Agentic AI for Automated Extraction of Enterprise Asset Management Data

Objective

A U.S.-based defense-focused enterprise asset management company partnered with NLP Logix to build an agentic AI system that turns complex technical manuals into structured data. Many of these manuals are long and detailed and employees had to read each document and manually enter the data into spreadsheets.

The goal was to reduce manual work, lower costs, and keep the high level of accuracy required in defense environments.

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Challenge

Before automation, analysts spent about 12 hours reviewing each document with a cost averaging about $600 per file. This process was slow and hard to scale as document volumes grew. Technical maintenance manuals are tedious to process due to their length ranging from 35 up to 500 pages, inconsistent formats, and strict defense-sector audit requirements.

Solution

NLP Logix deployed a secure, multi-pass agentic AI extraction pipeline built on the LOGIXFORGE LLM Kit accelerator framework.

Agentic AI Workflow

The system uses a structured and iterative reasoning to:

  • Analyze document structure and table of contents
  • Identify task-relevant sections
  • Extract tasks, parts, tools, and consumables
  • Re-pass the document to close recall gaps
  • Output structured, spreadsheet-ready data with page-level traceability

Human-in-the-Loop Validation

Designed so analysts can efficiently review highlighted source regions instead of entire documents, ensuring:

  • 100% final accuracy
  • Efficient correction of edge cases
  • Continuous improvement feedback

Flexible Deployment

  • Provider-agnostic LLM integration
  • Cloud or on-prem deployment
  • Optional BERT-based local extractor for constrained environments
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Results

The project produced a working extraction workflow and validated performance using a representative 35-page technical publication pilot. Results demonstrated that both the agentic LLM approach and the BERT based alternative can materially reduce manual effort, with clear, measurable tradeoffs between precision, recall, cost, and runtime. Human in the loop review of highlighted spans provides an auditable path to 100 percent final accuracy while also generating labeled feedback to improve automation in follow on phases.

  • 88 – 98% reduction in manual analyst effort
  • 90 – 99% cost reduction per document
  • Reduced processing time from 12 hours to 15 – 90 minutes
  • 98 – 100% precision and 100% recall with multi-pass agent
  • 100% precision and 58% recall with single-pass agent
  • 100% recall paired with human review with local BERT extractor
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Tech Stack

Generative AI & Orchestration

  • LLM Kit accelerator framework
  • Multi-pass LLM-based agent
  • Structured chain-of-thought prompting

Alternative Extraction Path

  • Lightweight BERT-based local model

Governance & Validation

  • Page-level span traceability
  • Precision/recall performance reporting
  • Auditable logging and guardrails
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