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Introduction:
Neum AI is an ETL platform for LLM data that enables companies to connect and synchronize data into vector stores in real-time. It provides a framework for building data infrastructure for Retrieval Augmented Generation (RAG) and semantic search. By utilizing various connectors, Neum AI creates data pipelines that transform structured and unstructured data into vector embeddings for search indexes. The platform is designed to scale to millions of vectors while ensuring they remain current as source data changes.
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Neum AI Product Information
What is Neum AI?
Neum AI is an ETL platform for LLM data, designed to help companies connect and synchronize data into vector stores in real-time. It provides a framework to build data infrastructure for Retrieval Augmented Generation (RAG) and Semantic Search. Neum AI offers connectors to create data pipelines that convert unstructured and structured data into vector embeddings for search indexes. The platform scales to process millions of vectors and keeps them up-to-date, even as the underlying data changes.
How to use Neum AI?
Use Neum AI to connect to various data sources, transform data into vector embeddings, store them in vector databases, and maintain synchronization. The platform provides open-source SDKs for developing pipelines locally and deploying them to the Neum AI cloud.
Neum AI's Core Features
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Data connectors for various sources, embedding models, and vector databases
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Open-source SDKs for composing data flows
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Managed platform for scaling data pipelines
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Real-time data synchronization
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Built-in retrieval informed by data organization and metadata
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Observability tools for monitoring data syncing
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Retrieval evaluation tools
Neum AI Use Cases
#1
Building Retrieval Augmented Generation (RAG) pipelines
#2
Creating semantic search indexes
#3
Keeping chatbots up-to-date with accurate information
#4
Real-time data embedding and indexing for RAG
FAQ from Neum AI
What is Neum AI? +
Neum AI is an ETL platform for LLM data that enables companies to connect and synchronize data into vector stores in real-time.
What does Neum AI help with? +
Neum AI assists in building data infrastructure for Retrieval Augmented Generation and semantic search.
What connectors are available? +
Neum AI provides connectors for various data sources, embedding models, and vector databases. Additionally, users can implement custom connectors using the open-source framework.
How can I test and deploy my pipelines? +
You can develop and run data pipelines locally using open-source SDKs, then deploy those same pipelines directly to the Neum AI cloud.
Neum AI Pricing
Related Model Comparison Pages
Use these comparison pages to understand the trade-offs between the models most relevant to Neum AI.
Compare Amazon Nova Pro and Amazon Nova Lite across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for tool-augmented workflows versus tool-augmented workflows.
Compare Amazon Nova Lite and Amazon Nova Micro across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for tool-augmented workflows versus tool-augmented workflows.
Compare Amazon Nova Lite and Mistral Medium 3 across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for tool-augmented workflows versus tool-augmented workflows.
Compare Amazon Nova Lite and Mistral Small 3.1 (25.03) across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for tool-augmented workflows versus cost-efficient scale.
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