Add Use Cases section + intro before tier table (consistency with GitHub)

README.md

@@ -609,6 +609,8 @@ Community discussions attached to editorial articles, with threading support for
 
 ### Tier Availability
 
+The parquet datasets ship with **all paid tiers except the $200 Core**:
+
 | Tier | CSV Core | Parquet Datasets |
 |------|----------|------------------|
 | **$200 One-Time Core** | ✅ | ❌ |
@@ -637,6 +639,30 @@ news_comments = pq.read_table('news_comments.parquet').to_pandas()
 
 Full schema in [`SPEC.md`](SPEC.md).
 
+### Use Cases
+
+**CSV Core (all tiers):**
+- **E-commerce** — Enrich product listings with detailed fragrance data, notes, accords
+- **Mobile Apps** — Build fragrance collection managers, scent discovery apps, perfume catalog apps
+- **Data Analysis** — Analyze fragrance industry trends by brand, country, perfumer, year
+- **Recommendations** — Content-based or collaborative filtering systems using accord/note vectors
+- **Multilingual UIs** — Localized perfume catalogs in 23 languages out of the box
+- **Knowledge Graphs** — Brand → Perfumer → Fragrance → Notes → Accords graph construction
+- **Market Research** — Country-of-origin analysis, parent company portfolios, perfumer productivity stats
+
+**Parquet Datasets ($400+ tiers):**
+- **NLP & Sentiment Analysis** — Train models on 4.6M multilingual fragrance reviews
+- **Recommender Systems** — Hybrid models combining CSV structure with review text similarity
+- **Language Models** — Domain-specific corpus for fragrance/perfumery LLM fine-tuning
+- **Review Classification** — Identify positive/negative reviews, fake review detection
+- **Trend Detection** — News article timeline analysis, emerging fragrance trends
+- **Author Networks** — Identify influential reviewers, perfumery journalists, community leaders
+- **Content-Based Discovery** — "Articles about this perfume" — JOIN news.related_pids with fragrances.pid
+- **Community Analytics** — Reply networks, engagement metrics on editorial content
+- **Cross-Language Studies** — Compare review sentiment across 23 languages for the same fragrance
+- **Search Engines** — Full-text search across reviews, articles, and structured metadata
+- **Knowledge Extraction** — Mine 24K editorial articles for perfume facts, launch dates, perfumer interviews
+
 ### Full Database
 
 | | Sample | Full Database |