This is like giving Dhiraagu’s mobile network a smart autopilot: software watches how the network behaves across all the islands and automatically fine‑tunes it so customers get faster, more reliable service without engineers having to tweak everything by hand.
Manual radio and transport network planning/optimization is slow, error‑prone, and cannot keep up with demand spikes, tourism seasonality, and coverage challenges across the Maldives’ dispersed islands. The AI solution automates performance optimization to improve coverage and quality of service while reducing engineering effort and OPEX.
Infovista’s moat is likely a combination of long‑standing RF planning and network analytics IP, historical performance datasets from multiple operators, and tight integration into Dhiraagu’s OSS/BSS and RAN vendor stack; once embedded into network operations, switching costs are high due to custom models, integrations, and operator‑specific tuning.
Hybrid
Time-Series DB
High (Custom Models/Infra)
Scalability is constrained by the volume and velocity of network telemetry (per-cell KPIs, call traces), the need for near‑real‑time inference across thousands of sites, and integration with heterogeneous vendor equipment while keeping data residency and security requirements in check.
Early Majority
Compared with generic AI operations tools, this deployment is focused on telecom‑grade RF/network optimization in a geographically fragmented island environment, requiring robust time‑series analytics on noisy radio KPIs, automation of domain‑specific actions (e.g., parameter tuning, load balancing, interference mitigation), and deep integration with Dhiraagu’s existing OSS/RAN ecosystem.