It’s the most complete and advanced machine learning platform to monitor and improve the experience of wi-fi and broadband customers while reducing costs at scale.
We offer a suite of tools such as smart data collection and powerful deep down analysis to offer the best available solutions to both operators and customers.
The complete telemetry and end-to-end monitoring platform based on Machine Learning. It monitors information from customers’ household networks (Broadband and Wi-Fi), access networks and transportation networks. An embedded agent in the customers’ CPE collects data at any frequency and time granularity, automatically or on demand. The Machine Learning algorithms process the data, triage issues and deliver real-time diagnostics and action solutions to repair problems.
Monitoring platform based on Machine Learning for Wi-Fi networks. It keeps track of connected devices, network coverage and other indicators, allowing optimization while reducing customers’ Wi-Fi problems. Beegol’s Machine Learning algorithms process data, triage issues and deliver diagnostics and solutions to fix problems. Beegol Light does not require an embedded agent in the CPE.
Beegol Machine Learning algorithms’ servers ingest the data collected by the Operator’s existing tools, process the information, make a triage for issues and deliver the diagnostics and solutionss suggested to solve the problems.
It completely manages and monitors customers’ Wi-Fi Mesh network. Compatible with EasyMesh, Qualcomm Carrier Son and PrplMesh technologies.
Most telemetry programs run TR-181 data collection commands inside the CPE at regular intervals. They have a data path. However, Beegol has both a data path and a control path.
The control path allows for changes to the data collection scope and granularity, including real-time adjustments. For example, Beegol can change the latency measurement from a minute time interval to a second interval whenever potential transport congestion is detected. It can also collect data from all clients in a building to detect MDU failures. The control path provides the flexibility to gather data at any time and anywhere in the network.
We combine our knowledge in advanced technology with extensive business experience
| Challenge | Our Solutions | ||||
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Suggested order | High complexity to determine the product mix and quantity to offer to each client at each interaction
| Machine Learning model to calculate Suggested order including
Other business elements considered
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Route-to-market | High complexity to define mode and frequency and to serve each client:
| Analytical model to define optimal service level according to desired objective, e.g:
Simulator to assess different scenarios. Multiple variables used, for example:
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Promotional optimization |
| Analytical model to measure Return on Investment by product/channel/region; Optimization model to allocate promotional budget considering
Calculation of price elasticity and price point to maximize sales |
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Insight generation machine |
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