Natural Language Processing for Document Intelligence

Deployment of NLP capabilities that automate the analysis of unstructured text — extracting entities, classifying documents, and generating summaries to accelerate human review and decision-making workflows.

PRACTICE

Applied AI & Data Analytics

CLIENT PROFILE

Federal agency processing large volumes of unstructured text — reports, correspondence, intelligence products, or regulatory filings — needing automated extraction, classification, and summarization

Challenge

Analysts manually reviewing thousands of documents for relevant information
No automated capability for entity extraction, topic classification, or cross-referencing
Valuable information locked in unstructured text formats not accessible to search or analytics

Approach

Implement document ingestion pipeline with OCR for scanned documents
Deploy named entity recognition (NER) and relationship extraction models
Build classification models for automated document routing and prioritization
Create searchable knowledge base with semantic search and summarization capabilities

Typical Outcomes

Automated extraction and classification reducing manual review time
Searchable knowledge base enabling rapid information retrieval across document collections
Analyst focus shifted from document processing to analysis and decision-making

Procurement Paths

NASA SEWP V for NLP platform infrastructure and GPU compute
GSA MAS for AI/ML development services
CIO-CS (NITAAC) for enterprise AI solutions

Partner Technology Examples

Elastic
NVIDIA
AWS GovCloud
Microsoft Azure

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