How NTT DATA Transforms Enterprise Document Parsing with LlamaIndex

2026-02-04 · Source: LlamaIndex · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, short

Summary

NTT DATA, a global IT consulting firm, is leveraging LlamaIndex to transform its enterprise document parsing and AI development processes. The company's global AI office, led by Manuel, focuses on strategic alliances with hyperscalers and innovative startups to enhance its AI capabilities. NTT DATA aims to become a fully AI-enabled company by 2027, integrating AI into both client solutions and internal operations. LlamaIndex is crucial for streamlining document parsing, data structuring, and enhancing Retrieval Augmented Generation (RAG) solutions, leading to increased efficiency and accuracy. Specific benefits include improved indexing methods like recursive retriever and surrounding context window, which maintain document structure and hierarchy, particularly for complex regulatory documents. This adoption has accelerated development cycles, allowing teams to focus on innovative features rather than foundational components.

Key takeaway

For NLP Engineers developing RAG solutions or handling complex document parsing, integrating LlamaIndex can significantly accelerate development cycles and improve accuracy. You should explore its diverse indexing methods, such as the recursive retriever, to maintain document structure and enhance information retrieval efficiency, especially for highly structured content like regulatory documents.

Key insights

LlamaIndex streamlines document parsing and RAG solutions, significantly boosting efficiency and accuracy in enterprise AI development.

Principles

• AI drives transformation for clients and internal operations.
• Strategic partnerships enhance AI capabilities and market reach.

Method

Utilize LlamaIndex's indexing capabilities, including recursive retriever and surrounding context window, to preserve document structure and hierarchy for effective data retrieval in RAG systems.

In practice

• Integrate LlamaIndex for chat bots and information retrieval.
• Apply LlamaIndex for complex regulatory document parsing.

Topics

• LlamaIndex
• Document Parsing
• Retrieval-Augmented Generation
• Enterprise AI Strategy
• AI Partnerships

Best for: NLP Engineer, AI Engineer, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by LlamaIndex.