Nexla Open Sources its Agentic Chunking Technology to Improve AI Accuracy for All

Agentic chunking contribution is first step in open sourcing agentic AI technology to spur innovation and accelerate enterprise-grade GenAI

Nexla, a leader in AI-powered integration for data and AI, is contributing key innovations from its cutting-edge agentic AI framework to the open source community, reinforcing its commitment to advancing enterprise-grade AI technology. Building on years of technological leadership, Nexla has released its groundbreaking advancements in agentic chunking to developers worldwide, empowering organizations to create more accurate GenAI-powered agents and assistants while accelerating industry-wide innovation.

Nexla has the industry’s first AI-powered integration platform that handles today’s overwhelming data variety, replacing endless connectors, diverse formats, and infinite schemas with AI-ready data products. With Nexla, you can integrate any document, data, app, or API, create AI-ready data products, and deliver GenAI projects without coding, up to 10x faster than the alternatives.

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Nexla uses AI to connect, extract metadata, and transform source data into human-readable data products, called Nexsets, that can be shared in a built-in marketplace for true data reuse and governance. Nexla’s agentic AI framework lets companies implement agentic RAG for agents and assistants without coding, using LLMs during each stage to improve accuracy. For example, Nexla can get context from multiple data products, use a unique algorithm to rank, prioritize, and eliminate data, and then combine the context with a rewritten query and submit it to just about any LLM.

Agentic chunking represents the next evolution of document processing for Retrieval-Augmented Generation (RAG), providing AI engineers with an intelligent, structured, and scalable way to break down complex documents for optimal retrieval and generation.

Agentic chunking has delivered the following benefits over other chunking techniques across internal tests and production deployments:

• Smarter document understanding: Instead of blindly splitting text into fixed-sized chunks, agentic chunking treats documents as structured knowledge, identifying key sections, headings, and relationships.
• Precision-driven efficiency: Uses LLMs like GPT-4o only where they add real value—detecting and classifying headings—while relying on smaller models and deterministic rule-based processing for everything else to achieve the b*********-performance.
• Improved retrieval and accuracy: By preserving hierarchical relationships and semantic structure, chunks retain essential context, leading to significantly better responses from RAG-based systems.
• Enterprise-grade: Scales linearly with document size and has proven its reliability in production deployments.
• Domain adaptability: Incorporates domain-specific chunking strategies beyond generic embeddings, ensuring AI-powered retrieval works optimally for financial reports, technical manuals, legal documents, and more.

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“Companies who have deployed GenAI assistants and agents often cite data quality and AI accuracy as two of their top challenges. They’re related: bad data leads to bad outcomes and AI hallucinations,” said Saket Saurabh, Nexla Co-founder and CEO. “Our agentic AI framework has dramatically improved AI accuracy and scale for our customers. But we believe the best solution is to solve these problems together as an industry by jointly contributing to open source. Open sourcing agentic chunking is just the first step. We’re excited to work with other vendors, and to release more of our agentic AI technology to help companies get to even higher quality data and outcomes.”

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