CloudlyNet
Turn every bit of Network data into intelligent decisions
Production-ready AI for communication operators, ISPs, and enterprise networks—built to predict, optimize, and self-heal networks securely at scale
Executive Summary
Introducing CloudlyNet, a RAN optimization platform that uses a Digital Twin with real UE data to reduce energy use, balance load, improve mobility, and close coverage gaps. Its network optimization models operate through automated SaaS workflows, while the AI Copilot delivers actionable insights, recommended actions, and a full run history. The result is lower operational costs, more stable network performance, faster optimization cycles, and seamless integration via APIs and multi-tenant controls.
Problem
- RAN energy is expensive and under-optimized
- Hotspots overload cells while nearby capacity stays unused
- Mobility creates ping-pongs and radio link failures
- Coverage gaps persist without closed-loop tuning
Our Solution
- Digital Twin simulates network behavior with UE data
- Network optimization models for Energy Saving, Load Balancing, MRO, and CCO
- SaaS workflows: baseline → train → infer → compare → export
- AI Copilot: explanations, recommended actions, run history
The Impact
- Reduce unnecessary power use with guardrails
- Stabilize QoE by reducing congestion and mobility issues
- Faster cycles with repeatable comparisons & audit trails
- Lower integration friction via APIs + multi-tenant controls
RAN inefficiencies that increase OPEX and degrade QoE
Everyday RAN inefficiencies create unnecessary OPEX pressure and directly impact subscriber experience.
Energy wasted
- Cells run high power even when traffic is low
- No safe switch-off without coverage guardrails
Congestion & imbalance
- Hotspots overload a few cells while neighbors idle
- p95 user throughput collapses under load skew
Mobility instability
- Ping-pongs and RLFs increase drops & retries
- More signaling overhead, poorer user experience
Coverage gaps
- Tilt/power misconfiguration creates weak zones
- Optimization is slow without simulation what-ifs
Platform Architecture
A Digital Twin + Network optimization models + Copilot, delivered as SaaS
Baseline + UE data in → safe, measurable actions out
Core flow
- Ingest a baseline (topology + config) and UE datasets
- Use the Digital Twin to predict RF behavior and evaluate changes
- Run rApps to propose actions, compare results, and export KPIs
- Repeat with guardrails (coverage, fairness, outage thresholds)
What you get
- Training + inference pipelines
- Model comparison (A/B) on the same dataset
- KPI dashboards and exportable reports
- Tenant isolation, RBAC, audit trails
Built as SaaS: Secure, Multi-tenant, Automation-first
Copilot turns KPI deltas into next actions
Platform capabilities
- Multi-tenant RBAC (tenant admin / tenant user)
- Contract-first APIs (OpenAPI) for integration
- Observability: logs, metrics, run history, audit trails
- Async training + inference workflows (job status, exports)
AI Copilot
- Explains KPI deltas and tradeoffs
- Suggests next experiments (datasets, guardrails)
- Helps debug failures (schema, inputs, job logs)
- Produces operator-ready action summaries
What’s Different vs Traditional OEM-only Tooling
Faster time-to-insight with measurable A/B evaluation
Traditional approach
- Vendor-specific workflows, slower experimentation
- Limited A/B evaluation across consistent datasets
- Automation fragmented across tools
- Hard to quantify ROI quickly across baselines
Cloudly Net AI
- SaaS workflows + multi-tenant governance
- Digital Twin evaluation + model comparison built-in
- Copilot reduces friction (debugging + recommendations)
- KPI export + cost mapping for ROI conversations
Let’s Run a Pilot on Your Network Data
KPI + ROI report in days, not weeks
- Pick one region + one baseline
- Upload UE mobility/traffic dataset (or generate synthetic first)
- Run ES/LB/MRO/CCO evaluations
- Get a KPI + ROI report