Context and responsibility.
Dieselmatic needed a reliable product foundation that could connect marketing data, customer-facing reporting and website delivery. I worked inside a small team while carrying the technology layer end-to-end: architecture, backend, frontend, data flows, deployment and production maintenance.
This was not isolated feature work. Product decisions, integration contracts, operational tooling and the details that keep a live platform dependable all sat inside the same ownership boundary.
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.
Integration surface.
The platform coordinates data and workflows across AgencyAnalytics, Google Cloud, Google Ads, Intercom, Stripe, HubSpot, Meta Ads, Fullbay, CallRail, Zoho and SendGrid.
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.