
We design your monitoring schema: which statistical tests to apply, which business KPIs to track alongside ML metrics, how to handle ground truth delay, and how to structure dashboards for your team's workflow.
We instrument your inference endpoints, feature stores, and data pipelines, integrating with your existing MLOps stack using FastAPI middleware or Kafka stream consumers — without disrupting production.
We build and configure monitoring dashboards in Grafana, Datadog, or Evidently AI, and set up alert routing to your incident management system with severity tiers and on-call escalation.
We run drift simulations, injection tests, and alerting drills to confirm detection accuracy and alert routing before go-live. Load and failure-scenario testing are included.
We deploy the monitoring stack, train your team on dashboards and runbooks, and provide ongoing support as your model portfolio grows and monitoring requirements evolve.
Select an industry to see how production model monitoring prevents accuracy degradation and protects business outcomes in that domain.
Financial models for fraud detection, credit scoring, and trading face continuous distribution shift as market conditions and fraud patterns evolve — making real-time monitoring critical.
Financial models for fraud detection, credit scoring, and trading face continuous distribution shift as market conditions and fraud patterns evolve — making real-time monitoring critical.
Clinical AI models must maintain high accuracy as patient populations, coding practices, and care protocols shift — and compliance demands full explainability.
Recommendation engines, pricing models, and demand forecasting systems experience rapid data drift during seasonal events and catalogue changes — requiring continuous monitoring.
Predictive maintenance and quality inspection models degrade as equipment ages and production conditions change — undetected drift leads to costly failures or missed defects.
Software companies running LLMs, search ranking, and churn prediction at scale need multi-model monitoring with latency SLA enforcement and cost tracking.
Legal AI systems require strict accuracy tracking, chain-of-custody logging, and governance workflows that satisfy enterprise compliance and external audit requirements.
Perimattic's work resulted in stable production systems. The team was helpful, easily accessible, and communicative through email. Their professionalism was impressive.
Quality
4.5
Schedule
5.0
Cost
5.0
Willing to Refer
4.5
DevOps Managed Services · Oct 2023 – Aug 2024
24/7 monitoring and support for production environments plus Linux server administration for a leasing automation company.
The new architecture is scalable and highly efficient, saving a lot of money in fees. Perimattic provides high-quality IT consulting and cloud development work promptly and at great value. The team remains involved from the planning stage to providing support, showing diligence and proactiveness.
Quality
5.0
Schedule
5.0
Cost
4.5
Willing to Refer
5.0
AWS Migration (Legacy → Microservices) · Nov 2018 – Ongoing
Transitioned a travel systems company's legacy server system to an AWS-based microservices architecture with ongoing maintenance.
Four structural advantages that separate purpose-built model monitoring from generic infrastructure alerts and point solutions.
We go beyond infrastructure monitoring to track ML-native signals: prediction distributions, feature drift, embedding shifts, and ground-truth-delayed accuracy estimation — the metrics that actually tell you whether your model is still working.
Every monitoring implementation is built for early detection, not post-mortem analysis. Statistical drift thresholds fire before accuracy drops are visible in business metrics — giving your team time to act before users are affected.
Our monitoring stacks instrument your existing inference endpoints and MLOps pipelines without requiring model rewrites or serving infrastructure changes. We meet your stack where it is, not where we would prefer it to be.
Every monitoring deployment includes model lineage tracking, SHAP-based explanation dashboards, and audit trail logging — providing the governance artefacts required for regulatory review and internal model risk management.
“Unlike infrastructure monitoring tools that only tell you a service is up, Perimattic's model monitoring tells you whether your AI is still making the right decisions — before your customers tell you it isn't.”
AI model monitoring is the continuous measurement of a deployed model's accuracy, performance, and input and output data distributions over time. Production models degrade silently — real-world data shifts away from training data (data drift), the relationship between inputs and targets changes (concept drift), and latency grows as traffic increases. Without monitoring, these issues go undetected until they cause visible business failures. AI model monitoring provides the visibility needed to catch and correct degradation before it reaches users.
Our monitoring systems detect drift and performance anomalies within minutes using statistical tests such as PSI, KS tests, and CUSUM algorithms applied to live inference data. Alert routing to Slack, PagerDuty, or email typically completes within 30 seconds of threshold breach. Detection speed depends on traffic volume — high-traffic models can be monitored in near-real-time, while low-traffic models use windowed statistical tests across defined time periods.
Data drift (also called covariate shift) occurs when the statistical distribution of model inputs changes — for example, when new customer segments or fraud patterns appear that were not present in training data. Concept drift occurs when the relationship between inputs and the target variable changes — for example, when economic conditions shift and historical features no longer predict the same outcomes. Both forms of drift degrade model accuracy and require different detection strategies. We monitor for both simultaneously.
AI model monitoring focuses specifically on ML model performance metrics: prediction accuracy, data and concept drift, bias, fairness, and inference latency. AI observability is broader, covering the entire AI system — including infrastructure traces, logs, embeddings, chain-of-thought reasoning, and system-level health. Model monitoring answers 'Is this model still accurate?' Observability answers 'Is this entire AI system working correctly?' The two are complementary, and we offer both as separate services.
Our stack centres on purpose-built ML monitoring platforms including Evidently AI, WhyLabs, Fiddler AI, and Arize AI for drift detection, bias monitoring, and performance dashboards. For observability infrastructure we use Prometheus, Grafana, and Datadog. For alerting and incident routing we use PagerDuty and Apache Kafka. The specific tools selected for each engagement depend on your existing MLOps stack, latency requirements, and compliance constraints — we are tool-agnostic and recommend the best fit.
Ground truth delay is one of the most common challenges in production monitoring. We use proxy metrics and indirect signals to bridge the gap: input feature distribution monitoring, prediction confidence distribution shifts, business outcome metrics such as conversion rates and return rates, and embedding drift for deep learning models. When ground truth labels eventually arrive — even days or weeks later — we use them to recalibrate drift thresholds and validate that proxy signals correctly predicted degradation.
Large language models require different monitoring approaches than traditional ML models. We monitor LLM outputs using embedding drift detection, semantic similarity scoring, output length and format consistency, toxicity and safety classifier pass rates, and user feedback signals. For RAG systems we additionally monitor retrieval quality and context utilisation. We integrate with LLM evaluation frameworks and custom human feedback pipelines to build ground truth over time.
Yes. We build monitoring integrations for all major MLOps platforms including MLflow, Kubeflow, Seldon Core, BentoML, AWS SageMaker, Azure ML, and GCP Vertex AI. Monitoring hooks are inserted at inference time — typically via FastAPI middleware or Kafka stream consumers — without requiring changes to your model serving code. Drift-triggered retraining integrates directly with Airflow, Prefect, or Argo Workflows to close the retraining loop automatically.
The core metrics fall into four categories. Performance metrics: accuracy, precision, recall, F1, AUC-ROC, and RMSE depending on model type. Data quality metrics: feature distribution PSI scores, missing value rates, and out-of-range input detection. Operational metrics: P50, P95, and P99 inference latency, throughput, error rates, and resource utilisation. Business metrics: conversion rate, churn rate, or any downstream KPI the model is intended to move. We configure monitoring across all four categories to give a complete view of model health.
Monitoring engagement cost depends on the number of models, inference volume, chosen tool stack, and required alerting complexity. Engagements typically begin with a free monitoring assessment of your current setup. Implementation projects range from targeted single-model monitoring setups to enterprise-wide monitoring platforms covering dozens of models across multiple cloud environments. Contact us to discuss your specific requirements and receive a scoped estimate.
Tell us about your production models and current monitoring gaps — we will show you exactly where drift detection, latency monitoring, and automated alerting can prevent the next silent failure before it reaches your customers.