How a Satellite Telco Modernized Its Mission-Critical Billing Platform Without Disrupting Revenue Operations
A leading provider of satellite and telecom services partnered with Galent to modernize its mission-critical billing and invoicing platform central to its revenue operations.Built on a legacy, monolithic architecture, the platform had become increasingly complex to maintain, scale, and evolve. Aging technologies tightly coupled components, and limited visibility into system behavior created constraints across performance, agility, and long-term sustainability.
The transformation was driven using the Galent AI Platform, adopting a POC-first, validation-led modernization approach. This enabled early-stage feasibility assessment, rapid system understanding, and structured execution.
By combining AI-led system intelligence with a spec-driven engineering model, the program enabled rapid decomposition, validation, and execution at scale while ensuring that only viable, high-impact transformation paths were taken forward.
The result: a more efficient, scalable, and future-ready architecture backed by measurable ROI and a significantly optimized operating model.
Client Challenges:
While the organization operated at scale, its billing platform presented several structural and operational challenges:
Legacy Technology Stack & Technical Debt: The system relied on outdated frameworks and deprecated technologies, increasing maintenance overhead, security risks, and limiting extensibility.
Monolithic Architecture Constraints:Tightly coupled components and batch-driven processes reduced agility, making it difficult to introduce enhancements or respond to evolving business needs.
Limited System Visibility:A lack of structured documentation and architectural clarity made it challenging to fully understand system dependencies, features, and integration points.
Scale Complexity:126 features spread across 541 backend files, 51 frontend files, and 146 database tables making manual modernization planning impractical.
Performance Limitations: As a revenue-critical system, the platform faced increasing pressure to handle growing transaction volumes without proportional improvements in performance.
High Support Overhead: Significant effort was required to maintain legacy systems, with large teams supporting ongoing operations driving up long-term costs.
Galent’s Approach
- Proof of Concept (POC): Focused on feasibility, validation, and demonstrating AI-led transformation potential.
- Implementation: Scaled execution of validated use cases.
- Identification of what is feasible vs. not feasible.
- Early validation of business impact and technical viability.
- Transparent recommendations, including explicitly ruling out low-value or non-viable paths.
- Detailed architecture diagrams and system overviews.
- Domain and process mappings.
- Comprehensive feature inventories.
- Contextual insights into integrations, components, and data flows.
- Spec discovery including domain glossary, bounded contexts, and API/event specifications.
- Microservices-based decomposition
- Cloud-native design principles
- Migration strategies aligned to business priorities
- Estimated timelines and resource requirements
- Managing ongoing support
- Driving continuous enhancements
- Reducing dependency on large support teams
- Reduced Transformation Risk through Validation: The POC-first approach ensured early feasibility validation minimizing risk and eliminating investment in non-viable transformation paths.
- Outcome-based Fixed pricing: Client pays only for validated, deployed working software not just at the feature level, but even at the epic-level
- Deep Visibility into Architecture & Functionality: Produced detailed architecture diagrams, domain and process mining outputs, and feature lists from 1,000+ files providing clear visibility into system structure and modernization opportunities.
- Spec‑Driven, Execution‑Ready Modernization Backlog: The platform transformed legacy complexity into a fully implementation‑ready backlog, decomposing 126 features into 275 AI‑generated epics.
- Actionable Insights for Decision-Making: Modernization decisions were guided by data‑backed insights, including risk scoring, feasibility assessments, and migration recommendations.
- Lean team structures
- Improved system stability
- Lower maintenance overhead
- Faster ROI Realization: Achieved ROI within an estimated 2–3 year horizon, driven by sustained cost savings and improved efficiency.
We leveraged our AI-native platform to drive a comprehensive, insight-led and validation-first modernization strategy, combining deep system analysis with structured transformation planning.
POC-First, Validation-Led Transformation
The engagement followed a two-phase model:
The POC phase was treated as a critical investment layer, enabling:
This honesty-led approach ensured focused investment and reduced downstream risk.
AI-Led System Analysis
The Galent AI Platform was deployed within the client environment to ensure secure, compliant analysis of the existing system. Initial validation was conducted on a representative sample, followed by full-scale backend analysis to extract features, architecture, and dependencies.
Knowledge graph and context graph were generated from 126 features in under 24 hours.
Platform-Driven Insights & Mapping:
Using specialized AI agents, the platform generated:
This enabled rapid understanding of a complex legacy system that would traditionally require extensive manual effort.
Validation & Alignment:Findings were validated through collaborative sessions with client stakeholders ensuring alignment across domain models, feature sets, and technology components before progressing further.
Target State Definition: Based on validated insights, Galent proposed a customized target architecture, including:
A phased transformation approach was recommended to ensure minimal disruption and controlled execution.
Solution Delivered – Execution Model
Spec-driven Epic Decomposition: 126 features, 275+ epics across complexity bands, each with AI-generated specs and test criteria.
AI-Accelerated Delivery: 3-day epic cycles with pod-based teams covering code generation, validation, and testing.
Traceability & Governance: End-to-end linking of specs to code for impact analysis and contract-driven testing.
Lean POD-Based Operating Model: Transitioned to a small, cross-functional team structure (3-member pods) capable of:
Outcome-Based Pricing Model: Client pays only for validated, deployed working software with zero upfront cost.
Business Impact
The AI-led modernization initiative delivered measurable improvements across system understanding, execution efficiency, and long-term operational economics.
Key outcomes:
<Sustained Cost Optimization Cost-modernization, support effort was significantly reduced through:
This resulted in 50–70% reduction in annual support costs, enabling a self-funding transformation model where modernization investments were offset by operational savings over time.
This engagement highlights how a POC-first, AI-led modernization approach can transform the way enterprises approach legacy transformation shifting from experimentation-heavy initiatives to validated, outcome-driven execution.
By combining deep system intelligence, structured validation, and a lean operating model, the organization successfully transitioned from a legacy-bound architecture to a scalable, resilient, and cost-efficient platform.The result is not just a modernized system, but a future-ready foundation built for sustained growth, agility, and measurable business impact.
Executive Insight: A Client Perspective
“Galent’s approach gave us clarity where we had uncertainty. The ability to validate what would work and just as importantly, what wouldn’t before scaling was invaluable. Beyond modernization, the real impact came from the long-term efficiency gains, where we’ve significantly reduced support overhead while improving system stability.”
– Head of Engineering Transformation