Telecommunications Application Catalogue

Enterprise AI applications for telecommunications operations, powered by the Semantic Ontology Layer.

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Data Plane Overview

The application catalogue draws from a unified data plane spanning BSS/OSS systems, CRM, and network telemetry sources:

graph TD
    subgraph Data Plane
        BSS["BSS / Billing System<br/><i>Subscribers, Billing, Rating, Product Catalog</i>"]
        CRM["Salesforce CRM<br/><i>Interactions, Sales Pipeline, Service Requests</i>"]
        OSS["OSS / Network Management<br/><i>Inventory, Faults, Performance, Configuration</i>"]
        CDR["CDR / Network Data Lake<br/><i>Call Records, Data Usage, Signaling, xDR</i>"]
        INTERACT["Customer Interactions<br/><i>Call Center, Chat, Email, Social Media</i>"]
        FIELD["Field & Tower Data<br/><i>Site Surveys, Maintenance Logs, Coverage Maps</i>"]
    end

    subgraph Semantic Ontology Layer
        ONT["Unified Telco Ontology<br/><i>Entity Resolution · Relationship Mapping · Business Rules</i>"]
    end

    subgraph Application Catalogue
        C360["Customer 360 & Churn"]
        NP["Network Performance"]
        RA["Revenue Assurance"]
        CEM["Customer Experience"]
        NC["Network Capacity"]
        DP["Dynamic Pricing"]
        FO["Field Operations"]
        REG["Regulatory Compliance"]
    end

    BSS --> ONT
    CRM --> ONT
    OSS --> ONT
    CDR --> ONT
    INTERACT --> ONT
    FIELD --> ONT

    ONT --> C360
    ONT --> NP
    ONT --> RA
    ONT --> CEM
    ONT --> NC
    ONT --> DP
    ONT --> FO
    ONT --> REG

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Data Plane Components

Structured Enterprise Systems

System	Examples	Role
BSS / Billing	Amdocs Revenue Manager, Ericsson BSCS, Netcracker, CSG	Subscriber master, billing cycles, rating, product catalog, order management, account hierarchy
CRM	Salesforce, Siebel, Microsoft Dynamics	Customer interactions, complaints, sales pipeline, retention campaigns, service requests
OSS / Network Management	Nokia NetAct, Ericsson ENM, Huawei iManager	Network inventory (cells, links, nodes), fault/alarm management, performance counters, configuration

Semi-Structured & Unstructured Sources

Source	Examples	Role
CDR / Network Data Lake	Mediation platforms, probe data, DPI	Call Detail Records, data session records, signaling traces, xDR (any Detail Record)
Customer Interactions	Genesys, NICE, Sprinklr, social APIs	Call center recordings, chat transcripts, email threads, social media mentions
Field & Tower Data	GIS systems, CMMS, drive test tools	Cell site surveys, tower maintenance logs, coverage maps, RF measurements

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Application Catalogue

P0 — Immediate ROI

Application	Primary Data Sources	ROI Signal	Time to Value
Customer 360 & Churn Prediction	BSS, CRM, CDR, Interactions	Reduce churn 15-25%, increase ARPU 8-12%	4-6 weeks
Network Performance & Anomaly Detection	OSS, CDR, Field Data	Reduce outages 30-40%, cut MTTR 50%	4-6 weeks

P1 — High Value

Application	Primary Data Sources	ROI Signal	Time to Value
Revenue Assurance & Fraud Management	BSS, CDR, OSS	Recover 1-3% of revenue leakage, reduce fraud losses 40-60%	6-8 weeks
Customer Experience Management	CRM, CDR, Interactions, OSS	Improve NPS 10-15 points, reduce complaints 25-35%	6-8 weeks

P2 — Strategic Value

Application	Primary Data Sources	ROI Signal	Time to Value
Network Capacity Planning & Optimization	OSS, CDR, Field Data	Defer CAPEX 15-20%, improve spectral efficiency 10-15%	8-10 weeks
Dynamic Pricing & Offer Management	BSS, CRM, CDR	Increase ARPU 5-10%, improve conversion 20-30%	8-10 weeks

P3 — Operational Excellence

Application	Primary Data Sources	ROI Signal	Time to Value
Field Operations Intelligence	Field Data, OSS, CRM	Reduce truck rolls 20-30%, improve first-fix rate 15-25%	10-12 weeks
Regulatory & Compliance Automation	All Systems, CDR, Field Data	70% reporting effort reduction, zero compliance penalties	10-12 weeks

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Ontology Entity Map

Key business entities resolved across systems by the Semantic Ontology Layer:

Business Entity	BSS / Billing	CRM	OSS / Network Mgmt
Subscriber	Customer Account / MSISDN	Account / Contact	—
Service	Product Instance / Subscription	Service Request	Service Path / Circuit
Network Element	—	—	Cell Site / Node / Link
Usage Event	Rated CDR / Billing Record	—	Raw CDR / xDR
Trouble Ticket	—	Case / Complaint	Alarm / Fault Ticket
Site / Location	Service Address	Customer Address	Cell Site / Tower / PoP

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Architecture Pattern

Every application in this catalogue follows a consistent execution pattern:

flowchart LR
    A["Ontology Layer<br/><i>Resolved Entities</i>"] --> B["AI Workflow Engine<br/><i>LLM + Business Rules</i>"]
    B --> C["Dashboard & Alerts<br/><i>Real-time Visualization</i>"]
    B --> D["Action Layer<br/><i>Recommendations · Automation</i>"]

1. Data Ingestion — Ontology layer resolves entities across BSS, CRM, OSS, and unstructured sources (CDR correlation, subscriber-to-network mapping, ticket-to-alarm linkage)
2. AI Workflow Execution — LLM-powered pipelines apply business logic, anomaly detection, predictive models, and network analytics
3. Presentation — Dashboards surface KPIs; alerts trigger on threshold breaches, SLA violations, and network degradation
4. Action — Recommendations feed back into source systems (proactive retention offers, automated fault tickets, dynamic plan adjustments)

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