Why does AI give irrelevant answers even after implementing RAG?

RAG systems retrieve information from a knowledge base, but if the underlying documents were never structured for machine readability, the AI retrieves the wrong context or misreads relevant sections. The accuracy of RAG depends heavily on how well source documents were preprocessed before being indexed, not just on the retrieval mechanism itself.

Why do human-readable document formats confuse AI systems?

Layouts and tables designed for human visual scanning do not map cleanly to the linear, element-based way AI models parse content. A table that is intuitive for a person to read can be interpreted by an AI model as disconnected or out-of-order data, leading to misinterpretation.

What is data structuring, and why is it a necessary step before AI adoption?

Data structuring is the process of breaking a document down into discrete elements such as headings, paragraphs, and tables, and converting them into formats like JSON or Markdown that AI systems can parse reliably. Without this preprocessing step, AI models are working from ambiguous input, which increases the likelihood of inaccurate output.

How does ComPDF AI convert documents into structured data?

ComPDF AI applies Intelligent Document Processing (IDP) to analyze a document's structure and extract its elements, generating structured output optimized for AI consumption. This removes the need for organizations to manually reformat documents before integrating them into AI workflows.

How does structured data reduce hallucinations in a RAG knowledge base?

When structured, high-quality data is integrated into a RAG knowledge base, the AI has cleaner, unambiguous context to retrieve from. This reduces the chance that the model fills gaps with plausible-sounding but incorrect information, which is what is commonly described as a hallucination.

What operational benefits can companies expect after adopting document AI-readiness practices?

Seminar attendees who work in daily document-heavy operations described the practical value of structuring data before AI adoption as directly relevant to their day-to-day workflows. Properly structured documents reduce the back-and-forth of correcting AI errors, which in turn lowers the operational overhead of maintaining an AI-assisted document workflow.

News & Events Archives

KDAN Open-Sources Core Products to Advance Enterprise AI Document Infrastructure and Data Sovereignty

KDAN has open-sourced core products ComPDF and DottedSign on GitHub with self-hosted deployment options, enabling enterprises to validate, deploy and scale AI-powered document workflows while maintaining data sovereignty and control over sensitive information.

ComPDF and DottedSign are now available on GitHub with self-hosted deployment options, enabling enterprises to validate, deploy and scale AI-powered document workflows while maintaining control of sensitive data

KDAN (TPEx: 7737), a global provider of AI document and data infrastructure, today announced the open-source release of core products ComPDF and DottedSign on GitHub, marking a major step in the company’s global strategy to support enterprise AI adoption through open-source access and commercial licensing.

[Event Recap] How KDAN is Helping Companies Transform Documents into AI-Readable Formats

KDAN JAPAN hosted a seminar on why AI misreads corporate documents and how data structuring with Intelligent Document Processing (IDP) reduces RAG hallucinations and improves AI accuracy.

Making corporate documents AI-readable requires converting human-formatted files—especially PDFs with complex tables and layouts—into structured data formats like JSON or Markdown before feeding them into AI systems. This preprocessing step, known as Intelligent Document Processing (IDP), is what KDAN JAPAN’s June 2026 seminar in Tokyo focused on, addressing why generative AI tools so often misread enterprise documents.

KDAN 2025 Year in Review: Building the Next Growth Curve Through Stability, Openness, and Collaboration

2025 was a year of challenge, transformation, and steady progress for KDAN.

Over the past year, we completed a fundamental shift in our product foundation. Our offerings evolved from a collection of standalone tools into integrated workflows designed to address real enterprise needs in a practical and sustainable way. Throughout this transition, we listened closely to enterprises and developers, placing security, compliance, and flexibility at the core of our product design. These priorities guided comprehensive upgrades across our technical architecture, product workflows, and collaboration models. At the same time, KDAN officially activated its global partner-led growth engine, while continuing to deepen the overall value we deliver to enterprise customers.

KDAN Product Rebrand: Building an Intelligent, AI-Driven Document Ecosystem

In today’s fast-evolving digital landscape, enterprises face challenges far beyond task automation or document efficiency. Real transformation happens when technology connects people, data, decisions, and workflows into one intelligent and reliable network.

This October, KDAN announced a major product and brand restructuring, anchored around our Digital Enablement Ecosystem, powered by an AI document tech stack focused on a modular SDK/API architecture. The new ecosystem spans the entire document workflow — from creation and automation to agreement and governance, with AI integrated across every stage, symbolizing KDAN’s transformation from a single-point solution provider into a strategic technology partner helping enterprises build intelligent, secure, and automated document infrastructures that drive digital advancement.

KDAN Launches New Brand Identity, Redefining the Role of Intelligent Documents in the AI Era

IRVINE, Calif., October 28, 2025 — KDAN (TPEx: 7737), a leading global Software-as-a-Service (SaaS) provider, today unveiled its new brand identity, unifying all business solutions under one vision to build an AI-driven intelligent document ecosystem for enterprises.