IgnitionRAG
Multimodal RAG platform, from POC to production in minutes
IgnitionRAG launched on April 28, 2026 with 83 votes, #559 of 21,248 launches that month and more than 95% of that year's launches.
What it does
IgnitionRAG is the managed RAG backend that replaces €50-200K consulting projects. Ingest any document (PDF, DOCX, images), search with hybrid retrieval + reranking, deploy AI agents as embeddable widgets in minutes, not months. API/SDK/MCP for devs, no-code dashboard for business teams. BYOK: bring your own LLM keys, zero markup. GDPR-compliant, hosted in France, self-hostable. Free tier available.
Transformez la connaissance de votre entreprise en assistants IA prêts pour la production en quelques semaines. Ingestion, recherche, agents, évaluation et observabilité — intégrés, pour arrêter de reconstruire le RAG.
IgnitionRAG donne aux équipes innovation tout le nécessaire pour transformer vos documents en assistant de production : ingestion, recherche, agents, évaluation et observabilité, prêts à l'emploi. Arrêtez de reconstruire la même stack RAG à chaque projet. Connectez vos propres clés OpenAI, Anthropic, Mistral ou Azure OpenAI. Vous payez les providers directement. Les équipes innovation subissent la pression de livrer l'IA vite. Pourtant la plupart des projets passent des mois à reconstruire la même tuyauterie avant qu'un seul utilisateur métier ne voie de la valeur. Des mois d'ingénierie avant que le premier utilisateur métier ne voie de la valeur. Les budgets fondent, les décideurs…from ignitionrag.com
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SpRAG – Open-source RAG implementation for challenging real-world tasks2024 · github.com · ▲69Hey HN, I’m Zach from Superpowered AI (YC S22). We’ve been working in the RAG space for a little over a year now, and we’ve recently decided to open-source all of our core retrieval tech. spRAG is a retrieval system that’s designed to handle complex real-world queries over dense text, like legal documents and financial reports. As far as we know, it produces the most accurate and reliable results of any RAG system for these kinds of tasks. For example, on FinanceBench, which is an especially challenging open-book financial question answering benchmark, spRAG gets 83% of questions correct,…
- FastRAGDec 2025 · fastrag.live · ▲2
Hey HN, I built this because I was tired of setting up the same RAG pipeline for every side project. The Problem: Most RAG tutorials give you an answer but don't show where it came from. Hallucinations are a dealbreaker for real apps. * The Solution:* I built a pipeline using Pinecone metadata to map vector chunks back to the original PDF page/text. The UI highlights the source snippet when the AI answers. Stack: Next.js 14 (App Router) Pinecone (Serverless) LangChain (Streaming) Supabase (Auth) Demo: https://www.fastrag.live Happy to answer questions about the chunking…

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