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fix: corriger les majuscules dans les statuts des projets et mettre à jour les descriptions des projets

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Arthur Danjou
2026-02-16 20:22:11 +01:00
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content/projects/hackathon-natixis.md
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@@ -6,7 +6,7 @@ description: An intensive 4-week challenge to build an AI-powered data assistant
shortDescription: A team-based project building an NL-to-SQL agent with Nuxt, Ollama, and Vercel AI SDK.
publishedAt: 2026-03-07
readingTime: 4
status: completed
status: Completed
tags:
- Nuxt
- Ollama
@@ -18,16 +18,16 @@ icon: i-ph-database-duotone
## The Challenge
Organized by **Natixis**, this hackathon followed a unique high-intensity format: **three consecutive Saturdays** of on-site development, bridged by two full weeks of remote collaboration.
Organized by **Natixis**, this hackathon followed a high-intensity format: **three consecutive Saturdays** of on-site development, bridged by two full weeks of remote collaboration.
Working in a **team of four**, our goal was to bridge the gap between non-technical stakeholders and complex financial databases by creating an autonomous "Data Talk" agent.
## Core Features
### 1. Data Engineering & Schema Design
Before building the AI layer, we had to handle a significant data migration task. I led the effort to:
Before building the AI layer, we handled a significant data migration task. I led the effort to:
* **ETL Pipeline:** Convert fragmented datasets from **.xlsx** and **.csv** formats into a structured **SQL database**.
* **Schema Optimization:** Design robust SQL schemas that allow an LLM to easily understand relationships (foreign keys, indexing) for accurate query generation.
* **Schema Optimization:** Design robust SQL schemas that allow an LLM to understand relationships (foreign keys, indexing) for accurate query generation.
### 2. Natural Language to SQL (NL-to-SQL)
Using the **Vercel AI SDK** and **Ollama**, we implemented an agentic workflow:
@@ -48,8 +48,8 @@ Data is only useful if it’s readable. Our Nuxt application goes beyond raw tab
## Impact & Results
This project demonstrated that a modern stack (Nuxt + local LLMs) can drastically reduce the time needed for data discovery. By the final Saturday, our team successfully presented a working prototype capable of handling multi-table joins and generating real-time financial dashboards from simple chat prompts.
This project demonstrated that a modern stack (Nuxt + local LLMs) can drastically reduce the time needed for data discovery. By the final Saturday, our team presented a working prototype capable of handling multi-table joins and generating real-time financial dashboards from simple chat prompts.
---
*Curious about the ETL logic or the prompt structure we used? I'd be happy to discuss how we optimized the LLM's SQL accuracy.*
*Curious about the ETL logic or the prompt structure we used? I can share how we optimized the LLM's SQL accuracy.*