AFG.Tech, a leading automotive SaaS provider unified its data and built its AI foundation with LakeStack in just four weeks.
Customer overview
- CRM - leads, sales pipeline, customer lifecycle
- Workshop management - job cards, service logs, technician activity
- Parts & invoicing - consumables, order history, billing
- Customer feedback & service history - repeat service patterns, CSAT, issues
- Finance & compliance - invoices, audits, regulatory data
Industry and customer challenges
The automotive software space faces a common bottleneck: data fragmentation. As dealership networks expand, each location introduces new systems, formats, and reporting practices, making visibility and consistency increasingly difficult.
1. Fragmented dealership data
Data lived across CRM, workshop management, ERP-style tools, feedback systems, and local formats. Different schemas and inconsistent inputs made it difficult to achieve a unified operational or customer view.
2. Inconsistent visibility across dealerships
Leadership lacked standard dashboards or real-time insights into sales, service throughput, parts trends, or customer behavior.
3. Heavy manual reporting workflows
Employees spent hours extracting data, merging spreadsheets, validating workshop logs, and assembling performance reports. This grew linearly with each new dealership added.
4. Zero data engineering bandwidth
AFG.Tech lacked an internal data engineering team. Any new data source, dashboard, or report required weeks of effort by developers already overloaded with core product work.
5. Expanding customer base increased complexity
Onboarding each new dealership added more systems, formats, and data flows that were time-consuming to support manually.
How LakeStack helped AFG.Tech
A production-grade proof of concept was delivered in four weeks, proving end-to-end capabilities across ingestion, modeling, governance, and analytics without requiring internal engineering effort.
Ingested and harmonized CRM, workshop, invoicing, customer feedback, and service history into a single governed model.
Ingestion pipelines were built through LakeStack’s no-code framework, eliminating manual exports and reducing engineering dependency.
Dealer performance, workshop KPIs, technician utilization, parts trends, and customer repeat-service metrics became available on day one.
Leadership could run natural-language queries like: “Show top-performing dealers this week” or “Which services have the longest turnaround time?”
The deployment established a foundation for predictive service prompts, parts demand forecasting, and dealer performance insights.
Row-level security, dealer-level permissions, audit logs, and structured access were configured without custom engineering.
Success metrics
- 9-12 months of engineering effort avoided.
- 80% reduction in ingestion and reporting workload.
- Reporting cycles reduced from days to minutes.
- Operational workflows became 40–50% faster.
- Achieved a governed, AI-ready foundation without hiring engineers.
Partnership note
AFG.Tech’s PoC was delivered in partnership with nClouds, a Premier Tier AWS Partner, with Applify powering the data engineering foundation through its LakeStack data intelligence platform on AWS.
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