The Promise of Always-On
Maximizing Business Uptime and Productivity with AI/ML-Led Predictive Incident Management Solutions
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.
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.
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.
With this fully integrated visualization tool, you can easily create dashboards from the data in Momentum, Impulse, or any other external data sources.
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.
A UI driven engine to build complex business automation tasks by sequencing ML models, OCR, ICR, computer vision, and NLP models.
End to end solutions
Our solutions have a suite of software platforms that enable end-to-end enterprise automation without any third-part dependency.
A single platform with no-coding and UI-driven approach builds complex automation tasks rapidly.
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.
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
FMCG Retail Company
Predicting NBA of shoppers and recommending for cross-sale and up-sale
- Predicting next best action (NBA) of shoppers
- Building a recommendation engine for cross-sale and up-sale
- 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
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
IoT-based Smart Lighting
To reduce power consumption at the streetlights by decreasing the intensity when there is no traffic in the street.
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.
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
Momentum: Smart City Parking
2000 parking spaces with sensors; forecast volume (resource and revenue planning) and identify outliers for potential revenue leakage and fraud.
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.
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.