> ## Documentation Index
> Fetch the complete documentation index at: https://docs.textql.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Module 6 · Track B: Build from Documents

> 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 diagram… (~30 min)

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](https://github.com/TextQLLabs/ontology-workshop-guide)'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](/workshops/build-your-ontology/reference) reference for upload, Drive, SharePoint, object storage, and git options.

## 6.2 · Build the ontology from it

```text Prompt theme={null}
Read these documents — SOPs, a metrics document, data dictionaries, process-flow diagrams, and call transcripts. Propose an ontology: the core entities, the metrics worth governing, and the dimensions. Explain where each input lands — which became an entity, a metric, a note, or an access rule — then draft the files.
```

<Check>
  **You'll see:** Ana fuse the mixed inputs into **one** coherent model, narrating where each piece went — entity, metric, note, or access rule.
</Check>

<Note>
  **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`).
</Note>

### ✅ 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