The Ultimate AIOps for Network Operations that keeps you out of trouble

As a seasoned Ninja Warrior in network engineering, you're no stranger to tools of the trade. But adding another to your arsenal? Doubtful.

Yet, behold SliceUp, a true game-changer.

SliceUp Ninja on fire, the ultimate Network AIops Weapon

NOC and IT Operations teams can now enjoy improved infrastructure reliability and employee productivity with SliceUp AIOps for Network Operations platform. Explore the key features below.

60%
MTTR
Reduction
60%
Boost in
NOC Productivity
20%
Reduction in
# of Issues

SliceUp AIOps Solution - Artificial Intelligence for IT Operations - automates log and network data correlation from variety of monitoring tools and leverages advanced Machine Learning algorithms to do predictive analytics. By learning patterns to establish the baseline of network behavior and an IT Infrastructure SliceUp can quickly pick up on future anomalies. It predicts performance issues before they affect critical services and operations. Moreover, it provides Network Operations and IT Teams with problem solving options, and in some cases uses automation to enable self healing infrastructure.

Agentless, Intelligent Data Ingestion

Flexible Data Ingestion Through APIs. SliceUp is an agentless tool that ingests unstructured and structured data in real-time via APIs. It requires zero human intervention to parse and onboard big data supporting various data sources including log data, SNMP, Infinistream, etc.

Intelligent Log Parsing Automation. SliceUp automates the parsing of various logs (Syslog, JSON, cloud logs, etc) in real-time, even the rare custom ones, and extracting features into the Machine Learning pipeline. This enables anomaly detection of rare long tail issues.

Automated Event Correlation. SliceUp combines relevant data from various systems. This high-fidelity contextualize data enables proactive rapid root cause analysis to fix problems before they affect critical services.

Gif animation showing a SliceUp dashboard showing how automated log data parsing works.

Log-based Artificial Intelligence

AI Driven Anomaly Detection of Long Tail Issues: SliceUp aims to remove blind spots by identifying long-tail issues that IT organizations didn't even know about. It uses several Machine Learning models to detect incidents early in real-time log data streams.

Noise Reduction with Criticality Rating: SliceUp uses a Machine Learning meta model to rank future issues and highlight critical ones. It provides transparency by showing the factors that influence each anomaly's rating, such as scarcity or negative sentiment in logs.

High Accuracy with Human-On-The-Loop: Human-on-the-loop component allows users to provide feedback when they want to adjust the assigned priority or category of system alerts. This ensures that future evaluations better reflect the user's IT environment.

Gif animation showing a SliceUp dashboard showing Log-based Artificial Intelligence. SliceUp aims to remove blind spots by identifying long-tail issues that operations teams didn't even know about. It uses several Machine Learning models to detect incidents early in real-time log data streams.

Lower MTTI* and Improved End-User Experience

No More Finger Pointing: SliceUp helps to reduce *Mean Time To Innocence by looking at Latency Analysis between different sites and identifying quickly whether it's a network or applications related problem, so you can skip finger pointing and start fixing.

Improved End User Experience: SliceUp predicts Latency and Interface Utilization issues that may affect End User Experience. By getting ahead of them, SliceUp minimizes delays and maximizes performance, boosting end user satisfaction.

Faster Root Cause Analysis: Autocorrelation of different data sources, and other related anomalies that happened in the same time window allows users to drill down and get to the root cause of an issue faster - further reducing the time to remediation of issues. 

Gif animation showing a SliceUp dashboard with Latency Analysis. SliceUp helps to reduce *Mean Time Io Innocence by looking at Latency Analysis between different sites and identifying quickly whether it's a network or applications related problem, so you can skip finger pointing and start fixing.

Improved Employee Productivity through Automation

Auto-Detect And Fix Routine Issues: SliceUp AIOps automatically detects recurring issues and streamlines the workflow for auto-fixes, saving time and effort. Examples of recurring issues include duplex mismatch, port security, and certificate expiration.

Streamlined Incident Management: SliceUp AIOps automatically opens up a ticket when the issue is detected, links a user created script to the log template, and then executes the script. When the script execution is verified, the ticket is closed automatically.

Operations Teams Supervision: IT Teams have full control over the resolution process.They can choose to either oversee and approve scripted fixes or, once they feel confident in the AIOps tool, let it fix problems automatically. All with zero impact on business service.

Gif animation showing a SliceUp dashboard showing the auto remediation of routine issues workflow.

AI for Network Performance “What If Analysis”

Auto-discovered Network Topology. SliceUp integrates with existing SNMP data collection systems to auto-discover your network topology. By overlaying key metrics on an always current topology, network administrators can easily find where the future problems are.

Plan Ahead - Get Proactive: SliceUp allows you to withstand the next big product launch or a black swan event. Proactively mitigate the risk based on modeled scenarios and take the appropriate action - add additional links, increase bandwidth, or re-route traffic. 

Identify Resources Needed. SliceUp's "what if calculator" models different future scenarios to identify bottle necks and see which interfaces may become over utilized if traffic increases, adjusted for risk. Examples of metrics measured: Interface Utilization, TCAM, Memory.

Gif animation showing a SliceUp dashboard with "what if Calculator". AI for Network Performance “What If Analysis” to model future scenarios to identify bottle necks and see which interfaces may become over utilized if traffic increases, adjusted for risk. Examples of metrics measured: Interface Utilization, TCAM, Memory.

Why AI-Ops? And why SliceUp? 

SliceUp, the Ultimate AIOps platform, empowers network operations teams to feel invincible. With its comprehensive set of features, SliceUp revolutionizes network operations and enhances employee productivity.

By harnessing the power of Artificial Intelligence for IT Operations, SliceUp automates log and network data correlation, predicts network issues, and enables self-healing infrastructure by providing remediation steps and automated issue resolution.


Its agentless and intelligent data ingestion capabilities effortlessly handle large volumes and a wide range of data sources, while automated event correlation helps rapidly identify root cause analysis. SliceUp's log-based AI drives anomaly detection of long-tail issues, reduces noise with criticality rating, and leverages human-on-the-loop for accurate evaluations. Moreover, SliceUp lowers Mean Time to Innocence (MTTI), improves end-user experience, and speeds up root cause analysis. With automation at its core, SliceUp auto-detects and fixes routine issues, streamlines incident management, and empowers IT staff with control over the resolution process. Additionally, SliceUp's "What If Analysis" feature enables proactive planning, risk mitigation, and resource identification.

Choose SliceUp for a transformative AIOps experience to prevent outages, improve and take operational efficiency of IT Organizations to new heights.

Ready for The Ultimate Quest with SliceUp Network AIOps Solution?

Talk to us about your use cases

FAQ:

How is it deployed? SliceUp is a Kubernetes-based, scalable deployment that can be installed in your Private Cloud or On-Prem. This way, your data never has to leave your environment. With our fully automated data preparation and parsing, it is designed to work well even in an air-gapped environment.

What are your existing out-of-the-box integrations? Some of the existing integrations include Syslog, Kafka, NetScout, Ansible, ServiceNow, SolarWinds, NNMI, and others. Contact us for a full list of integrations, or to discuss a custom integration to meet your needs.

How much historical data do I need for it to work? The system needs a minimum of a week's worth of historical data, but we recommend 30 days’ worth of data for it to establish the baseline for your environment accurately.