Modernizing a Mission-Critical Billing Platform into a Cloud-Native, AI-Enabled Architecture
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 project focused on transforming this legacy system using the Galent AI Platform enabling a structured, AI-led modernization approach. The transformation was driven by a spec-driven engineering model, enabling rapid decomposition, validation, and execution at scale.
The result: a more efficient, faster, and scalable architecture purpose-built for future growth.
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Legacy Technology Stack & Technical Debt:The system relied on outdated frameworks and deprecated technologies, increasing maintenance overhead, security risks, and limiting extensibility.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.
Galent’s Approach
- Detailed architecture diagrams and system overviews
- Domain and process mappings
- Comprehensive feature inventories
- Contextual insights into integrations, components, and data flows
- Microservices-based decomposition
- Cloud-native design principles
- Migration strategies aligned to business priorities
- Estimated timelines and resource requirements
- Spec-driven Epic Decomposition: 126 features, 364+ 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 gen, validation, and testing.
- Traceability & Governance: End-to-End linking of specs to code (~79k loc) for impact analysis and contract-driven testing.
- Outcome-Based Pricing Model:Client pays only for validated, deployed working software with zero upfront cost
- 126 Features → 364 Epics (Implementation-ready Backlog)
- 3-day AI-Accelerated Epic Delivery Cycles
- 9-month, 7-Phase Structured Transformation Roadmap
- Outcome-Based Delivery Model With no Upfront Cost
Galent leveraged its AI-native platform to drive a comprehensive, insight-led modernization strategy combining deep system analysis with structured transformation planning.
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:
Spec discovery including domain glossary, bounded contexts, and API/event specifications.
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
Business Impact
The AI-led modernization initiative delivered measurable improvements in system understanding, decision-making, and transformation readiness.
Key outcomes:
1. Comprehensive System Understanding at Scale: Achieved accurate feature extraction and architecture mapping across legacy systems validated by stakeholders for over 580 lines of code in initial samples and extended to 79,000+ lines in full backend analysis.
2. 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.
3. Actionable Insights for Decision-Making: Delivered executive summaries and dashboards with risk scores and recommendations enabling data-driven prioritization and informed modernization decisions.
4. Defined Future-State Architecture: Proposed a tailored target architecture, including microservices decomposition and migration strategies supported by estimated timelines and resource planning based on stakeholder inputs.
5. Reduced Transformation Risk through Validation: Enabled collaborative validation sessions across domain, feature, and technology layers ensuring alignment, reducing uncertainty, and accelerating readiness for implementation.
Impact Metrics
This engagement highlights how AI-led analysis can transform the way enterprises approach legacy modernization turning complex, opaque systems into structured, actionable insights within a fraction of the time.
By combining deep system intelligence with a clearly defined transformation roadmap, the organization is now positioned to transition from a legacy-bound architecture to a scalable, resilient, and future-ready platform built to support evolving business demands and sustained growth.
Executive Insight: A Client Perspective
“The ability to generate a complete system – understanding features, dependencies, and architecture etc. in under 24 hours was a game changer. Galent’s AI platform didn’t just accelerate analysis; it gave us the confidence to move forward with modernization at scale.”
– Head of Engineering Transformation, Hughes