Sector: Artificial Intelligence and Data
The quiet layer everything else runs on.
Good software runs on good data. We build the infrastructure that collects data from inconsistent sources, cleans it into a consistent shape, and distributes it to the tools that depend on it, starting with vehicle inventory and extending across the portfolio.
The unglamorous work that makes everything smarter
Data sounds simple until you work with it. Sources go stale, feeds break quietly, and the same thing is described five different ways across five systems. We built data infrastructure because every product we operate kept hitting that same wall, and solving it well once was better than solving it badly everywhere.
Owning this layer is a working example of our preference for owning the data our products depend on rather than renting it.
Collect, normalize, distribute
The pattern is consistent across the portfolio: retrieve data on a schedule with monitoring that notices when a source changes, resolve it into clean and deduplicated records, and deliver it to the tools that need it through simple integrations. When the data underneath is accurate and current, everything built on top of it gets better.

What we do here
Capabilities in Data and Inventory Infrastructure
Automated collection
Scheduled retrieval from many sources, with monitoring that catches silent breakage.
Normalization
Inconsistent inputs resolved into consistent, deduplicated records.
Distribution
Clean data delivered to the tools that depend on it through simple integrations.
Freshness
Real-time accuracy, because data that is right tomorrow is worthless today.
Questions and Answers
Questions about data and inventory infrastructure
What data infrastructure does DotNet Holdings operate?
Why build data infrastructure instead of renting it?
Artificial Intelligence and Data
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