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Cipherwave: Building an Agentic Customer Intelligence Platform for the Cognitive ISP
Cipherwave partnered with Pygio to unify fragmented customer, finance, CRM, and help desk data into a scalable, AI-ready intelligence platform.
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Business Requirements
Cipherwave needed to unify fragmented customer, billing, CRM, and helpdesk data into a single, trusted source of truth. Data was manually exported into Excel, contained duplicates, and lacked consistent identifiers - limiting visibility and strategic decision-making.
The project faced several key challenges:
The project faced several key challenges:
- Consolidating multiple disconnected systems into one semantic model·Resolving duplicate customer records and inconsistent IDs
- Replacing manual retention and reporting processes
- Enabling predictive churn and revenue intelligence
- Enabling AI agents to reason over customer lifecycle data and generate operational insights
Solution
Pygio designed and implemented a Snowflake-powered Customer 360 data foundation, consolidating disparate data sources into a structured semantic layer.
Fuzzy matching logic was applied to resolve duplicate records and standardize customer identities across systems.
The solution enabled real-time churn analysis, revenue assurance visibility, and automated operational reporting.
A scalable, AI-ready architecture was established to support advanced analytics, secure role-based access, and future monetizable data products.
The semantic data layer was intentionally designed to support AI agents capable of analyzing customer behaviour, identifying risk signals, and generating operational insights in real time.
Fuzzy matching logic was applied to resolve duplicate records and standardize customer identities across systems.
The solution enabled real-time churn analysis, revenue assurance visibility, and automated operational reporting.
A scalable, AI-ready architecture was established to support advanced analytics, secure role-based access, and future monetizable data products.
The semantic data layer was intentionally designed to support AI agents capable of analyzing customer behaviour, identifying risk signals, and generating operational insights in real time.
Manual to Automated Intelligence
Retention reporting
Sales Reporting to Agent-Driven Insight
Churn analysis
Fragmented Data to Unified Customer 360
Unified customer data
Excel Exports to AI-Ready Data Platform
Experts
Business Requirements
Our core focus in the first phase was to create a unified data foundation that consolidates fragmented customer, billing, CRM, and helpdesk systems into a single semantic Customer 360 view.
The goal of this phase was to:
- Ingest and standardize data from multiple disconnected source systems
- Clean and reconcile duplicate customer records across platforms
- Apply fuzzy matching logic to resolve inconsistent IDs and naming variations
- Design a structured semantic model separating facts and dimensions
- Enable churn analysis and revenue visibility across the customer lifecycle
- Replace manual Excel-based retention reporting processes
- Ensure the architecture was scalable and adaptable for future system integrations
- Minimize incorrect joins and false matches in customer records
- Provide operational teams with a trusted, unified source of truth
This phase established a reliable, AI-ready data backbone - transforming fragmented datasets into structured intelligence.
Intelligence Layer & Operational Enablement
Phase II focused on operationalizing the Customer 360 foundation by enabling agentic intelligence capable of interpreting data, identifying risk patterns, and generating actionable operational insight.
Specifically, the goals of this phase were to:
- Enable real-time churn risk identification
- Introduce revenue assurance visibility (“as-sold vs as-used vs as-billed”)
- Provide role-based access control (RBAC) for secure user access
- Deliver structured reporting for executive and operational decision-making
- Replace static flat-file analysis with dynamic, queryable data views
- Lay the groundwork for AI-driven anomaly detection and automation
- Prepare the platform for future monetizable data products for resellers
This platform gives leadership near real-time visibility into customer performance, churn drivers, and revenue trends - turning data from a reporting burden into a strategic asset.
Agentic Use Cases Enabled
The unified Customer 360 intelligence layer enables several emerging AI-driven capabilities:
Churn Intelligence Agents
- Identify customers showing early cancellation signals based on service performance, contract status, and support activity.
Revenue Assurance Agents
- Continuously analyze discrepancies between “as-sold”, “as-used”, and “as-billed” revenue streams.
Operational Monitoring Agents
- Detect anomalies in customer behaviour, service usage, or operational performance in near real time.
Project Overview I
Project Overview II
Agentic Use Cases Enabled
Tech Stack we work with
Richard Zoutendyk
CEO, Cipherwave
Pygio has been instrumental in building our 'Unified Intelligence Backbone.' By implementing a production-grade Snowflake platform, they successfully unified our fragmented data across Billing, CRM, and IoT Metadata. Their work on our 'As-sold, As-used, As-billed' reconciliation model has been a game-changer for revenue protection. If you need a partner to build an AI-ready data foundation with rigorous governance, I highly recommend Pygio.
Client
Cipherwave
Industry
ISP/Connectivity, Cloud &Security, Telco
Project start
2025 - Present
Country
RSA
Project focus
Customer 360 & Data Intelligence
Services
#DataStrategy
#DataEngineering
#Customer360
#RevenueAssurance
#AIAnalytics #AgenticAI
#DataEngineering
#Customer360
#RevenueAssurance
#AIAnalytics #AgenticAI