Maveric – AI-Native RAN Optimization
Optimize your mobile network with confidence.
Maveric is the open-source, AI-native digital twin platform that helps telecom operators simulate, test, and optimize their RAN before making live changes.
Why Maveric?
Fewer dropped calls
with smarter handover tuning
Better coverage
through dynamic tilt and azimuth optimization
Balanced capacity
across towers during peak demand
Lower energy costs
by intelligently shutting down idle sectors
Safer experimentation
with privacy-first digital twin simulations
Key Capabilities
Mobility Robustness Optimization (MRO)
Reduce RLFs and ping-pong handovers by simulating and tuning parameters like hysteresis and TTT.
Coverage & Capacity Optimization (CCO)
Close coverage gaps and ease congestion by optimizing antenna tilt, azimuth, and transmit power.
Energy Savings
Cut power usage during off-peak hours with ML-driven control of cell activity and tilt.
Load Balancing
Distribute traffic dynamically using predictive, RL-based tilt adjustments to maintain high QoS.
Traffic Load Generation
Generate realistic UE traffic traces for simulation, planning, and ML model training.
How Trials Work
Baseline
Share your network data (topology + Rx power or mobility traces).
Simulate
Maveric runs optimizations in a digital twin.
Validate
Apply best candidates to a few live sites.
Measure
Receive baseline vs optimized reports and OpEx savings
Your Data, Fully Protected
Secure upload via SFTP or API
On-prem, cloud, or hybrid deployments
Real UE data never exposed—simulations run on encrypted or synthetic data
Built for You
Open-source
(see GitHub) - transparent, extensible
Modular
use one app or the full stack
Compatible
works with Open RAN and traditional networks
Scalable
from edge sites to national deployments
Cloudly + LF Connectivity: Meet Maveric v1.0 - The AI-Ready Digital Twin for Smarter RANs
White Paper
Maveric – AI-Native Optimization for the Modern Network
Maveric is an AI-native and ML-driven platform that transforms network management by continuously learning from live data and automatically optimizing settings. It improves performance- reduces dropped calls, balances capacity, and cuts operational costs, all while enhancing user experience.