Telecom

Real-time telecom intelligence — across Marketing, Care, Network, Operations, and Finance

Unify subscriber, network, usage, revenue, and operational data into a correlated real-time analytics foundation, then let domain AI assistants deliver sub-second answers in plain language.

The telecom reality

Telecom data is extreme: operators can generate billions of daily events across probes, DPI, OSS counters, CRM, billing, and more. Generic cloud/streaming stacks often struggle with cost, latency, rigid models, and insights trapped in silos, leaving business and operations disconnected.

What we deliver for telcos

A unified Data + AI Fabric for telecom

    • Real-time Data Fabric to ingest and correlate multi-domain telecom data (network + subscriber + usage + revenue + operations).
    • iCrunch RAS (Real-Time Analytics Storage) for sub-second analytics at extreme scale.
    • iCrunch Pulse: domain-specialized AI assistants that understand telecom KPIs, terms, and workflows.

iCrunch Pulse: AI assistants built for telecom teams

Pulse Market, Pulse Care, Pulse Network, Pulse Operations, Pulse Finance — each assistant is specialized to your telco context and operates on top of a correlated, denormalized RAS schema for fast, consistent results

How it works

      1. Ask a question in natural language
      2. A private, fine-tuned model understands telecom intent and KPIs
      3. Optimized SQL is generated for the correlated RAS schema
      4. Execution stays inside your infrastructure (no data leaves)
      5. Results return instantly as KPIs, tables, charts, and explanations

Why it’s different from “NLQ on a data warehouse”

Generic NLQ/BI tools mostly generate SQL but still rely on slow, fragmented, normalized backends with heavy joins and inconsistent KPI definitions across teams. Pulse combines the interface and the performance foundation (Fabric + RAS). 

 

The KEY challenge: Cross-domain RCA needs the right foundation (CMTV)

Automated RCA (Root Cause Analysis) isn’t “magic AI.” It starts with deterministic cross-domain correlation.
The CMTV (Cross Domain Meta Troubleshooting Vector) provides a unified vectorization and reconciliation foundation to correlate Radio, Access/Core, Transport, and Subscriber context — enabling fast, explainable root cause analysis at scale. 

Two-step approach

      • Step 1 (mandatory): Build the CMTV foundation — unified data, deterministic correlation, stable RCA baseline independent of AI.
      • Step 2 (optional): Add AI on top — earlier detection, noise reduction, incident grouping, configuration risk scoring, higher diagnostic confidence.

Measured gains (proof points)

      • 50–80% MTTR reduction
      • Detect anomalies 30–90 minutes earlier
      • 50–90% alarm noise reduction
      • 20–50% fewer escalations