Services
Application of machine learning to IT operations to automate issue resolution, predict server outages, and manage complex cloud infrastructure without human intervention. Sub-services include predictive IT incident management AI, automated root cause analysis algorithms, multi-cloud AIOps deployment, and intelligent network monitoring systems.
Deployment of machine learning models that analyze historical and real-time telemetry to forecast IT incidents before they cause downtime, focusing on Mean Time to Resolution (MTTR) reduction and proactive alerting.
Development of causal inference and graph-based AI algorithms that automatically identify the primary source of complex, multi-layer IT failures, drastically reducing manual investigation time.
Architecture and deployment of unified AIOps platforms that ingest, correlate, and analyze data across AWS, Azure, GCP, and private clouds to provide a single pane of glass for heterogeneous environments.
Implementation of deep learning for network traffic analysis and anomaly detection, using models like LSTMs to predict congestion, identify security threats, and optimize performance in real-time.
Engineering of unsupervised machine learning systems that establish dynamic baselines for thousands of metrics, detecting subtle deviations indicative of impending failures in servers, applications, and databases.
Development of predictive maintenance models for physical and virtual infrastructure, using sensor data and logs to forecast hardware failures and performance degradation weeks in advance.
Creation of closed-loop automation where AI not only diagnoses issues but also executes pre-approved remediation scripts, enabling autonomous recovery for common failure patterns.
Integration of NLP and conversational AI to automate tier-1 support ticket classification, routing, and resolution, integrating with ITSM tools like ServiceNow and Jira Service Management.
Deployment of machine learning for FinOps, analyzing cloud usage patterns to identify waste, recommend right-sizing, and forecast spend, directly integrating with AWS Cost Explorer and Azure Cost Management.
Engineering of time-series forecasting models that predict future infrastructure demand based on business growth metrics, seasonal trends, and application deployment cycles.
Development of advanced NLP and pattern recognition systems to parse unstructured log data at scale, extracting actionable insights and correlating events across disparate sources.
Architecture of next-gen observability platforms that unify metrics, traces, and logs with AI-driven analytics, moving beyond dashboards to automated insights and narrative generation.
Specialized AIOps services for orchestrated environments, providing anomaly detection, performance optimization, and failure prediction for microservices running on Kubernetes and Docker.
Implementation of AI to reduce alert fatigue by clustering related alerts, suppressing duplicates, and identifying the single actionable incident from hundreds of triggered alarms.
Development of models that correlate infrastructure metrics with application performance (APM) data to predict user-experience degradation and pinpoint the underlying resource bottleneck.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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