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Scaling to 15,000 Users: A Monolith Rescue Case Study | RMJ Technologies

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Executive Summary

RMJ Technologies, a North American fleet‑optimization provider, needed to stabilize a semi‑functional monolithic platform built offshore, restore on‑time behavior‑based driver training, and scale onboarding for rapid growth. TeamStation AI assembled a dedicated nearshore squad (Senior Frontend, Backend, QA, UX/UI) with Delivery and Product Management included, refactored critical modules, and launched a progressive microservices program that unblocked onboarding and reporting at scale. The platform is now scaling toward 15,000 users, positioning RMJ for multi‑million‑dollar revenue expansion.

Client Snapshot

  • Industry: Automotive — Fleet Optimization & Predictive Coaching (Behavior‑Based Automated Driver Training)
  • Headquarters: San Diego, CA
  • Platforms: Web SaaS for enterprise fleets; integrations with telematics (GeoTab)

The Challenge

  • Budget & reliability: Operate within a lean budget while stabilizing a semi‑functional platform built in India.
  • Documentation deficit: Minimal architectural documentation; unclear service boundaries.
  • Monolith bottlenecks: Onboarding failures and delayed scheduled driver training due to tightly coupled modules and shared database constraints.
  • Reporting pressure: Stakeholder demand for faster, more granular reporting across large tenant deployments.

Why TeamStation AI (Nearshore IT Copilot)

  • Precision team assembly: AI‑assisted selection of senior engineers with telematics, .NET, and Vue experience; proven large‑tenant SaaS history.
  • Delivery discipline: Embedded Delivery Manager and Product Manager (included) for scope control, roadmap clarity, and outcome accountability.
  • Time‑zone velocity: Same‑day iteration with U.S. leadership; faster decision loops, fewer handoff losses.
  • Built-in governance: MDM‑managed devices, MFA/SSO, least‑privilege access, pen‑testing cadence, and SOC 2/ISO‑aligned practices.
  • Continuity & scale: An elastic bench to surge for peak releases while maintaining core team cohesion.

Objectives

  1. Modernize architecture: Evolve from a fragile monolith to a microservices‑oriented design supporting large‑scale onboarding.
  2. Restore training reliability: Ensure on‑time, policy‑compliant automated driver training schedules.
  3. Unlock reporting: Deliver fast, accurate, role‑aware reporting for enterprise stakeholders.
  4. Reduce operational risk: Improve observability, fault isolation, and change safety (CI/CD, rollbacks).

Solution & Delivery

Team Composition

  • Senior Backend Engineers (C#/.NET, SQL Server, LINQ)
  • Senior Frontend Engineers (Vue.js, modern JS)
  • QA Engineering (functional, regression, performance)
  • UX/UI for workflow clarity and safety‑critical interaction design
  • Delivery Manager & Product Manager (included)

Key Architecture Decisions

  • Domain boundaries:
    • Tenant & User Management — isolation and provisioning flows for large fleets.
    • Training Orchestrator — independent scheduler with retry/backoff and idempotent jobs to guarantee on‑time driver training.
    • Telemetry Ingestion (GeoTab) — buffered ingestion and normalization to decouple real‑time events from reporting loads.
    • Reporting/Analytics — read‑optimized patterns to avoid monolith write contention.
  • Technology baseline:
    • Server: C# and .NET (.NET Framework base with progressive adoption of .NET‑based microservices).
    • Data: SQL Server with query tuning, indexing strategy, and partitioning plan for scale.
    • Frontend: Vue.js SPA, gradually retiring legacy jQuery while preserving critical flows.
    • Integration: Stable adapters for GeoTab; message/queueing for asynchronous jobs.
    • Ops: CI/CD pipelines, environment parity, feature flags, and safe rollback playbooks.

Outcomes

  • Platform stabilized; training punctuality restored — scheduled driver training is issued on time, protecting compliance.
  • Refactor complete for priority modules; microservices program in flight — reliability and isolation improved without downtime.
  • Scalability unlocked — onboarding progressing toward 15,000 users, enabling multi‑million‑USD revenue.
  • Stakeholder reporting accelerated — faster dashboards and exports for enterprise clients.
  • Operational risk reduced — improved observability, lower incident frequency, and safer deploys.