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Technology

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Maximizing Business Uptime and Productivity with AI/ML-Led Predictive Incident Management

Discover more about CriticalRiver’s powerful solution, which enables businesses to enhance and support intelligent decisions for faster, more accurate incident resolution, higher response quality, and cost efficiency.

  • Intelligent Capacity Management

    Enterprises can now optimize cloud infrastructure with data-driven recommendations on hybrid/multi-cloud resource sizing. With historical data insights and a ML engine, IT leaders can get a holistic view of their hybrid cloud resources.

  • Root Cause Analysis

    Intelligent Root Cause Analysis can be used to identify incident clusters and probable resolutions. Clustering related incidents helps with the identification of significant incident drivers and the prevention of recurrence.

  • Intelligent Workflow Automation

    Organizations can use validated and approved recommendations for automating workflows that support the flow of information in near real-time. They can also get access to precise automation triggers.

Intelligent Business Decision Making

While IT issues can occur without a warning, a planned, proactive approach can assist to significantly reduce their effects, if not completely eradicate them. Also, post-deployment incidents are significantly decreased when possible issues are identified early on in a change request. The benefits include shorter incident response times, higher data quality, and much higher ITSM maturity.

Decision Intelligence

Visualize AI/ML outcomes, monitor KPIs, build powerful dashboards, automate insight delivery and create alerts for what matters the most for your business with Inset BI, a web-based decision support tool that gives visibility to your business.

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Business Insights Visualization

We use a web-based visualization engine to display your business insights in the form of dashboards consisting of graphs and charts.

Create Dashboards

With this fully integrated visualization tool, you can easily create dashboards from the data in Momentum, Impulse, or any other external data sources.

Access Control

Control who sees what by role-based access control feature.

Automation of business processes

Process automation is the application of advanced machine learning skills to streamline processes and workflows in order to make work simpler and more satisfying.

And to finally guarantee happier agents and even happier IT service desk users at a reduced cost.

Process Automation

A UI driven engine to build complex business automation tasks by sequencing ML models, OCR, ICR, computer vision, and NLP models.

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End to end solutions

Our solutions have a suite of software platforms that enable end-to-end enterprise automation without any third-part dependency.

Non-coded

A single platform with no-coding and UI-driven approach builds complex automation tasks rapidly.

Cost saving

No third-party dependency saves on license costs and avoids integration complexity.

Easy to use

No specialized skill is needed to work with the platform.

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Data science platform

Our solutions offer an enterprise-scale data science platform to train computer vision, machine learning, and NLP models that enable intelligent automation.

Our Success Stories

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FMCG Retail Company

Predicting NBA of shoppers and recommending for cross-sale and up-sale

Objectives
  • Predicting next best action (NBA) of shoppers
  • Building a recommendation engine for cross-sale and up-sale

Solutions

  • Based on the shopper’s interest in the product, the algorithm predicts the shopper’s NBA
  • Recommend the products for up-sale as well as cross-sale based on the NBA prediction outcome algorithm
  • Predicts how the up-sale and cross-sale products will be arranged in a grid on the digital screen to draw the shoppers’ attention
  • Small grids and others are 3×4 (12) small grids, so the display prediction algorithm must deal with an additional level of complexity to capture the maximum attention of the shoppers

Benefits

Our solutions are under observation in one of the largest shopping mall chains in the United States, which has around 1600 stores all over the country

Expected growth in the top line

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IoT-based Smart Lighting

Challenge

To reduce power consumption at the streetlights by decreasing the intensity when there is no traffic in the street.

Solution

Our solution collects data from parking sensors in real time, CCTV video and images, and data from business applications.

Used data science in building fact-based descriptive and prescriptive analytics to provide better insight and help in saving electricity based on real-time sensor data from light sensors, motion sensors, and live weather data.

Result

Have achieved the following few important analytics: Identify and predict traffic volume on the street

  • Dimming and brightening of streetlamps based on pre-determined, identified triggers
  • More than 15% of power consumption was reduced
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Momentum: Smart City Parking

Challenge

2000 parking spaces with sensors; forecast volume (resource and revenue planning) and identify outliers for potential revenue leakage and fraud.

Solution

Our solution aggregated stream-based real-time parking sensor data, CCTV video and images, and business application data. Data science was used in the development of fact-based descriptive and prescriptive analytics to provide better insight and aid in the prevention of fraud using sensor and CCTV video and image analytics.

Result

We have achieved the following few important analytics:

  • Forecasting parking occupancy rates hourly, daily, weekly, and monthly, as well as for any specific hours on any given day, week, or month, and so on
  • Outlier detection, such as cars coming in 15–16 times per day, long-term parking, etc.

Examples of our Proof of Concept