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Co-Founder / CEO Architecting for Trust: A Deep Dive into Our Privacy-First RAGaaS Pipeline

Retrieval-Augmented Generation (RAG) is revolutionizing how businesses interact with their data, transforming vast internal knowledge bases into intelligent, conversational AI assistants. But this power comes with a critical question: where does your sensitive data go when you ask a question? With most RAG solutions, the answer is unsettling—it's sent to a third-party API, outside your control.

Sebastien Peterson

Co-Founder / CEO

Co-Founder / CEO Building a Production-Grade RAG System: Infrastructure, Scalability, and Costs

Retrieval-Augmented Generation (RAG) has taken the AI world by storm. The concept is beautifully simple: connect a powerful Large Language Model (LLM) to your private data, allowing it to answer questions with up-to-date, relevant, and verifiable information. Building a proof-of-concept (PoC) in a Jupyter notebook can feel like magic, often taking just a few hours and a couple of libraries. However, the journey from that magical PoC to a robust, production-grade RAG system that serves thousa…

Sebastien Peterson

Co-Founder / CEO

Co-Founder / CEO Optimizing Your RAG System: A Deep Dive into Chunking Strategies

Retrieval-Augmented Generation (RAG) is revolutionizing how we build AI applications, blending the vast knowledge of Large Language Models (LLMs) with the precision of external data sources. The result? AI that can provide accurate, up-to-date, and contextually relevant answers. But the magic of RAG doesn't just happen; it relies on a crucial, often overlooked, foundational step: chunking.

Sebastien Peterson

Co-Founder / CEO

Co-Founder / CEO Anatomy of a RAG Pipeline: From Vector Databases to Answer Synthesis

A Retrieval-Augmented Generation (RAG) pipeline is a system that enhances Large Language Models (LLMs) by connecting them to external knowledge bases. Its core anatomy consists of five stages: loading raw data, chunking it into manageable pieces, indexing it by converting the chunks into numerical representations (embeddings) and storing them in a vector database, retrieving the most relevant chunks based on a user's query, and finally, synthesizing a human-like answer by feeding the query and …

Sebastien Peterson

Co-Founder / CEO