AI process monitoring: anomalies, alerts, and immediate actions

AI monitoring: data analysis, deviation alerts, failure prediction, and automatic incident response.

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Our clients

Capital Group
FSK Group
SMLT
Tochno
Dogma
Sber City
FM Logistic
Danone
Relief Center
Pandora
AI monitoring is automatic control of processes and events
Saint-Gobain
Askona
FIX PRICE
Snezhnaia Koroleva
Muztorg
TVOE
Greenway
Polaris
Campari
Yandex
Lenta
International perfume and cosmetics brand
AI monitoring is automatic control of processes and events
RAEC
EKF
L'Etoile
Inventive Retail Group

We deploy continuous monitoring systems for infrastructure, services, and processes. ML-based anomaly analysis and instant alerts help you respond sooner, not after a failure. Systems go down, processes stall, and users find out first. We implement AI monitoring that responds to failures before they become a problem. AI monitoring for processes: anomalies, alerts, and instant actions

Real-time anomaly detection

AI detects not only failures but also "pre-incidents": API delays, rising load, and anomalous service behavior. All before complaints and downtime.

Integration with DevOps, business processes, and ITSM

Linking monitoring with Jira, ServiceNow, CI/CD, and Kafka makes it possible to build automatic responses: restart, failover, and task creation.

Prediction of failures and degradation

Models are trained on historical data and predict where and when a failure is likely, what caused the issues, and which nodes are overloaded.

Priority notifications with actions

Not just an alert that something broke, but concrete actions: who to notify, where to look, what to disable. This saves hours of troubleshooting.

Efficiency and growth in one solution

  1. From reactive IT to a predictive model that signals before an incident. The solution includes:

  2. We configure collection of technical and business metrics: errors, response time, outages, and delays

  3. We train the model to detect anomalies in user behavior, traffic, APIs, and load

  4. We integrate with DevOps, Jira, ServiceNow, CI/CD, and alerts, so response follows a predefined scenario. Business outcome:

  5. Failures and degradation are detected early, before complaints

  6. The causes of problems are eliminated, not just the consequences

  7. Downtime and service losses are sharply reduced. Solutions without unnecessary complexity, from idea and analysis to results.

Assess where AI can deliver impact in your process

clients@kt.team Telegram @kt_team_it

We will study your processes and propose a ready-to-use implementation plan

  1. We consult We discuss goals and tasks, define priorities, and set expected outcomes for the joint work

  2. We analyze your processes We study current processes and approaches, identify growth points, and determine which solution will deliver tangible results

  3. We plan the solution rollout, define scope, stages, and timelines, assign responsibilities, and agree on the criteria for success.

  4. Launch and support We implement the solution, train your team and provide support so the solution delivers tangible value

AI monitoring delivered by practitioners, not theorists

We implement AI monitoring for IT and business processes: the system detects anomalies and predicts failures. Response happens before an incident, not after user complaints. 1-2 months - setup and integration of AI monitoring 15+ projects delivered in retail and real estate development 70% reduction in downtime through early problem detection 100% automated response to critical failures Client reviews

FAQ

Frequently Asked Questions about AI Monitoring

How is this different from standard monitoring?

Traditional monitoring detects an outage after it has already happened using predefined thresholds. Here, the model is trained on historical data and identifies pre-incident signals: API delays, increased load, and abnormal service behavior before user complaints and downtime.

What data is the model trained on?

On historical technical and business metrics: errors, response time, outages and delays, as well as user behavior, traffic, load, and API calls. The model looks for deviations from the normal profile rather than comparing a metric with a fixed threshold.

What happens after an alert is triggered?

The alert includes priority and specific actions - who to notify, where to look, what to disable - instead of a vague "something broke" message. That saves hours of incident troubleshooting.

Which environment does it fit into?

In DevOps, business processes, and ITSM: integration with Jira, ServiceNow, CI/CD, and Kafka makes it possible to set up automated responses - restart, failover to backup, and creating a task for the responsible owner.

How is implementation carried out?

First we discuss goals and priorities, then we study the processes. After that, we configure the collection of technical and business metrics, train the model to detect anomalies, integrate response scenarios into Jira, ServiceNow, and CI/CD, train the team, and support the system.

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