> ## 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.

# Start with Your North Star

> The single biggest mistake with a starter pack is to skip this step — to point it at your custody warehouse, decide it’s "close enough," and ship. That bakes in decisions before… (~15 min)

The single biggest mistake with a starter pack is to skip this step — to point it at your custody warehouse, decide it's "close enough," and ship. That bakes in decisions before you know which questions matter, and you get a model that's 10% useful and 90% noise. This module is the antidote, and it takes 15 minutes.

## 0.1 · Answer one question

<Note>
  **The North Star question** — **When this works, what does someone do differently on Monday morning?**
</Note>

Not "model all of wealth management." Something narrow and real: *"the COO stops waiting a week for the firm-wide net-flows number,"* or *"every advisor sees the concentrated positions in their book before the quarterly review."* If you can't name it yet, you're not ready to build the ontology — you're ready to have **this** conversation. Have it first.

## 0.2 · Pick the archetype that fits

Most wealth / asset-management engagements start from one of three North Stars. Pick the closest — it tells you which surfaces, classification, and governance to lead with, and which to leave for later. (Full detail in the repo's [`NORTH_STAR.md`](https://github.com/TextQLLabs/ontology-starter-kits/blob/main/wealth/NORTH_STAR.md).)

<table>
  <tr><th>Archetype</th><th>For</th><th>Lead with</th></tr>
  <tr><td>**A · BI parity**</td><td>"Match the AUM, flows, and book numbers we already report"</td><td>`aum`, `net_flows`, `advisor_book`, `effective_fee_rate` + golden queries — reconcile *first*, govern second.</td></tr>
  <tr><td>**B · Portfolio risk & concentration**</td><td>"Find the exposure before it finds us"</td><td>`concentration_risk`, `asset_allocation`, `benchmark_relative_return` + the classification layer.</td></tr>
  <tr><td>**C · Household rollup & suitability**</td><td>"Report at the relationship, not the account"</td><td>`advisor_book`, `client_attrition`, `aum` by segment + `identity-resolution.md` and MNPI/PII governance.</td></tr>
</table>

## 0.3 · Let Ana write it down (next module)

You don't pick from a question bank or fill out a worksheet. In **Module 1** you let Ana look at your actual data, run a short scoping conversation, and draft your North Star — one paragraph on what the ontology is *for*, plus the 6-8 questions it must answer in 30 days — saved as `north_star.md` and proposed as a reviewed change. The archetypes above are examples to spark yours, not a menu; Ana recommends one from what it sees.

<Note>
  **Why the model gets cheaper as you use it** — This isn't just good practice — it's the design. An agent that re-discovers your warehouse on every question pays an "amnesia tax" (most of its tokens go to rediscovery, and results aren't reproducible). Reading a known model first and committing what it learns inverts that: discovery is paid **once** and amortized across every future question. See TextQL's research, *"Malleability Is All You Need"* (VLDB 2026) — a measured \~30% token reduction and a model that gets more reliable the more it's used.
</Note>

### ✅ Checkpoint

* [ ] You can state your North Star in one sentence
* [ ] You know the three archetypes (BI parity / risk & concentration / household rollup) — examples to spark yours, not a menu

**Not sure which archetype?** That is fine — in **Module 1** Ana looks at your data and recommends one. You do not have to decide here.