Merge branch 'main' of https://huggingface.co/spaces/DataEyond/Agentic-Service-Data-Eyond-Catalog

ARCHITECTURE.md

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+## Product vision (north star)
+
+Data Eyond is an *AI data scientist* for business analytics, structured around **CRISP-DM** (Business Understanding → Data Understanding → Data Preparation → Modeling → Evaluation → Deployment). Targets executives doing self-serve deep-dives and data analysts/scientists offloading routine work.
+
+Envisioned user flow: **interview agent** captures goal → user connects data sources → asks natural-language question → CRISP-DM-structured analytical response, exportable as a **presentation** or **notebook-style report**.
+
+The catalog-driven, IR-based architecture documented below is the *foundation*. The next architectural evolution is an agentic layer (analytical planner, per-stage CRISP-DM agents, evaluator, reporter) that consumes the existing IntentRouter → QueryPlanner → Executor → ChatbotAgent spine as its tool layer. See `REPO_CONTEXT.md` → *Roadmap — agentic evolution* for the target agent topology.
+
+---
+
 ## TL;DR
 
 A catalog-driven AI service for data analysis. Users upload documents and register databases or tabular files; they ask natural-language questions and get answers grounded in their data.

REPO_CONTEXT.md

@@ -4,6 +4,26 @@ Orientation file for future Claude Code sessions. Cross-reference `ARCHITECTURE.
 
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+## Product vision — Data Eyond, your AI data scientist
+
+Data Eyond is positioned as an *AI data scientist* that supports business analytics. It is built around the **CRISP-DM** framework (Business Understanding → Data Understanding → Data Preparation → Modeling → Evaluation → Deployment) — the agent works through data problems the way a real analyst would, not as a one-shot Q&A bot.
+
+**Target users:**
+- **Executives** — deep-dive into their own data and extract insight to drive business decisions without needing a data team in the loop.
+- **Data analysts / scientists** — offload routine analysis so they can focus on heavier work.
+
+**Envisioned user flow:**
+1. **Discovery interview** — a short conversation with a Data Eyond *interview agent* that draws out goal, business context, and what the user is actually trying to learn (CRISP-DM Business Understanding).
+2. **Connect data** — DB connection or file upload (DB, CSV, XLSX, Parquet, documents).
+3. **Ask Data Eyond** — natural-language analytical question.
+4. **CRISP-DM-structured analytical response** — exportable as a **presentation deliverable** or a **notebook-style report**.
+
+North star: less "chatbot over a database", more "junior data scientist that hands back a polished, decision-ready deliverable."
+
+The current repo (Phase 2, below) is the *foundation* — IntentRouter → QueryPlanner → Executor → ChatbotAgent gives us a reliable structured-query spine. The next evolution is the agentic layer that turns this into an end-to-end CRISP-DM workflow (see *Roadmap — agentic evolution* further down).
+
+---
+
 ## TL;DR
 
 FastAPI multi-agent backend for data analysis. Users upload documents and register databases / tabular files; they ask natural-language questions and get answers grounded in their data, streamed via SSE.