Ondexx’s new API provides a technical framework that ensures accuracy and maintains permission structures for enterprise data used in internal AI solutions.
Ondexx today announced the availability of its Ondexx Content Extraction API, launched in conjunction with the rollout of its Ondexx 6.0 release. Engineered for enterprise operations across North America, the new API provides a secure framework to ensure data accuracy and preserve structural context at scale, by feeding verified business knowledge directly into internal AI initiatives.
The utility of AI depends on the data it consumes. Our API provides a secure pipeline for your enterprise knowledge, ensuring internal AI tools provide outputs that are accurate, reliable, and secure.”
— Paul Quigley, CEO, Ondexx
As enterprises adopt LLM’s and other AI tools, the accuracy of AI outputs is increasingly dependent on the structure and reliability of the source data. The Ondexx API addresses the ’garbage in, garbage out’ challenge by allowing systems to access a ‘single source of truth’ across all enterprise content and information contained within the Ondexx environment, ensuring that AI-driven insights are based on verified and compliant information. The REST-based interface is designed to provide organizations with a secure, automated method for transferring internal business knowledge into artificial intelligence (AI) models and Retrieval-Augmented Generation (RAG) workflows.
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“The utility of AI is directly tied to the structure and quality of the data it consumes,” said Paul Quigley, CEO of Ondexx. “This API functions as a secure pipeline, allowing corporate intelligence to move from our platform into any enterprise system that requires it”.
Technical Specifications and Enterprise Utility:
• RAG Optimization: The API extracts structured content alongside rich metadata to provide context for AI models, with the goal of reducing hallucinations.
• Integration Standards: Utilizing a RESTful architecture, the tool integrates with existing enterprise ecosystems and machine learning pipelines.
• Governance and Security: Authentication is managed via OAuth 2.0 and OpenID Connect (OIDC), ensuring that data remains within governed enterprise boundaries during extraction.
• Operational Consistency: The automated extraction process is intended to replace manual data synchronization between departments, maintaining information consistency across disparate systems.
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Architectural Framework and Data Continuity
Unlike traditional data transfer methods that extract raw text into disconnected files, the Ondexx Content Extraction API preserves the original structure and meaning of enterprise information. By maintaining complete document relationships during transfer, the interface removes the need for technical teams to manually rebuild data frameworks, lowering the risk of inaccuracies and preserving organizational knowledge.
“To deploy internal artificial intelligence systems that actually work, organizations must feed them accurate data,” said Dr. Anthony ‘Tony’ Rhem, CEO of AJ Rhem & Associates. “This API ensures that downstream workflows draw strictly from a verified, permission-governed environment, automatically preserving correct user permissions and complete document structures. This structural continuity stops AI tools from guessing or generating inaccurate responses, giving teams reliable answers without forcing IT departments to constantly clean up messy data files.”
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