Fix Bad RAG Answers at the Source
Shelf cleans and enriches unstructured data to deliver accurate GenAI answers.
Research from the Applied AI Institute shows that the top 5 out of 7 RAG failure points are caused by data issues.
What is holding you back from scaling your RAG initiatives?
The following RAG approaches fail to address underlining unstructured data quality issues
Does not address underlying data issues nor preserves context.
Limited to basic chunking without quality controls or data enrichment.
Tuning of models can’t substitute for data quality assurance.
goes straight into your Data product/AI
trusted high-quality data fuels your AI
“No matter what our data science teams have tried thus far, they haven’t been able to reliably improve the answer quality of our GenAI POCs. Shelf’s platform is the piece we’ve been missing!”
– Head of Automation at Leading Manufacturing Company What’s inside
3 immediately actionable RAG strategies for improving answer quality so you can deploy RAG with more answer transparency, confidence and impact.
Created by data experts who have 40 years of experience and have processed over 100M pieces of content for some of the world’s best brands, including Amazon, Lufthansa and the Mayo Clinic.
Overview of Shelf’s RAG solution
Easily integrate with prebuilt connectors or configure custom ones to bring all your unstructured data into Shelf.
Run prebuilt and custom quality algorithms to detect and resolve issues impacting RAG performance.
Leverage over 20 prebuilt and custom enrichment capabilities to add context and business meaning to your content.
Design and manage data preparation pipelines tailored to your RAG use case.
Access Shelf through our developer portal, APIs, and SDKs for seamless integration into your workflows.
Track downstream RAG results in real time and proactively address emerging data quality issues.
Get started in 3 easy steps
RAG data
enrich with context
monitor performance
