Artificial Intelligence Platform for Office 365 Management
Cloudficient’s Artificial Intelligence and Anomaly Detection Engine is something special.
We use artificial intelligence, machine learning and anomaly detection to transform your data into useable information. The platform can then turn that information into actions with precision and confidence.
Machine learning, no supervision necessary
Our AI platform is the beating heart of all of the modules that make up the cloudficient platform.
It models the complexity of your Office 365 hybrid or cloud environment, and spots anything unusual as it is happening.
Its ability to learn normal behaviour and complexities of your Office 365 environment is what makes the system really stand out. The longer you run it, the better it knows your system.
It becomes better at spotting anomalies, and more importantly better at filtering out those false positives that take up so much of your time and attention!
Cloudficient’s AI engine can deliver:
- Analysing the past and monitoring the present, and then using that to forecast the future needs of your organization
- Combining data from multiple sources across silos to produce valuable, actionable information
- Spotting changes in key metrics, and gathering more information on outliers without having to be told
- Fewer false positives, and a more reliable monitoring system
- Awareness and prediction of environmental pressures at certain times, even on complex cycles
- Spot spikes or drops in application requests, and retrieve related data automatically
- Capacity planning based on increasingly solid predictions
- Real, effective SLA monitoring
Putting this combined AI, Machine Learning and Anomaly Detection system at the core of our service offering makes nearly everything that you do in the cloud more efficient.
With more information available to you, you’ll make better decisions. With most of that information already gathered for you, you can make those decisions faster.
Many of these decisions can be automated as well, easing pressure on support centre staff dramatically, even at lower staffing levels.