Governance-first semantic analytics for enterprise quotation workflows
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.
Build a proof of concept around a curated semantic layer instead of exposing raw enterprise tables to an LLM.
SAP ERP
│ daily batch
▼
Snowflake raw → dbt staging → dbt transforms
│
▼
Semantic views
│
▼
Cortex guardrails & audit
┌────────┴────────┐
▼ ▼
Snowflake UI Internal chatbot
| 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 quotation research; avoids premature real-time complexity. |
| Guardrails in data layer | Protection of personal and customer information, access control, freshness checks, and exception escalation are enforceable—not only prompt instructions. |
| 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.
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.