Blue Ridge rebuilds and migrates its supply chain planning platform on cloud-native architecture in 9 months, with a lean team of 7 engineers and a fleet of purpose-built AI agents.
Client profile
A global provider of supply chain planning and management software
Industry
Supply Chain & Logistics
Region
North America, Global
Faster platform delivery vs. the 3-year traditional estimate
Recurring annual operational savings
Blue Ridge Global is a supply chain planning software company founded in 2007. Distributors, manufacturers, and retailers run demand forecasting, inventory optimization, and replenishment automation on their platform, paired with Blue Ridge’s signature LifeLine advisory model – demand planners and purchasing specialists embedded with every customer.
01 The ChallengeThe supply chain software market is moving fast. Distributors, manufacturers, and retailers expect AI features as standard: explainable forecasting, conversational planning, AI agents that find exceptions before they hit operations. Vendors that ship those capabilities define what customers expect. Vendors on legacy architecture lose the deals. Most importantly: a full re-architecture of an enterprise supply chain & logistics platform can run up to 36 months – more than enough time to be outpaced.
Blue Ridge’s platform had carried the business well for over a decade. Its planning engine ran on hundreds of tables, 190K+ lines of database code, and 1,500 stored procedures managing global state – encoding hard-won supply chain logic. As Blue Ridge moved upmarket toward the world’s largest retailers and distributors, the architecture started to show its age: for example, overnight order processing and forecasting cycles that had run comfortably in 8-hour windows were starting to overrun under enterprise-scale data volumes.
190K+ lines of code
Held inside the database itself, encoding a decade of supply chain logic
A traditional re-architecture of a platform that deep was projected at three years. Too slow for the market, too disruptive to enterprise customers running their operations. Blue Ridge’s leadership made a strategic call: deprecate the legacy platform, rebuild from scratch on cloud-native architecture, and migrate customers to the new stack. All in a fraction of conventional modernization timelines, and without service interruption for live customers.
02 The ApproachA traditional rebuild starts with months of reverse-engineering: what the business logic is, where the dependencies live, what edge cases the system handles. That up-front work slows conventional modernization. AI-first approach allows to do it faster, with the engineering team paired to purpose-built AI agents.
Blue Ridge engaged Provectus for a Migration & Modernization Readiness Assessment. The assessment mapped the existing platform, identified migration risks, and defined the target: a cloud-native data lakehouse ready for enterprise data volumes and the AI workloads on Blue Ridge’s roadmap.
Provectus’s engineering team then built a fleet of AI agents specifically for the migration. Once deployed, the agents covered six categories of work that engineers would otherwise have done by hand:
Alongside the purpose-built agents, engineers used Anthropic’s Claude Code and Cursor for interactive code reading, refactoring, and pair-programming through the rebuild. The agents integrated with the team’s development tools through Model Context Protocol (MCP) connectors. Guardrails kept AI output in scope: automated code reviews, self-validation loops, shared engineering standards, static analyzers, and security scanners. Engineers owned every architectural decision, every design call, and the resulting code.
The engagement ran on a lean team – 7 engineers and a PM – in two-week sprints, with a Solution Architect and a Solution Owner leading the work. Blue Ridge’s customers migrated onto the new platform component by component, with no service interruption along the way.
03 The BuildThe new platform is a cloud-native data lakehouse on Databricks and AWS, replacing the .NET monolith and MSSQL that carried the legacy stack. The benefit is that compute decouples from storage, and workloads scale horizontally with customer data volumes.
Three design decisions distinguish the build:
Each customer runs in a dedicated environment. This removes the compliance friction that used to slow enterprise deals – large customers onboard without renegotiating data residency or isolation requirements every time.
The legacy platform encoded its planning logic in hardcoded stored procedures, each one expensive to change. The new platform pulls validation rules, column mappings, and schema definitions from configuration tables. Onboarding a new customer or adjusting a validation rule is now a same-day configuration update.
Blue Ridge’s planning workloads are gigabytes of data with heavy per-row calculations. Provectus built the planning compute on Polars, a workload-matched choice for this scale of data and this style of computation. Per-day forecasting calculations dropped from minutes to seconds.
Operationally, the new architecture runs without the manual interventions the legacy stack required for nightly cycles. Observability is built in. Common processing issues are handled automatically.
The AI-first engineering workflow that delivered the rebuild is the same workflow that maintains it. Engineers use Claude Code, Cursor, and the team’s purpose-built agents for ongoing dependency analysis, impact review, and regression validation.
04 The ResultsThe rebuilt platform went from initial discovery to live launch in 9 months, against an original estimate of 3 years for traditional modernization. A team of 7 engineers and a PM delivered it, with the fleet of purpose-built AI agents handling the routine work – parsing stored procedures, mapping dependencies, generating test coverage – at a pace no manual team could match.
9 months
From initial discovery to live launch
For enterprise customers, the planning run is the most visible change. Overnight forecasting cycles that used to overrun their 8-hour windows now complete in 40 minutes. Replenishment planning went from 3+ hours to 30 minutes.
The new platform supports larger enterprise accounts without proportional growth in operating cost, contributing to an estimated $800K in recurring annual savings.
Blue Ridge is on the new platform, ready to put new AI capabilities in front of customers as the market evolves.
05 What’s NextFor Blue Ridge, the engagement continues with more customer migrations and AI features being built on the platform.
For other supply chain software vendors weighing the same modernization, Provectus’s Migration & Modernization Readiness Assessment is the typical first step. Contact Provectus to begin.