Perimattic builds AI observability platforms that give you full visibility into model performance, data drift, and system health across your ML pipeline — enabling proactive monitoring, faster debugging, and AI systems your business can depend on in production.
Observability Technologies We Build With — Prometheus, Grafana, MLflow, Evidently AI, WhyLabs, DataDog
AI observability is the practice of monitoring, tracking, and understanding the behaviour of AI and machine learning systems in production. Unlike traditional software monitoring — which tracks infrastructure metrics like uptime, CPU, and memory — AI observability covers model accuracy, data quality, feature drift, and prediction confidence. These are the failure modes that matter most for AI systems, and they are invisible to standard infrastructure monitoring tools. A model can fail silently for weeks: returning predictions with high confidence while accuracy quietly degrades as the world changes around it.
AI observability platform development involves designing and building the full infrastructure stack that makes these failure modes visible and actionable. This means metric collection pipelines, time-series storage, drift detection algorithms, explainability dashboards, alert routing, experiment tracking, and feature lineage — connected to your existing MLOps tooling and incident management workflow. The platform does not just tell you when something is wrong. It tells you what changed, which model is affected, what the data looks like upstream, and what actions are available.
Perimattic builds AI observability platforms that are purpose-built for your model landscape, not generic SaaS tools configured around your requirements. We instrument your model serving infrastructure, build drift detection tuned to your data distributions and business tolerances, design dashboards that surface the metrics your team actually uses to make decisions, and configure alert routing that reaches the right people with the right context — without the noise that causes teams to ignore alerts entirely. The result is full-stack visibility from raw data ingestion through to production model outputs.
Monitoring scope
Infrastructure only — uptime, CPU, memory, response time
Monitoring scope
Full ML pipeline: data quality, feature drift, model accuracy, prediction confidence
Drift detection
No automated drift detection — model degradation goes unnoticed until business impact
Drift detection
Automated data and concept drift detection with configurable alert thresholds and retraining triggers
Explainability
No model explainability — impossible to understand why predictions changed
Explainability
Per-prediction SHAP and LIME explanations, feature importance tracking, and persistent decision audit trails
Alert intelligence
Infrastructure alerts only — no correlation with model performance trends
Alert intelligence
Model-aware alerting that distinguishes infrastructure issues from accuracy degradation and data quality failures
Compliance support
No auditability of model decisions — fails regulatory review under GDPR, EU AI Act, SR 11-7
Compliance support
Prediction logs, model version tracking, and explainability records designed for audit and regulatory compliance
The distinction matters most for production AI: a model can degrade silently for weeks without triggering any infrastructure alert. AI observability makes that invisible failure mode visible — before it affects business outcomes.
Seven specialist service lines covering every dimension of a production AI observability engagement — from architecture through instrumentation, drift detection, explainability, and ongoing model health reviews.
Real-time tracking of accuracy, precision, recall, AUC, latency, throughput, and error rates across all deployed models. Rolling time-window dashboards surface performance trends before they become business incidents.
Automated detection of input data distribution shifts and concept drift — when the relationship between inputs and outputs changes. Configurable alerting thresholds trigger retraining workflows before accuracy degrades to business-impacting levels.
Visual tools that surface why your models make specific predictions using SHAP values, LIME, and attention visualisation depending on the model type. Supports regulatory compliance, engineer debugging, and stakeholder transparency requirements.
Configurable multi-tier alert rules covering data quality, model performance, and infrastructure health. Alert routing to PagerDuty, Slack, and OpsGenie with configurable severity levels, escalation paths, and suppression rules to prevent alert fatigue.
Built-in infrastructure for comparing model versions under live traffic, measuring statistical significance of performance differences, and tracking experiments from hypothesis through production rollout. Guardrail alerts prevent regressions during experiments.
Connect to your feature store for end-to-end lineage tracking from raw data through feature engineering to model predictions. Data lineage dashboards surface feature staleness, upstream pipeline failures, and schema drift before they affect model accuracy.
Strategy-first engagement covering metric taxonomy design, tooling selection, integration architecture, and platform roadmap before any instrumentation work begins. Ensures your observability investment fits your model landscape, team structure, and compliance requirements.
A six-phase process from free discovery through production deployment and ongoing model health reviews. Every phase delivers a concrete output your team owns and operates.
We audit your current model landscape, data pipelines, serving infrastructure, and any existing monitoring tooling. We define the critical models, key performance metrics, alert thresholds, and compliance requirements that will drive the platform design. This session is free and carries no obligation.
We design the observability architecture: metric storage strategy, drift detection approach, dashboard topology, alert routing design, and integration points. We select the tooling combination that extends your existing infrastructure rather than competing with it, and produce a technical design document for review.
We instrument your model serving infrastructure, data ingestion pipelines, and feature engineering code with the metrics, logs, and traces needed to power the observability platform. Instrumentation is designed to be non-invasive — core model serving code is not modified.
We build operational dashboards for each model and pipeline, configure drift detection with calibrated thresholds, define multi-tier alert rules, and wire alert routing to your incident management tools. All configurations are reviewed with your team before go-live.
We validate drift detection sensitivity against historical drift events in your data, load test the metrics pipeline under production traffic, and deploy the platform with zero-downtime rollout. A parallel monitoring period confirms parity before legacy tooling is decommissioned.
Post-deployment, we run monthly model health reviews covering performance trends, alert effectiveness, and coverage gaps. We refine thresholds based on production experience, extend coverage to additional models, and improve signal-to-noise ratio to prevent alert fatigue.
We audit your current model landscape, data pipelines, serving infrastructure, and any existing monitoring tooling. We define the critical models, key performance metrics, alert thresholds, and compliance requirements that will drive the platform design. This session is free and carries no obligation.
We design the observability architecture: metric storage strategy, drift detection approach, dashboard topology, alert routing design, and integration points. We select the tooling combination that extends your existing infrastructure rather than competing with it, and produce a technical design document for review.
We instrument your model serving infrastructure, data ingestion pipelines, and feature engineering code with the metrics, logs, and traces needed to power the observability platform. Instrumentation is designed to be non-invasive — core model serving code is not modified.
We build operational dashboards for each model and pipeline, configure drift detection with calibrated thresholds, define multi-tier alert rules, and wire alert routing to your incident management tools. All configurations are reviewed with your team before go-live.
We validate drift detection sensitivity against historical drift events in your data, load test the metrics pipeline under production traffic, and deploy the platform with zero-downtime rollout. A parallel monitoring period confirms parity before legacy tooling is decommissioned.
Post-deployment, we run monthly model health reviews covering performance trends, alert effectiveness, and coverage gaps. We refine thresholds based on production experience, extend coverage to additional models, and improve signal-to-noise ratio to prevent alert fatigue.
AI observability requirements differ significantly by sector. Select an industry to see how Perimattic approaches the specific model types, data sensitivity, compliance requirements, and monitoring cadences involved.
AI observability in healthcare ensures clinical decision support models, diagnostic algorithms, and patient outcome predictors remain accurate and explainable across evolving patient populations — with HIPAA-aligned audit trails.
AI observability in healthcare ensures clinical decision support models, diagnostic algorithms, and patient outcome predictors remain accurate and explainable across evolving patient populations — with HIPAA-aligned audit trails.
AI observability in financial services ensures credit, fraud detection, trading, and risk models remain accurate and auditable in volatile market conditions and under increasing regulatory scrutiny.
AI observability for legal technology platforms ensures document analysis, contract intelligence, and compliance screening models remain accurate and explainable as regulatory frameworks evolve and document volumes grow.
AI observability in e-commerce ensures recommendation, pricing, demand forecasting, and personalisation models respond correctly to seasonal patterns, product catalogue changes, and evolving consumer behaviour.
AI observability in manufacturing ensures predictive maintenance, quality control, and process optimisation models stay accurate as operating conditions, equipment age, material inputs, and production volumes change.
AI observability for SaaS platforms ensures in-product AI features, LLM-powered assistants, and ML-driven automation remain reliable, relevant, and explainable as user bases grow and product functionality evolves.
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 to 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 distinguish production-grade AI observability from dashboards that look good in demos but miss the failure modes that matter.
Most monitoring vendors understand infrastructure. Perimattic understands machine learning. Our team has production experience with supervised, unsupervised, and LLM-based systems — and understands the failure modes unique to each. We instrument at the right layer, with the right metrics, for the right model type.
Every observability platform Perimattic builds includes data encryption in transit and at rest, role-based access controls on dashboards and alert rules, audit logging of all platform queries and configuration changes, and data residency controls. Compliance requirements — HIPAA, GDPR, SR 11-7 — are designed in from the architecture phase.
We do not prescribe a single observability stack. We evaluate Prometheus, Grafana, DataDog, Evidently AI, WhyLabs, MLflow, and Weights & Biases against your existing infrastructure, team familiarity, and commercial constraints. The platform we build extends what you have rather than creating a competing toolchain.
Perimattic does not hand you a configuration file and expect your team to operationalise it. We deliver the complete platform: metric taxonomy, instrumentation, dashboards, alerts, runbooks, and ongoing model health reviews. If you also need the models retrained or the AI applications extended, our AI development practice connects without a handover gap.
“Unlike generic infrastructure monitoring tools that treat AI systems the same as web applications, Perimattic builds AI observability platforms engineered from day one for the failure modes unique to machine learning: data drift, concept drift, prediction confidence decay, and model accuracy degradation.”
Monitoring tracks predefined metrics: uptime, latency, error rate. Observability goes deeper — it lets you ask arbitrary questions about system behaviour, understand why a model produced a specific output, and detect failure modes you did not anticipate when you built the monitoring rules. Monitoring tells you when something breaks. Observability tells you why, and often surfaces problems before they break. AI systems have unique failure modes — data drift, concept drift, feature inconsistency, prediction confidence decay — that standard infrastructure monitoring tools do not detect. Perimattic builds platforms that cover both.
A focused proof of concept covering a single model, a core metrics dashboard, and basic drift detection can be delivered in two to four weeks. A production deployment with multi-model support, custom dashboards, alert routing, feature lineage, and integration into your incident management tools typically takes eight to fourteen weeks. Costs range from USD 25,000 for a scoped PoC to USD 100,000 for an enterprise platform covering a complex model landscape. Perimattic provides a detailed cost estimate after the free discovery session based on your specific model count, data volumes, and integration requirements.
We integrate with the full observability stack depending on your existing infrastructure. For infrastructure metrics: Prometheus, Grafana, DataDog, and New Relic. For experiment tracking and model registry: MLflow and Weights & Biases. For drift detection: Evidently AI and WhyLabs. For time-series storage: InfluxDB. For real-time data pipelines: Apache Kafka. For distributed tracing: OpenTelemetry. For alert routing: PagerDuty, Slack, and OpsGenie. We select and configure the combination that matches your team's existing tooling rather than introducing a competing stack.
Data drift occurs when the statistical properties of the data your model processes in production diverge from the data it was trained on. This matters because machine learning models are fundamentally pattern-recognition systems — when input patterns change, prediction quality degrades, often without any visible technical error. A fraud model trained on pre-2022 transaction patterns will miss post-pandemic fraud vectors without reporting any exceptions. A credit model trained on a bull-market applicant pool will underestimate default risk in a rising rate environment. Perimattic's platforms monitor input distributions continuously and alert your team when drift exceeds configurable thresholds — enabling proactive retraining before accuracy degrades to business-impacting levels.
Detection approach depends on whether ground truth labels are available. When labels are available — for example, loan default predictions where actual defaults are confirmed within weeks — we compare model predictions against actuals in rolling time windows and generate precision, recall, and AUC trend reports. When labels are not immediately available — for example, demand forecasts — we monitor input distribution drift, output distribution shift, prediction confidence score distributions, and upstream data quality signals as leading indicators of degradation. We configure degradation thresholds specific to your use case and business tolerance, and build dashboards that surface degradation trends before they become business incidents.
Model explainability is the ability to communicate why a model made a specific prediction. In an observability context, explainability tools serve two distinct purposes. For engineers: they surface which features are driving unexpected predictions, which is essential for debugging performance degradation and data quality issues. For compliance: in financial services, insurance, and healthcare, regulators increasingly require that automated decisions can be explained to the people affected by them. Perimattic builds explainability dashboards using SHAP values, LIME, and attention visualization depending on the model type, and documents the explanation methodology in a persistent audit trail.
Integration is designed to be non-invasive. If you have a model registry in MLflow or Weights & Biases, we connect the observability platform to consume model metadata and versioning directly. If you have infrastructure monitoring in Prometheus and Grafana, we extend it with AI-specific metrics and panels rather than competing with it. If you have data pipelines in Kafka or Airflow, we instrument them at the ingestion layer to detect upstream data quality issues before they reach the model. Most integrations add monitoring as a sidecar or interceptor — your core model serving code is not modified.
A well-designed AI observability platform generates three categories of alert. Data quality alerts fire when upstream data violates expected schemas, contains unusual proportions of null values, or shows distributional shifts in key features. Model performance alerts fire when rolling accuracy, precision, recall, or custom business metrics fall below configured thresholds or exhibit statistically significant downward trends. Infrastructure alerts fire when model serving latency, error rate, memory usage, or GPU utilisation exceed thresholds. All alerts route to your existing incident management tools — PagerDuty, Slack, OpsGenie — with configurable severity levels and escalation paths.
Regulatory frameworks in financial services, healthcare, and insurance increasingly require organisations to demonstrate that automated decision systems are accurate, fair, and auditable. An AI observability platform provides three compliance-critical capabilities. Audit trails: every prediction, input feature set, and model version is logged, so any automated decision can be reconstructed and reviewed. Fairness monitoring: demographic parity, equal opportunity, and calibration metrics can be monitored across protected characteristics. Explainability records: model explanations for individual predictions can be generated on demand and archived, satisfying explainability requirements under GDPR, the EU AI Act, and sector-specific frameworks such as SR 11-7 for model risk management.
A focused proof of concept covering instrumentation of a single model, a core metrics dashboard, and basic data drift detection can be delivered in two to four weeks. This is enough to demonstrate the value of full observability and validate the technical integration approach before committing to a production deployment. The PoC runs against your actual model and live data, not synthetic examples, so the results are directly applicable to your production decisions. The free discovery session — typically 90 minutes — is the prerequisite: we use it to scope the PoC and confirm that the integration approach is sound before any work begins.
Perimattic offers a free discovery session to review your current model landscape, identify critical monitoring gaps, and scope the right observability architecture for your infrastructure.
No generic SaaS dashboards. No vendor lock-in. An AI observability platform built for the specific models, data pipelines, and failure modes that matter to your business.