AI-assisted application modernization

Vulnerability and security remediation

Scanner findings, open penetration test items and the dependency debt behind them, closed across whole application portfolios. On one engagement for a national distributor, that meant more than 620 known vulnerabilities eliminated and every penetration test finding remediated, with results cross-checked against a second vulnerability database that surfaced issues the first scanner missed.

• Commercial scanner findings remediated and cross-checked against the open-source OSV database
• Penetration test findings closed, with the evidence your security team needs
• Real vulnerabilities found beyond what the scanner reported, and fixed

• OSV
• OWASP

Framework and runtime upgrades

Applications several major versions behind are a security problem before they are a feature problem. We bring frameworks and runtimes current: on the distributor’s portals, Angular 16 to 21 and Azure Functions from the .NET 6 in-process model to the .NET 10 isolated worker, with Angular bundles about 50 percent smaller.

• Several major versions closed in one engagement
• Packages updated across every project
• Dead infrastructure found and removed along the way: more than $25K a year realized on that engagement

• Angular
• .NET
• Azure Functions
• TypeScript

Codebase consolidation and multi-tenancy

Many estates carry the same application deployed once per customer or business unit, each copy drifting and each one billed. We consolidate them into one multi-tenant set of services, so a new customer means a new database rather than a new suite of cloud resources. For the distributor, five to six copies of one codebase are moving onto one multi-tenant platform, with about $78K a year in projected savings.

• Per-customer copies mapped, compared and folded into shared services
• A new customer becomes a new database, not a new suite of Azure resources
• Duplicated cloud resources retired as each tenant moves across

• Azure
• Multi-tenant architecture

Legacy ETL conversion to Databricks

For a major national retailer we built an AI-assisted migration tool, using Claude and structured prompts, that converts legacy ETL logic, orchestration scripts and deployment configurations into PySpark pipelines on Databricks. Unit tests and infrastructure as code are generated with every conversion, and in the first phase the tool handled 78% of the legacy transformation types.

• Legacy transformations converted into PySpark pipelines on Databricks
• Unit tests and Terraform generated alongside every pipeline
• Senior engineers validating output against known behavior instead of translating by hand

• Databricks
• PySpark
• Claude
• Terraform
• GitHub Actions