Most multi-location businesses run on data that no system can actually read: financial reports as PDFs, operations knowledge in email threads, vendor terms in someone's inbox. For Palms Car Wash, a 5-location business in Austin, Agxntsix is building the alternative — a unified, LLM-readable data layer. It is our flagship architecture engagement, currently in its foundation phase.
What is a unified data layer?
It is a single governed knowledge system that ingests what the business already produces — emails, call transcripts, vendor data, raw financial exports — and restructures it so both humans and AI models can query it reliably. Instead of "ask the person who remembers," the business gets "ask the system, get a sourced answer."
What has been built at Palms so far?
| Layer | What it does |
|---|---|
| Ingestion | Pulls emails, transcripts, vendor data, and raw financial exports into one pipeline |
| Extraction | Financial reporting extraction with cross-checking against source documents |
| Knowledge base | A structured, LLM-readable vault unifying all 5 locations |
| Decision support | Interactive analysis decks generated from the extracted data |
The financial pipeline came first deliberately: extracted figures can be verified against the source exports, which builds trust in the layer before softer knowledge (operations, vendor history) is added on top.
Why does this matter more than another dashboard?
Dashboards show you numbers someone decided to chart. A data layer answers questions nobody anticipated — because the underlying knowledge is structured, governed, and traceable to sources. When a new AI capability ships next quarter, a business with a unified data layer plugs it in; a business without one starts another data-cleanup project.
This is the same architecture thinking behind our AI infrastructure practice, and Palms is the first instance of a repeatable per-client pattern. If your business runs on PDFs and inboxes, book a consultation — the foundation phase is smaller than you think.