AI-Powered Architecture Discovery for a Legacy Monolith

An enterprise-grade policy administration and claims platform serving multiple lines (auto, property, liability, etc.) needed a revamp to improve performance, reduce operating expenses, and boost user experience. Since major modules included quotes, underwriting, endorsements, billing, collections, loss notices, indemnification, and subrogation, we needed to map dependencies within the app and workflow to ensure the smooth, secure transition.

The app core is represented by a tightly coupled monolith with embedded ratings, rules, document generation, and batch jobs for nightly bordereaux, commissions, and compliance reporting. Also, there were connections to payment gateways, credit bureaus, third-party data providers, and regulatory e-filing.

Recommended by industry experts, the client sought to eliminate operational pain points, opaque dependencies, manual handoffs, and fragile CI/CD. Devox Software’s team got to work, making a zero-downtime, evidence-driven modernization kit.

The team roles were flexibly distributed according to the project phase and involved a Delivery Manager, a Solution Architect, a Senior Backend Engineer, a Data and ML Engineer, a DevOps Engineer, a QA Engineer, and a Business Analyst with experience in InsurTech.

The project showed the most common difficulties when trying to upgrade a legacy app:

• Sparse documentation,
• Tight coupling across policy, billing, claims, and reporting services,
• Brittle CI/CD;
• Inconsistent data contracts;
• Mixed tech generations, and more.

However, industry-specific hurdles amplified the risk:

• The differences in state-by-state insurance rules and reporting made it hard to implement universal procedures and required audit trails.
• Rating, scoring, and underwriting engines required actuarial sign-off.
• Reinsurance, bordereaux, and catastrophe modeling were connected to external services (desktop version).

.NET 8, ASP.NET Core, Azure AKS, Azure DevOps, OpenTelemetry, Grafana, Kafka, Redis, PostgreSQL, Azure Key Vault, Terraform

Our approach was to implement an AI-assisted “Architecture Discovery” pipeline.

First, we ingested source, configs, SQL, and CI scripts, and then parsed call graphs and DB dependencies. Next, the correlated dependencies were connected with production traces to assess real-world flows.

As a result, we’ve built a navigable knowledge graph showing domains, owners, data lineage, SLIs/SLOs, and services. This measure has opened the way to a strangler-fig roadmap:

• carving out low-risk domains first,
• wrapping high-risk modules behind anti-corruption layers,
• shifting to canary releases with automated rollback.

Additionally, observability and pre-flight checks reduced deployment risk.

We’ve created a comprehensive app architecture mapping to prepare for further modernization steps. This way, the client has received the following outcomes:

• shortened time for the modernization process,
• fewer Sev-1 incidents during releases,
• faster root-cause analysis thanks to unified tracing,
• and, of course, a clear migration path, security- and audit-approved.

As a result, the following modernization has been carried out without downtime.