Dev Summit 2026

You collect your users' feedback in Condens during your research, but feel like the tagging is an endless task.
You've been assigned to a research started by someone else and struggle to explore data you didn't collect.
You simply look for ways to pace up your UX research workflow.
This demo will explore ways to fast-track your analysis using assisted tagging (within and between sessions) and automate the clustering of feedback to improve your research delivery.
Come visit us if you want to know more

In the retail industry, the accuracy of product to concept mapping is the backbone of customer experience. At Leroy Merlin France, this represents a massive operational challenge: over 5.7M product-concept pairs (out of 67M) currently show poor semantic alignment, leading to misclassified products. Furthermore, at search time, 15.8% of the customer terms remain unmapped to our Search & Pub Knowledge Graph, inducing lower recall and precision. Solving these issues are a massive business opportunity.
While dense embeddings became the standard about semantic alignment, they often lack the lexical precision needed to fix these specific business gaps. This led us to explore an alternative: **Sparse Encoders**, a solution designed to bridge our massive product catalog with the explicitness of Knowledge Graphs to reclaim these lost data points.
We will walk through the evolution of our approach: from traditional term expansion (SPLADE) to a custom model with a key innovation: **using KG concepts themselves as tokens**. I will share the "behind-the-scenes" of our experimentation, including:
* **The Technical Pivot**: Why we moved from generative Seq2Seq models to Masked Language Modeling (MLM) and finally settled on a Distillation strategy.
* **Implementation Hardships**: Deep dive into the challenges of GPU optimization, custom DataCollators, and refining loss functions to enforce sparsity without losing semantic depth.
* **Real-world Results**: How we achieved 99%+ sparsity while maintaining alignment for complex product categories.

Beyond the code, this talk is a feedback loop on **data quality and model refinement**. Whether you are working on search, recommendation or data classification, you will gain practical insights into leveraging Sparse Encoders to build more robust and interpretable data products.

This initiative introduces a robust, Python-based Generative AI solution engineered to safeguard a 18-million SKU marketplace from regulatory and brand safety risks. By automating the detection of non-compliant products and offensive content, this tool replaces inefficient manual keyword searches with a highly scalable, semantic validation engine.

The Challenge: The "Scale vs. Safety" Gap Following a massive catalog expansion in 2025, the organization experienced a surge in "Out of Scope" (OOS) listings. While illicit goods are centrally managed, two critical operational gaps emerged:

- Strategic Misalignment: Non-core products (e.g., smartphones, toys, or food) infiltrating a specialized DIY/Home Improvement marketplace.
- Brand Safety: Offensive content and hate speech hidden within product titles and descriptions.
- Inefficient Current State: Local Business Units (BUs) relied entirely on manual, unscalable keyword searches within the Mirakl platform to police the catalog.

The Solution: Hybrid AI Auditing Architecture To address these challenges, we developed a dual-approach architecture combining automated catalog screening with ad-hoc auditing tools:

1. Proactive Pipeline (Catalog-Wide Screening): A comprehensive, custom-built tool designed to validate the health and compliance of the entire country-level catalog, encompassing both published and pending (pre-publication) selection. This background process operates through a robust four-step automated workflow:
1.1 Initial Selection Filtering: Executes a broad sweep across the entire BU and supplier catalog, utilizing Regular Expressions (RegEx) and country-specific rules to rapidly identify and isolate products that are potentially Out of Scope (OOS).
1.2 Semantic Context Analysis: Leverages Vertex AI to conduct deep Natural Language Processing (NLP) on the isolated items. It evaluates full product titles and descriptions to understand their true semantic context (e.g., accurately distinguishing a prohibited "smartphone" from a permitted "smartphone repair tool").
1.3 OOS Classification: Categorizes the analyzed products with high precision, definitively flagging non-compliant items and separating them from legitimate, brand-safe merchandise.
1.4 Automated API File Generation: Seamlessly operationalizes the decisions by automatically generating ready-to-upload files (via API integration), eliminating manual data manipulation and enabling immediate catalog updates.

2. Reactive Pipeline (Targeted Auditing App): A custom Streamlit-based web application that empowers non-technical users to conduct deep, ad-hoc catalog audits through a four-step automated workflow:
2.1 AI Query Expansion: Leverages Gemini (Vertex AI) to dynamically expand a single seed keyword (e.g., "Barbie") into a comprehensive cluster of related terms (e.g., "dolls", "toys") to capture evasive listings.
2.2 Live Search Analysis: Audits front-end webpage results, capturing and evaluating exactly what the customer sees in real-time.
2.3 Semantic Validation Engine: A hybrid verification step combining BigQuery product data with Gemini’s Natural Language Processing (NLP) to ensure the product aligns with the BU’s strategic scope.
2.4 Operationalized Outputs: Automatically generates actionable files, including a Mirakl-formatted auto-rejection list for zero-GMV items, alongside a prioritized manual review queue for high-risk, revenue-generating products.

Impact & Current Results: The implementation has successfully transitioned the business from reactive compliance to proactive risk mitigation:
- Scale at Speed: Processes 274 SKUs per minute (approx. 3,300 SKUs in 12 minutes).
- Proven Output: To date, the system has screened 178,000+ products, analyzed 1,641 risk keywords, and successfully delisted 46,000+ non-compliant items.
- Precision & Revenue Protection: Maintains a False Positive Rate (FPR) of

How do you build a conversational AI platform that combines capabilities from multiple teams across an organization?

In this talk, we share how we designed, scaled, and governed a multi-assistant AI platform, from first integration to a reusable governance framework. Through concrete REX with Opus and Locus teams, we'll show how we went from case-by-case integrations to a clear model where any team can plug their AI capabilities into the platform.

We'll cover architecture decisions, integration patterns, and the prerequisites we now require before anything reaches production.

Designing a data architecture that can handle the **massive volume of a retail operation** requires more than just good tools; it requires **hardcore engineering rigor**.

At **Obramax**, we completely redesigned our data infrastructure, moving away from **isolated legacy databases** to a **unified, event-driven model** hosted on the **ADEO Group's DFDP platform**.

This session dives deep into the **engine room of our new Data Analytics platform**.

We will demonstrate our **aggressive automation approach** to building a **Medallion Architecture**.

You will see how we use **Apache Kafka** to capture **high-volume events** *(from SAP, VTEX, etc.)* and land them in our **Bronze layer**.

From there, we will showcase our strict **GitOps workflow using Terraform and dbt (Data Build Tool)** to automatically refine data into the **Silver layer**, and finally aggregate it into **business-ready Gold layer datasets**.

> Come see how we **automated the entire lifecycle of a Kafka event**
> from **system integration** to **BigQuery dashboard**
> with **zero-touch provisioning and maximum scalability**.

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# 🎯 Key Takeaways

Participants will learn:

### ⚙️ Event Streaming Data Platform

A deep dive into building a **Medallion Architecture (Bronze, Silver, Gold)** using **event streams on Google BigQuery**.

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### 🧱 Engineering Data Pipelines at Scale

How to combine:

- **Apache Kafka**
- **dbt (Data Build Tool)**
- **Terraform**

to build **bulletproof, scalable data pipelines** on the **ADEO DFDP platform**.

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### 🔄 GitOps Data Infrastructure

Practical **GitOps strategies** for achieving:

- **zero-touch data infrastructure provisioning**
- reproducible data pipelines
- automated platform governance

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# 🏗 Architecture Themes Covered

- Event-Driven Architecture
- Apache Kafka
- dbt (Data Build Tool)
- Terraform
- GitOps for Data Platforms
- Google BigQuery
- Data Lakehouse Pipelines

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# 👨‍💻 Target Audience

This talk is ideal for:

- Data Engineers
- Platform Engineers
- Integration Engineers
- Data Infrastructure Teams
- DevOps / Platform Engineering Teams

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# 🧭 Why This Talk Matters

Modern data platforms require **engineering discipline at scale**.

Retail environments generate **massive volumes of operational events**, and traditional batch pipelines cannot keep up with the **speed and reliability required for real-time analytics**.

By combining **event streaming, infrastructure-as-code, and GitOps practices**, Obramax built a **fully automated data platform** capable of transforming raw operational events into **business-ready analytical datasets**.

This session shares the **engineering patterns and automation strategies** that make this architecture scalable and maintainable.

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# 🏢 Case Study

**Company:** Obramax

**Architecture Stack:**

- Apache Kafka
- ADEO DFDP Platform
- Google BigQuery
- dbt (Data Build Tool)
- Terraform
- GitOps Workflows
- Event-Driven Data Pipelines