Optimizing Search and Data Processing Through Self-Hosted SLMs

Beyond Text Only LlamaIndex Retriever with Superlinked’s Mixture of Encoders | Vector Space Day

From BM25 to Mixture of Encoders | Haystack EU 2025

Stories from the AI Search Frontier | Vector Space Day

Natural Language Search on Semi-Structured Data

Mixture of Encoders A Vector Native Approach to Search - Filip & Ben | Berlin Buzzwords 2025

Conversational Jobs & Hotels search with metadata-aware embeddings

Metadata aware Vector Embedding Models | AI User Conference 2025

Superlinked Framework Overview

Metadata-aware Vector Embedding MoE Models | Haystack Conf 2025

Why Superlinked #3: Handle preferences perfectly in natural language

Why Superlinked #2: Multi-attribute embedding

Why Superlinked #1: Your users expect better search

Making Vector Search Work With Complex Data | AI User Group

Redis for startups 2024 | Redis Released

The Future of Vector DB | GenAI Summit 2024

Vector Search Over Complex Semi Structured Data | MongoDB.local London

The “Bitter Lesson” for Retrieval | Bain Capital Ventures

Ben Gutkovich - Making Large Language Models work in the enterprise | The AI Summit London 2024

How to Take Control of Your Rag Results // Daniel Svonava // AIQCON

Your Job in 2024: Turning Data Into Vectors | Big Data LDN 2023

Vector Search Over Complex Semi-Structured Data

Speak Your Query: Superlinked’s Natural Language Query Feature | AI-Powered Search

Beyond Text: How Superlinked Embeddings Outperform Traditional LLMs

RAG at Scale: Unlock Production-Level Performance in Your AI Systems

10x Your ML Pipeline with Multimodal Transformers | Image-Text Retrieval Breakthrough

RAG Evaluation Secrets: 4 Steps to Supercharge Your AI

Understanding RAG: A Quick Guide to Enhancing Language Models

Build RAG on a dataset full of numbers with Mór Kapronczay, Lead ML @ Superlinked

Sparse Data? No Problem! 💡 Hack Your Way to 20% Better Recommendations

The AI Behind Your Next Binge: Recommender Systems 101

Vector Embeddings Explained: How to Build Spotify-Level Recommendations in 5 Minutes

Beyond Bag-of-Words: Harnessing Node2Vec, GraphSAGE, and LLMs for Enhanced Node Embeddings

Knowledge Graphs & Embeddings: The Ultimate Language AI Combo for Unbeatable Results 🏆

How Knowledge Graph Embeddings (KGEs) Uncover Hidden Connections in Relational Data

Boost Your Conversational AI Performance with This Multi-Agent RAG Trick! 📈

Revolutionize Your RAG Evaluations with RAGAS: The Ultimate Guide

Unleash the Power of RAG Retrieval: Game-Changing Insights for Data Scientists

Is Your RAG System Underperforming? 😫 Boost Results with Hybrid Search & Semantic Reranking NOW! 🏆

Build AI in Your Browser with Just TypeScript?! 🤯 Game-Changing Tutorial!

The Future of RecSys: An Engineers Approach to Building Real-Time RecSys with Vectors

MLOps Best Practices [Panel @ AI Summit London]

CONF42 ML: How to run vector embedding-powered apps in production

Data Science Festival: Build better products with user embeddings