AI for Operations

AIOps: AI powering IT operations

As a consulting firm specializing in IT infrastructure, we help our clients develop new AI use cases designed for production teams.

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AIOps is becoming essential in modern IT environments.

Faced with the growing complexity of systems, the ever-increasing volume of alerts, and the demand for real-time availability, AIOps has emerged as a key driver for modernizing infrastructure management and strengthening operational resilience.

IT environments are becoming increasingly hybrid, dynamic, and difficult to monitor using traditional methods. Teams are overwhelmed by a flood of information — logs, metrics, and incidents — that can no longer be analyzed manually in real time.

AIOps addresses this challenge by automating the collection, analysis, and correlation of data from multiple sources. It enables faster anomaly detection, better incident anticipation, and improved responsiveness through automated remediation.

How we help you successfully deliver your AIOps initiatives

Our experience in innovation and IT operations management enables us to successfully guide your AIOps journey, ensuring the integration of the right AI methodologies and tools.

Audit and diagnostic

We conduct a comprehensive analysis of your infrastructure, monitoring tools, and operational culture to identify automation opportunities and AIOps readiness requirements.

Focused AIOps prototype

We quickly deploy an initial AIOps use case within a pilot scope, such as anomaly detection, alert noise reduction, or script automation.

Progressive deployment of AIOps

Integration of AIOps tools into the existing ecosystem (Prometheus, ELK, Grafana, Kubernetes, etc.) using an iterative and secure approach.

Training and cultural adoption

Raising your teams’ awareness of responsible AI practices in IT environments, data security, and the best practices to ensure the long-term success of your AIOps strategy.

Key AIOps use cases

Real-time anomaly detection

Automatic analysis of metrics (CPU, memory, latency, etc.)
Detection of abnormal behavior using ML models

Incident response automation

Automatic execution of scripts (Terraform, Ansible)
Integration with Teams and Slack

Intelligent log analysis

Use of Large Language Models (LLMs) to interpret logs and enable natural language queries
Automatic generation of Root Cause Analysis (RCA)

Alert classification and correlation

Noise reduction using NLP (Natural Language Processing) or clustering algorithms
Correlation of logs and infrastructure events to identify root causes faster

Resource optimization

Recommendations for automatic scaling of Kubernetes pods
Prediction of resource requirements de RCA (Root Cause Analysis).

Backup monitoring and automation

Automatic verification of successful backup execution
Alerts in case of failure or anomalies in backup duration
Proactive recommendations on backup frequency and storage based on activity peaks

4 reasons to work with us

We have a pool of expert profiles ready to meet your specific AIOps project needs, including DevOps engineers, cloud architects, AI specialists, and cybersecurity experts.

We have deep expertise in complex environments, orchestration tools (Kubernetes, Terraform), and monitoring platforms.

We prioritize a phased deployment approach that delivers visible results within the first few weeks.

We leverage private LLM models and robust frameworks (OpenAI, Mistral, scikit-learn, LangChain) within a secure environment.

AI charter, cybersecurity leads, and strict GDPR compliance — we make data protection a cornerstone of every project.

Accelerate your IT transformation with AIOps

Our experts support you in automating your operations and enhancing your performance.

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Frequently Asked Questions

Is AIOps suitable for my IT environment?

Yes, if you have monitoring tools, actionable metrics, and a basic understanding of DevOps. We can help you lay the groundwork if needed.

What types of incidents can AIOps detect?

Data for Ops leverages a wide range of technical data: system logs, performance metrics, monitoring alerts, application traces, security logs, resource usage indicators, etc. This data is centralized, cleaned, and then integrated into ETL/ELT pipelines to feed dashboards or predictive analytics models.

Is it compatible with my existing tools?

Yes. AIOps integrates with existing tools: Prometheus, ELK, Grafana, Ansible, Terraform, Slack, Teams, etc.

Does AIOps mean losing control for IT teams?

Implementing a Data for Ops solution relies on monitoring tools (Grafana, Kibana, Metabase, Power BI), data processing pipelines (ETL/ELT), AI/ML analytics platforms for anomaly detection, and custom dashboards for IT teams. These technologies enable advanced monitoring, improved data governance, and faster decision-making.

How can data security be ensured with AIOps?

Synapsys offers a comprehensive approach to structuring and leveraging operational data: a rapid audit of existing data, definition of priority use cases, implementation of a proof of concept (POC), creation of dashboards tailored to operational roles, integration of critical data, and team training. The goal is to transform raw data into high-impact, sustainable, and controlled operational decisions.