List of SearchBlox SearchAI PreText NLP Customers
Glen Allen, 23060, VA,
United States
Since 2010, our global team of researchers has been studying SearchBlox SearchAI PreText NLP customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased SearchBlox SearchAI PreText NLP for Natural Language Processing from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using SearchBlox SearchAI PreText NLP for Natural Language Processing include: Defense Advanced Research Projects Agency, a United States based Government organisation with 220 employees and revenues of $4.12 billion, Federal Student Aid, a United States based Government organisation with 1400 employees and revenues of $350.0 million, United States Department Of Justice Antitrust Division, a United States based Government organisation with 1200 employees and revenues of $233.0 million and many others.
Contact us if you need a completed and verified list of companies using SearchBlox SearchAI PreText NLP, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight | Insight Source |
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Defense Advanced Research Projects Agency | Government | 220 | $4.1B | United States | SearchBlox Software | SearchBlox SearchAI PreText NLP | Natural Language Processing | 2018 | n/a | In 2018, Defense Advanced Research Projects Agency implemented SearchBlox SearchAI PreText NLP for Natural Language Processing. The deployment concentrated on indexing and searching technical and program level research content to accelerate R&D knowledge discovery across defense research programs in the United States. The SearchBlox SearchAI PreText NLP implementation emphasized document metadata extraction and automated content prompting to improve semantic retrieval and relevance ranking. Configuration patterns aligned with Natural Language Processing platforms were used, including taxonomy driven metadata tagging, entity extraction, and full text indexing to support contextual search and prompt based content enrichment. Operational coverage targeted program offices and research teams working with technical program level artifacts, with workflows focused on knowledge discovery and research reuse. Governance elements inferred from the implementation included metadata stewardship and structured query workflows to preserve research context and control access to indexed research content. The rollout is referenced publicly as part of SearchBlox government customer usage to speed R&D knowledge discovery, and the presence of SearchAI PreText NLP for document metadata and content prompts is an inferred usage based on SearchBlox's NLP automation platform and product suite rather than a named module in a public case study. | |
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Federal Student Aid | Government | 1400 | $350M | United States | SearchBlox Software | SearchBlox SearchAI PreText NLP | Natural Language Processing | 2020 | n/a | In 2020, Federal Student Aid implemented SearchBlox SearchAI PreText NLP to support search and knowledge discovery across program and policy documents. The deployment targeted education and citizen services content held by the agency, and the implementation explicitly used SearchBlox SearchAI PreText NLP as the Natural Language Processing application for corpus-wide semantic indexing and query understanding. SearchBlox SearchAI PreText NLP was configured to surface semantic search results, perform entity extraction, and enable document classification and relevance ranking workflows. Configuration work focused on ingestion pipelines for policy and program documentation, automated text enrichment using NLP automation, and tuning of semantic relevance and query paraphrase handling to align search behavior with program-specific terminology. Operational scope centered on program and policy teams responsible for guidance, compliance, and citizen-facing content within the United States education services context. The implementation emphasized index governance, access control and content lifecycle management to maintain classification accuracy and relevance across evolving document sets, and it established processes for ongoing model and taxonomy updates consistent with Natural Language Processing operational practices. The initiative positioned SearchBlox SearchAI PreText NLP to enable knowledge discovery across FSA content, improving the agency’s ability to surface policy and program information for internal stakeholders and citizen services use cases. Deployment details reflect a focused application of Natural Language Processing capabilities for document-centric search and retrieval rather than broader enterprise platform replacement. | |
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United States Department Of Justice Antitrust Division | Government | 1200 | $233M | United States | SearchBlox Software | SearchBlox SearchAI PreText NLP | Natural Language Processing | 2019 | n/a | In 2019, the United States Department Of Justice Antitrust Division implemented SearchBlox SearchAI PreText NLP to support secure, permission-aware search across investigative and legal document collections. The deployment uses SearchBlox SearchAI PreText NLP in the Natural Language Processing category to deliver semantic search and automated text analysis capabilities for government and regulatory workflows within the Antitrust Division. Implementation focused on permission-aware indexing and NLP automation, with inferred module-level use for semantic embeddings, named entity extraction, document classification, and automated metadata tagging to improve legal discovery and investigative research. Configuration emphasized access controls and role-based result filtering to align search outputs with investigative and legal function requirements, and the operational scope centers on the Antitrust Division's document repositories and case files managed within United States federal offices listed by the vendor. |
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