CloudFabrix Enables your Autonomous Enterprise Journey

Data-centric AIOps Platform
for Hybrid Deployments

Powered by Robotic Data Automation Fabric (RDAF)

Enabling the Autonomous Enterprise!

CloudFabrix was founded on a deep desire to enable Autonomous Enterprises. As we interviewed several big and small enterprises, one thing became very apparent. As Digital businesses were becoming more complex and abstract, it was impossible for traditional data management disciplines and frameworks to meet these requirements. As we dug deeper, 3 building blocks emerged as key pillars for embarking on an autonomous enterprise journey – the enterprise needed to adopt

  1. Data-First
  2. AI-First and
  3. Automate Everywhere strategy

What is an Autonomous Enterprise?

What is an Autonomous Enterprise
Alert Noise Reduction
  • Too many domain specific Observability tools; data integration with these tools
  • Observability tools lack real time topology and operational context
  • Lots of Alert Noise

About 40% of IT organizations see over a million event alerts a day, with 11% receiving over 10 million alerts a day; Average cost of Alert Noise - $1.27M per year

Root Cause Analysis
  • Accurate Alert Correlation needs cross domain context enrichment with full stack topology and operational insights
  • Need multiple levels of correlation techniques - AI/ML, Stack, Time, Attribute, Predictive based to derive accurate RCA

Average MTTR > 7 Hrs., Average cost of single service outage - $400K; Facebook 5 hour outage costed $65M and 4.8% valuation loss

Incident Remediation
  • Needs automated incident enrichment with NLP for Level 0 prioritization and resolution
  • Needs Recommendation Engine to recommend from Knowledge graph
  • Needs Incidents Routing and notifications to right stakeholders

Too many tickets to handle surveys show 82% of IT incidents are not actionable and each incident costs $10,700

Business & ITOps
Accelerate Revenue, Reduce Operational Costs
  • Innovate Faster, with less time spent on Operational Issues
  • Correlate Business and IT issues, for customer 360
  • Reduce IT Operational costs by 50%, with less incidents and faster resolution
SecOps & NOC
Reduce Risk
  • Bring velocity to BusDevOps and CI/CD pipelines; Accelerate Digital Transformation
  • Implement Data-First, AI-First & Automate Everywhere strategies
  • Improve Time to Market and Time to Insights
Improve Productivity
  • Improve Service Uptime; Predict and Prevent Outages and Security Breaches
  • Improve Full Stack Insights, Business Continuity
  • Improve Customer Digital Experience and NPS Score

AIOps powered by
Data Observability Pipelines

AIOps as a Service

Powered by Robotic Data Automation Fabric (RDAF) - A Low Code Analytics Platform for Data In Motion

Descriptive Analytics

Enables to take an inventory of all your IT assets, application assets and business assets to build the application dependency mapping.

  • Asset Intelligence Analytics
  • Data Integration and Ingestion
  • Seamless consolidation of MELT data into Observability Data Lake or SnowFlake
  • Enrichment using Application Dependency Mapping, GeoIP lookups, Security feeds.
Descriptive Analytics

Proactive Analytics

Data Intelligence and Automation becomes part of the information systems

  • Correlate Business to Service and Ops uptime
  • Customer 360
  • Capacity and Resource Orchestrations
  • Self healing and Self Learning
  • Visual customizable dashboards
  • Radar Views and Incident Room
Cognitive Analytics

Prescriptive Analytics

Once Root cause, anomaly or what-if analysis what action can be taken to remediate. This is done using ServiceOps for Incident creation, management and resolution or notifications

  • Incident Enrichment and Classification using NLP techniques
  • Bidirectional integration with Automation solutions
  • IT Automation, DevOps Automation, Process Automation, Security Automation, Data Automation
  • Alert Notification & Collaboration
Prescriptive Analytics

Predictive Analytics

Root cause and regression analysis, using Explainable AI/ML pipelines to setup dynamic baselining and identifying anomalies, trends and doing what-if analysis.

  • Alert and Log Correlation
  • Root Cause Analysis
  • Dynamic Baselining
  • Anomaly detection
  • Forecasting
Predictive Analytics

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