Y42 enables data teams to transform siloed data sources into LLM-ready embeddings in Snowflake or BigQuery, replacing ad-hoc scripts with modular workflows to deliver high-quality model inputs.
PROBLEM STATEMENT
Despite investing in cutting-edge LLM technology, many organizations struggle with inconsistent results due to ad-hoc, non-standardized data preparation processes.
Without a robust, production-ready pipeline for data extraction, cleaning, and vectorization, even advanced LLMs fail to deliver on their promise, leading to hallucinations, irrelevant responses, and poor performance when put to the test with real users.
WHAT WE DO
Unify, process and vectorize diverse data sources, transforming your data warehouse into a vector store for reliable LLM performance in production.
Extract data from documents, websites or APIs using Python and 200+ pre-built connectors.
Clean, chunk, and tokenize text with modular workflows, preparing content for vectorization.
Generate, store, and manage embeddings for efficient retrieval and LLM applications.
Pull data from anywhere with 200+ pre-built connectors or custom Python code. Y42’s managed infrastructure handles data extraction, parsing and loading so you can enhance your model with a variety of inputs.
Develop and visualize your pre-RAG pipeline using reusable data assets organized in a directed acyclic graph. Eliminate ad-hoc Jupyter notebooks to ensure consistent, high-quality model inputs.
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Schedule regular updates of fresh vector embeddings, materialized directly in your data warehouse. Provide a unified embeddings catalog with rich metadata, facilitating clear handoffs and understanding for LLM application teams.
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Get personalized insights on ELT pipelines for your large language models.
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