Customer Operations Application Catalogue¶

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

Data Plane Overview¶

The application catalogue draws from a unified data plane spanning structured enterprise systems, contact center platforms, and unstructured data sources:

graph TD
    subgraph Data Plane
        SF["Salesforce CRM<br/><i>Cases, Accounts, Contacts, Knowledge</i>"]
        OE["Oracle ERP<br/><i>Billing, AR/AP, Payments, Disputes</i>"]
        SAP["SAP<br/><i>Service Orders, Field Dispatch, SLAs</i>"]
        WXCC["Webex Contact Center<br/><i>Calls, Queues, Agent States, IVR</i>"]
        GD["Google Drive<br/><i>SOPs, Knowledge Base, Training</i>"]
        SL["Social Listening<br/><i>Customer Sentiment, NPS, Feedback</i>"]
    end

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

    subgraph Application Catalogue
        AIR["Autonomous Issue Resolution"]
        UOH["Unified Operations Hub"]
        PAA["Proactive Agent Assist"]
    end

    SF --> ONT
    OE --> ONT
    SAP --> ONT
    WXCC --> ONT
    GD --> ONT
    SL --> ONT

    ONT --> AIR
    ONT --> UOH
    ONT --> PAA

Application Catalogue¶

P0 — Immediate ROI¶

Application	Primary Data Sources	ROI Signal	Time to Value
Autonomous Issue Resolution Engine	Salesforce, WXCC, Oracle ERP, SAP	Reduce avg resolution time 40-60%, cut escalations 30-45%	4-6 weeks

P1 — High Value¶

Application	Primary Data Sources	ROI Signal	Time to Value
Unified Operations Hub	Salesforce, Oracle ERP, WXCC, SAP	Reduce cross-system handoff time 50-70%, cut billing errors 25-35%	6-8 weeks

P2 — Strategic Value¶

Application	Primary Data Sources	ROI Signal	Time to Value
Proactive Agent Assist Copilot	Salesforce, WXCC, Oracle ERP, Social Listening	Increase first-call resolution 15-25%, uplift CSAT 8-12 points	8-10 weeks

Ontology Entity Map¶

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

Business Entity	Salesforce CRM	Oracle ERP	SAP	Webex Contact Center
Customer	Account / Contact	AR_Customer	Business Partner	Caller Profile
Case / Ticket	Case / Case_Comment	—	Service_Order	Call_Record
Billing	—	AR_Invoice / AR_Payment	—	—
Dispute	Case (type=Billing)	Billing_Dispute / Credit_Note	—	—
Interaction	Activity / Email	—	—	Call_Record / Chat_Transcript
Agent	User (Service Agent)	—	—	Agent_State / Agent_Metrics
Knowledge	Knowledge_Article	—	—	—
SLA	Entitlement / Milestone	—	SLA_Record	Queue SLA

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>"]

Data Ingestion — Ontology layer resolves entities across Salesforce, Oracle ERP, SAP, WXCC, and unstructured sources
AI Workflow Execution — LLM-powered pipelines apply business logic, anomaly detection, and predictive models
Presentation — Dashboards surface KPIs; alerts trigger on threshold breaches
Action — Recommendations feed back into source systems or trigger automated workflows

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