Agentic AI Designer

Access any unstructured data using connectors for any document stores – including Sharepoint, SFTP, S3, Dropbox, Box, Google Drive and more – and use built-in agentic chunking to deliver AI-ready data.

Orchestrate across Nexla’s built-in PDF parser and advanced parsers from external providers including AWS Textract, Tesseract OCR, and Unstructured.

Nexla’s agentic chunking preserves semantic structure by identifying key sections, headings, and relationships using domain-specific chunking strategies. It balances LLMs and deterministic rules-based processing to achieve the best quality, price, and performance with linear scale.

Enhance your final outcomes with metadata infused embedding generation for powerful citations and data freshness management.

Embed data into the vector database of your choice without coding  including Pinecone, Weaviate, and MongoDB with new connectors delivered under 24 hours.

Choose the best embedding algorithm for data from OpenAI, Cohere, NIMs, Voyage, and more, or add your own embedding algorithm in minutes.

Use built-in reranking or any 3rd party reranker to identify the best context for each query with the option to leverage NIM gpu acceleration.

Leverage built-in LLM result evaluation to help prevent hallucinations, or build test cases.

Evaluate and use the best models for each project. Choose from prebuilt connectors to over 20 models, or easily integrate to new models, including ones you run privately. Compare models side-by-side to help choose the best model based on quality, cost, and performance. Then use the best model and other technologies for each project in production.

Control all access to data via data products to encrypt or remove sensitive data and prevent data leaks. Leverage service key-based access ensures RAG flow can only access allowed data based on permissions.

Implement integration and RAG to support agents and assistants, and embed Nexla into your products via Nexla’s APIs to manage Nexla and invoke pipelines.