ForesAIght
AI-assisted Precision Forecasting
You only see problems after users do
Most operators rely on monitoring and historical data to understand performance. But by the time an issue shows up, users have already felt it.
Existing approaches come with trade-offs. Models are often built per network element, which makes them hard to scale and maintain. And when predictions are off, they tend to underestimate demand, leading to service degradation.
Some approaches rely on crowdsourced data, which is often incomplete, noisy, and increasingly restricted by privacy regulations.
In practice, visibility is limited, and action comes too late.
See experience before it drops
See experience before it drops
1. Analyse multiple signals
Our ForesAIght engine can analyse multiple KPIs across the network at the same time.
2. Build a quality index
When quality of service is of interest, we translate traffic throughput measurements into an objective metric of user experience.
3. Predict what happens next
So you can see where quality is about to drop, before it does.
Built to scale. Designed for privacy.
ForesAIght uses a single neural model to cover an entire deployment. It doesn’t require separate models for each network element, which keeps it simple to deploy and maintain.
It also runs entirely on metadata, without collecting or processing user traffic. That means lower complexity, faster training, and no dependency on user-level data.
Alongside its predictions, the system provides confidence measures, giving operators clarity on how much to trust each insight.
From reactive to proactive
From reactive to proactive
When you can see how quality will change ahead of time, the way you run the network changes. You’re no longer reacting to issues. You’re staying ahead of them.
Capacity is adjusted before congestion builds. Handover decisions happen before users are impacted. Weak spots are addressed before they become visible problems.
The outcome is simple: a more consistent user experience and better use of network resources.