Carbon and Nango are both data integration platforms, but Carbon's specialized features for unstructured data processing and retrieval make it a more suitable choice for companies building generative AI applications with retrieval augmented generation (RAG) capabilities.

Data Connectors and Ingestion

With over 25 pre-built data connectors, Carbon excels in seamlessly syncing user data from diverse sources, including Slack, Google Drive, Dropbox, OneDrive, and Box. This extensive range of connectors is particularly advantageous for retrieval augmented generation applications, as it facilitates easy integration of unstructured data. While Nango also offers a catalog of pre-built integrations, Carbon's focus on unstructured data ingestion gives it an edge in powering generative AI use cases. Carbon's ability to set custom sync schedules for each connected data source further enhances its flexibility and efficiency in data management.

Processing and Vectorization

Carbon's processing and vectorization capabilities are tailored for optimal performance with large language models, making it particularly effective for retrieval augmented generation. The platform offers:

• Cleaning, chunking, and vectorizing of ingested content to enhance LLM performance

• Flexibility in selecting embedding models and chunking strategies for effective content indexing

• The ability to retrieve chunks and embeddings for content from any data source with a single API call, streamlining the retrieval process

These features enable companies to efficiently prepare and structure their data for use in generative AI applications. In contrast, Nango does not have any comparable vectorization and embedding generation capabilities specifically designed for retrieval use cases.

Search and Retrieval Features

Carbon's built-in enterprise-grade hybrid search combines semantic and keyword search capabilities, offering fine-grained control over weights and reranking. This feature enables precise retrieval of the most relevant content, leveraging optimized vector embeddings generated during the ingestion and processing stages. In contrast, Nango does not possess comparable semantic search and retrieval features, suggesting that Carbon may have an advantage in this crucial aspect of retrieval augmented generation for generative AI applications.

Data Security and Compliance 

Carbon prioritizes data security and compliance, offering robust measures to protect sensitive information in AI applications. The platform encrypts both credentials and content at rest and in transit, ensuring maximum security for customer data. Importantly, Carbon never trains models on customer data, maintaining strict data privacy. The company is fully SOC 2 Type II compliant, demonstrating its commitment to rigorous security standards.

While Nango has its own security measures, Carbon's specific focus on AI applications and its clear stance on not using customer data for model training gives it an advantage for companies concerned about data privacy in generative AI projects. Additionally, Carbon provides enterprise-level SLAs and 24/7 support from engineers via Slack, offering peace of mind for businesses building critical AI applications.