CASE STUDY 01 / PRODUCT ENGINEERING

Dieselmatic product platforms.

A connected technology layer for a North American heavy-duty repair marketing company: data collection, customer operations and self-service website delivery.

3PLATFORMS BUILT
11EXTERNAL INTEGRATIONS
2023START OF CHAPTER
FULLTECHNOLOGY OWNERSHIP

Context and responsibility.

Dieselmatic needed a reliable product foundation that could connect marketing data, customer-facing reporting and website delivery. For DieselSites V1, the immediate product question was whether repair shops wanted a self-service website product, not whether a small team could reproduce a full publishing suite on every device.

A full mobile page editor carried disproportionate V1 build and maintenance cost. I recommended a simpler content editor inside a template-driven system so we could test demand without committing the small team to the most expensive editing surface. I still carried the technology layer end-to-end: architecture, backend, frontend, data flows, deployment and production maintenance.

Three connected platforms.

API and data collector

A shared integration layer collects and normalizes operational and marketing data for downstream workflows and reporting.

Customer Portal

Customer dashboards cover billing, return-on-investment reporting, campaigns, reputation management and client-facing account workflows.

DieselSites

A self-service website SaaS supports onboarding, template-driven site delivery, customer controls and subscription workflows for heavy-duty repair shops. The V1 decision produced a credible MVP while preserving room to learn from real demand before investing in deeper page-editing capabilities.

Integration surface.

The platform coordinates data and workflows across AgencyAnalytics, Google Cloud, Google Ads, Intercom, Stripe, HubSpot, Meta Ads, Fullbay, CallRail, Zoho and SendGrid.

  • PHP / LARAVEL
  • REACT
  • OPENAI SDK
  • STRIPE
  • GOOGLE CLOUD
  • DOCKER
  • HUBSPOT
  • META ADS
AI inside a real operating system

OpenAI SDK workflows support structured data extraction, reporting and operational automation. The goal is practical leverage inside established product boundaries, not an AI layer detached from the business process.

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