List of OPPSCIENCE NLP Customers
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Since 2010, our global team of researchers has been studying OPPSCIENCE 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 OPPSCIENCE 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 OPPSCIENCE NLP for Natural Language Processing include: Peraton, a United States based Aerospace and Defense organisation with 21000 employees and revenues of $8.00 billion, Belgian Federal Police, a Belgium based Government organisation with 12300 employees and revenues of $4.50 billion, BNP Paribas Securities Corp., a United States based Banking and Financial Services organisation with 612 employees and revenues of $438.0 million and many others.
Contact us if you need a completed and verified list of companies using OPPSCIENCE 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.
The OPPSCIENCE NLP customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Belgian Federal Police | Government | 12300 | $4.5B | Belgium | OPPSCIENCE | OPPSCIENCE NLP | Natural Language Processing | 2022 | Sopra Steria |
In 2022 the Belgian Federal Police began deploying OPPSCIENCE NLP as part of the national I-Police reform, a Belgium-wide initiative to centralize and semantically analyze law-enforcement data. OPPSCIENCE NLP is positioned as an Intelligence Analysis Management capability within the Natural Language Processing category and was scoped to handle large volumes of structured and unstructured police and intelligence records.
The implementation emphasized core Natural Language Processing capabilities including semantic indexing, entity extraction, relationship mapping, document classification and centralized corpus search to support investigative workflows. These functional modules were delivered as part of an I.A.M. capability set designed to surface links and patterns across case files, reports and disparate data sources and to provide analysts with semantic query and visualization tools.
Delivery was executed through a consortium led by the system integrator Sopra Steria with KPMG, Microsoft and OPPSCIENCE participating in the program, and pilots began in 2022 with a planned full switchover by 2025. The rollout targeted national police forces and intelligence units across Belgium, establishing a centralized semantic layer intended to converge data from multiple agency systems and improve cross-jurisdictional access to investigative intelligence.
Governance and operational change followed a phased pilot to production model, with consortium-coordinated rollout, analyst onboarding and process alignment to embed semantic analysis into investigative procedures. The program explicitly aimed to improve investigative efficiency and data convergence while centralizing Intelligence Analysis Management workflows through OPPSCIENCE NLP.
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BNP Paribas Securities Corp. | Banking and Financial Services | 612 | $438M | United States | OPPSCIENCE | OPPSCIENCE NLP | Natural Language Processing | 2015 | n/a |
In 2015, BNP Paribas Securities Corp. deployed OPPSCIENCE NLP to support its economic research and communications teams, implementing OPPSCIENCE's Bee4sense search and semantic enrichment technology on the corporate intranet. The Natural Language Processing implementation was executed as a France based deployment aimed at improving access to specialized press and web articles used by research workflows.
The implementation centered on OPPSCIENCE NLP modules for semantic enrichment and enterprise search, with Bee4sense providing multilingual relevance tuning, attachment indexing, and entity linking capabilities. Configuration focused on indexing web and attached content and surfacing semantic links between actors and documents, embedding Natural Language Processing functions directly into research search workflows.
Operational coverage emphasized intranet search for economic research and communications users, with the deployment boosting multilingual search relevance, improving attachment indexing, and enabling identification of links between actors via OPPSCIENCE's semantic and NLP capabilities. Governance and rollout details were aligned to research team needs and intranet search operations to integrate semantic search into existing research processes.
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Peraton | Aerospace and Defense | 21000 | $8.0B | United States | OPPSCIENCE | OPPSCIENCE NLP | Natural Language Processing | 2020 | n/a |
In 2020, Peraton implemented OPPSCIENCE NLP as part of OPPSCIENCE's Bee4sense platform to index and search educational resources for educators and students in the United States, aligning the project with Natural Language Processing capabilities for knowledge management and search use cases. The deployment was oriented toward searchable access to large document collections, supporting discovery across instructional content and resource repositories used by educators and students.
The implementation emphasizes semantic enrichment and document indexing, with inferred use of OPPSCIENCE NLP and Semantic Studio functionality to perform semantic tagging, content classification, and vectorized search indexing. Configuration focused on automated ingestion pipelines and text processing to normalize heterogeneous educational documents, enabling semantic search and relevance scoring consistent with Natural Language Processing workflows.
Operational scope is United States based, targeting education-focused business functions related to knowledge management and information retrieval for educator and student user groups. Governance activities implied by the implementation include content indexing policies and search relevance tuning to maintain searchable access to large collections, while integrations are limited to the OPPSCIENCE Bee4sense indexing architecture as described by the vendor source.
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