chore: ignore PDF source artifacts
Keep generated PDF source files local while publishing the final PDF.\n\nCo-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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@gabriel.pereira
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<!doctype html>
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<html>
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<head>
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<meta charset="utf-8">
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<title>Talk-to-Data Case Study</title>
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<style>
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@page{size:A4;margin:14mm}body{font-family:Arial,sans-serif;color:#1f2933;font-size:10pt;line-height:1.25}h1{color:#0b4f71;font-size:24pt;margin:0 0 2pt}h2{color:#0b4f71;font-size:13pt;margin:10pt 0 4pt;border-bottom:1px solid #b8c9d3;padding-bottom:2pt}p{margin:4pt 0}table{width:100%;border-collapse:collapse;margin:4pt 0 7pt;font-size:9pt}th{background:#0b4f71;color:#fff;text-align:left}th,td{border:1px solid #cbd5e1;padding:4pt;vertical-align:top}pre{background:#eef4f7;padding:6pt;border-left:3px solid #0b4f71;font-size:8pt}strong{color:#0b4f71}
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</style>
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</head>
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<body>
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<h1>Talk-to-Data</h1>
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<p><strong>Governance-first semantic analytics for enterprise quotation workflows</strong></p>
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<h2>The challenge</h2>
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<p>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 <strong>5–30 minute delay per question</strong>.</p>
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<h2>The approach</h2>
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<p>Build a proof of concept around a curated semantic layer instead of exposing raw enterprise tables to an LLM.</p>
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<pre>SAP ERP
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│ daily batch
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▼
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Snowflake raw → dbt staging → dbt transforms
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│
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▼
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Semantic views
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│
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▼
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Cortex guardrails & audit
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┌────────┴────────┐
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▼ ▼
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Snowflake UI Internal chatbot</pre>
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<h2>Architecture decisions</h2>
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<table><tr><th>Decision</th><th>Why</th></tr>
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<tr><td>Snowflake + Cortex</td><td>Fast POC path with data, AI, and governance in one platform.</td></tr>
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<tr><td>dbt layers</td><td>Reproducible SQL, lineage, testing, and versioned business logic.</td></tr>
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<tr><td>Semantic views</td><td>One business contract for dashboards and natural-language queries.</td></tr>
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<tr><td>Daily refresh</td><td>Sufficient for quotation research; avoids premature real-time complexity.</td></tr>
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<tr><td>Guardrails in data layer</td><td>Protection of personal and customer information, access control, freshness checks, and exception escalation are enforceable—not only prompt instructions.</td></tr></table>
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<h2>Estimated outcome</h2>
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<table><tr><th>Signal</th><th>Estimate</th></tr>
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<tr><td>Routine answer latency</td><td><strong>5–30 min → <1 min</strong></td></tr>
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<tr><td>Sales users in scope</td><td><strong>30+</strong></td></tr>
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<tr><td>Data freshness</td><td><strong>Daily batch</strong></td></tr>
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<tr><td>Main benefit</td><td>Less coordination overhead and faster quote responses.</td></tr></table>
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<p>These are <strong>estimates based on the human workflow</strong>, not measured production KPIs. The next delivery step is a controlled pilot that captures actual latency, adoption, escalation rate, and answer quality.</p>
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<h2>Delivery lesson</h2>
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<p>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.</p>
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<p><strong>Stack:</strong> SAP source system · Snowflake · dbt · Snowflake semantic views · Cortex Analytics · internal chatbot workspace</p>
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<p><em>All examples are anonymized and contain no proprietary source data.</em></p>
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</body>
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</html>
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# Talk-to-Data
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## Governance-first semantic analytics for quotation workflows
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### The challenge
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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**.
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### The approach
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Build a proof of concept around a curated semantic layer instead of exposing raw enterprise tables to an LLM.
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```text
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SAP ERP
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│ daily batch
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▼
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Snowflake raw → dbt staging → dbt transforms
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│
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▼
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Semantic views
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│
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▼
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Cortex guardrails & audit
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┌────────┴────────┐
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▼ ▼
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Snowflake UI Internal chatbot
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```
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### Architecture decisions
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| Decision | Why |
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|---|---|
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| Snowflake + Cortex | Fast POC path with data, AI, and governance in one platform |
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| dbt layers | Reproducible SQL, lineage, testing, and versioned business logic |
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| Semantic views | One business contract for dashboards and natural-language queries |
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| Daily refresh | Sufficient for the quotation research use case; avoids premature real-time complexity |
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| Guardrails in the data layer | Protection of personal and customer information, access control, freshness checks, and exception escalation are enforceable—not only prompt instructions |
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### Estimated outcome
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| Signal | Estimate |
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|---|---:|
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| Routine answer latency | **5–30 min → <1 min** |
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| Sales users in scope | **30+** |
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| Data freshness | **Daily batch** |
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| Main benefit | Less coordination overhead and faster quote responses |
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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.
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### Delivery lesson
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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.
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**Stack:** SAP source system · Snowflake · dbt · Snowflake semantic views · Cortex Analytics · internal chatbot workspace
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*All examples are anonymized and contain no proprietary source data.*
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