Skip to main content
This is how most organizations actually start: not from a clean database, but from a stack of mixed documents — SOPs, a metrics document, data dictionaries, process-flow diagrams, call transcripts, golden datasets, access policies.

​
6.1 · Gather the pile

Do: look at the input set (the companion repo’s example-scenario/ is a complete illustrative set — a member-services contact center, in every input type). For your own documents, see the Datasets & Document Sources reference for upload, Drive, SharePoint, object storage, and git options.

​
6.2 · Build the ontology from it

Prompt
You’ll see: Ana fuse the mixed inputs into one coherent model, narrating where each piece went — entity, metric, note, or access rule.
The ontology is more than text and .tql —Anything that helps a future agent or human understand, generate, or verify work belongs in the repo: entity/lineage diagrams, stylesheets for branded report output, spreadsheet or slide generators for recurring deliverables — with example outputs stored next to the generator and searchable filenames (finance-revenue-lineage.png, not diagram-final.png).

​
✅ Checkpoint

  • Your input pile had at least 4 input types (docs, metrics, diagrams, transcripts…)
  • Ana narrated where each input landed in the model
  • The drafted files form one model — not one model per document