One platform to see every connection, resolve issues faster and optimize every investment — multi-technology, multi-vendor and multi-domain across core, transport and radio.
What customers feel and why CSPs face challenges are usually measured separately. ProciGenX connects the symptom to the cause.
Slow browsing, low throughput and video buffering; OTT latency and app response delays; dropped calls, poor VoLTE/VoNR quality, coverage and QoS issues — surfaced at the device, app or call.
Highly complex, multi-vendor networks with limited end-to-end observability — and no unified intelligence layer for consistent cross-domain optimization across core, transport and radio.
No direct measure of how each domain impacts end-user performance across core, transport and radio.
Limited E2E observability and no unified intelligence layer for consistent cross-domain optimization.
Experience and transmission health data remain siloed across structured and unstructured sources.
Current tools are largely rule-based, with limited capability to learn from network behaviour patterns.
Knowledge management is static and reactive, with limited self-learning to resolve recurring issues.
One unified, self-learning intelligence layer that connects customer experience to network performance.
One connected solution that turns customer experience into evidence-based, closed-loop network resolution.
Capture what customers feel — across app, device and call.
One shorthand from KPI/KQI to network problems, vendor-neutral.
Evidence-based root-cause analysis — not guesswork.
Closed-loop actions with workflow automation and auto-ticketing.
One language across core, transport and radio. Symptom-to-cause translation from KPI/KQI to network problems — always-on and multi-vendor neutral, with the same shorthand regardless of OEM.
A single command view across every domain and vendor. Always-on observability with anomaly & drift alerts, data-led diagnostics and closed-loop resolution with workflow automation and auto-ticketing.
A unified view that connects every layer to identify the true root cause and build a reusable library.
ProciGenX owns the complete analytics workload — all-vendor ready, multi-layer and multi-technology, with a self-learning spiral model.
Auto-discovery and E2E multi-domain observability.
Real-time AI/ML detection — reduce noise and false positives.
Evidence-based RCA across core, transport and radio.
Correlate traces, logs, alarms and KPIs for faster isolation.
Correlate alarms and clear codes to speed resolution.
Quantify impact before and after changes; continuous audit.
Closed-loop change management with auto-configuration.
Self-learning spiral model — solve once, reuse everywhere.
Service assurance, capacity forecasting and CR generation.
A layered pipeline that ingests any source, correlates it, and surfaces intelligent, closed-loop decisions.
Drive testing, crowdsourcing, probes, OSS counters, logs, API & streaming inputs.
Rule-book, workflow & template chaining across structured and unstructured data.
Postgres, MongoDB, Elasticsearch, Kafka and object storage for sanitised data.
ML, RAG, LLM, agentic AI pipeline with guard rails and evaluation.
Visualization, auto-configuration, auto-ticketing and workflow generation.
A selection of what ProciGenX handles — from trend analysis to agentic, closed-loop operations.
Identify performance trends and pinpoint worst-performing cells.
Auto-tune vendor and regular parameters from RCA findings.
Categorize causes across cells — interference, congestion, coverage.
End-to-end service topology with real-time multi-domain observability.
Correlate traces, logs, alarms and KPIs for faster problem isolation.
Identify high-utilization cells and predict future congestion.
Simulate changes before implementation and validate impact.
Detect → Diagnose → Recommend → Act → Validate, closed-loop.
Capacity right-sizing and cost optimization with consolidation insights.
Measured across live deployments — 800+ minutes saved per operating cycle, unified, automated and auditable.
| Task | Before → After | Saved |
|---|---|---|
| Anomaly Detection | 60 → 5–7 min | 91% |
| Root Cause Analysis | 600 → 60 min | 90% |
| Long-Term Analysis | 180 → <40 min | 78% |
| Radio Interference | 40 → <15 min | 62% |
| Outcome | Finding | Value |
|---|---|---|
| Recover — MW links unused | 12% of 4,345 deployed | 521 |
| De-risk — single-path links | Any single failure = outage | 556 |
| Clean up — ghost entries | 25% of 14k links | 3,500 |
| Automated actions applied | Power, IFLB & routing | 265+ |
Let ProciGenX audit every link, router and inventory record — and show you the CapEx, risk and efficiency gains you can capture.
Request a demo →