docs: add talk-to-data case study

Add anonymized architecture, governance examples, diagrams, and interview materials.\n\nCo-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

case_study_one_page.md

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+# Talk-to-Data
+## Governance-first semantic analytics for quotation workflows
+
+### The challenge
+
+More than 30 salespeople could depend on internal experts to answer routine quotation questions about products, discounts, and customer agreements. Manual lookup and email or chat coordination introduced an estimated **5–30 minute delay per question**.
+
+### The approach
+
+Build a proof of concept around a curated semantic layer instead of exposing raw enterprise tables to an LLM.
+
+```text
+SAP ERP
+   │ daily batch
+   ▼
+Snowflake raw → dbt staging → dbt transforms
+                                  │
+                                  ▼
+                         Semantic views
+                                  │
+                                  ▼
+                    Cortex guardrails & audit
+                         ┌────────┴────────┐
+                         ▼                 ▼
+                  Snowflake UI      Internal chatbot
+```
+
+### Architecture decisions
+
+| Decision | Why |
+|---|---|
+| Snowflake + Cortex | Fast POC path with data, AI, and governance in one platform |
+| dbt layers | Reproducible SQL, lineage, testing, and versioned business logic |
+| Semantic views | One business contract for dashboards and natural-language queries |
+| Daily refresh | Sufficient for the quotation research use case; avoids premature real-time complexity |
+| Guardrails in the data layer | Protection of personal and customer information, access control, freshness checks, and exception escalation are enforceable—not only prompt instructions |
+
+### Estimated outcome
+
+| Signal | Estimate |
+|---|---:|
+| Routine answer latency | **5–30 min → <1 min** |
+| Sales users in scope | **30+** |
+| Data freshness | **Daily batch** |
+| Main benefit | Less coordination overhead and faster quote responses |
+
+These are **estimates based on the human workflow**, not measured production KPIs. The next delivery step is a controlled pilot that captures actual latency, adoption, escalation rate, and answer quality.
+
+### Delivery lesson
+
+The reusable asset was not just the chatbot. It was the delivery pattern: establish the semantic contract, encode business rules, apply governance, and then expose the smallest useful interface.
+
+**Stack:** SAP source system · Snowflake · dbt · Snowflake semantic views · Cortex Analytics · internal chatbot workspace
+
+*All examples are anonymized and contain no proprietary source data.*