Business Software for Palantir Apollo

Foundry is a transformative data platform built to help solve the modern enterprise’s most critical problems by creating a central operating system for an organization’s data, while securely integrating siloed data sources into a common analytics and operations picture. Palantir works with commercial companies and government organizations alike to close the operational loop, feeding real-time data into your data science models and updating source systems. With a breadth of industry-leading capabilities, Palantir can help enterprises traverse and operationalize data to enable and scale decision-making, alongside best-in-class security, data protection, and governance. Foundry was named by Forrester as a leader in the The Forrester Wave™: AI/ML Platforms, Q3 2022. Scoring the highest marks possible in product vision, performance, market approach, and applications criteria. As a Dresner-Award winning platform, Foundry is the overall leader in the BI and Analytics market and rate

NetOwl EntityMatcher provides accurate, fast, and scalable identity resolution based not only on similarities of the entity names but also other key entity attributes such as date of birth, place of birth, address, and nationality. Identity resolution can also be based on social network information such as employer, spouse, associate, etc. NetOwl performs identity resolution based on any combination of available entity record attributes by utilizing its unique proprietary search and indexing engine that allows combination of evidence from multiple matching attributes in a highly robust, scalable, and intuitive fashion. Application-specific business rules can be implemented by determining what combination of record attributes should be matched and what weights should be assigned to each attribute. NetOwl leverages our machine learning-based multicultural, multi-lingual name matching product, NetOwl NameMatcher, to enable sophisticated name matching of various entity types.

NetOwl Extractor offers highly accurate, fast, and scalable entity extraction in multiple languages using AI-based natural language processing and machine learning technologies. NetOwl's named entity recognition software can be deployed on premises or in the cloud, enabling a variety of Big Data Text Analytics applications. With over 100 types of entities, NetOwl offers a broad semantic ontology for entity extraction that goes beyond that of standard named entity extraction software. It includes people, various types of organizations (e.g., companies, governments), several types of places (e.g., countries, cities), addresses, artifacts, phone numbers, titles, etc. This expansive named entity recognition (NER) forms the foundation for more advanced relationship extraction and event extraction. Domains include Business, Finance, Politics, Homeland Security, Law Enforcement, Military, National Security, and Social Media.

NetOwl TextMiner combines our award winning NetOwl Extractor with Elasticsearch to provide unique text analytics software. TextMiner leverages all aspects of NetOwl capabilities and is ideal for supporting “what if” analysis, discovery, quick response investigation, and detailed research. NetOwl TextMiner integrates all text analytics capabilities of NetOwl Extractor, including entity extraction, relationship, and event extraction, sentiment analysis, text categorization, and geotagging into all-encompassing text mining software. Extractor output is stored in Elasticsearch for a variety of intelligent search and analytic capabilities. The combination of Elasticsearch and NetOwl provides fast and scalable real-time text analysis for Big Data. TextMiner’s Web-based UI is an easy to use and configurable text analytics tool for different analysis scenarios and enables users to gain quick access to all and only high-value information derived from a vast amount of texts.

NetOwl NameMatcher, the winner of the MITRE Multicultural Name Matching Challenge, offers the most accurate, fast, and scalable name matching available. Using a revolutionary machine learning-based approach, NetOwl addresses complex fuzzy name matching challenges. Traditional name matching approaches, such as Soundex, edit distance, and rule-based methods, suffer from both precision (false positives) and recall (false negative) problems in addressing the variety of fuzzy name matching challenges discussed above. NetOwl applies an empirically driven, machine learning-based probabilistic approach to name matching challenges. It derives intelligent, probabilistic name matching rules automatically from large-scale, real-world, multi-ethnicity name variant data. NetOwl utilizes different matching models optimized for each of the entity types (e.g., person, organization, place) In addition, NetOwl performs automatic name ethnicity detection as well.

Skywise is an aircraft option fuelled by Airbus expertise and Palantir Technologies, in class data analytics provider. By combining in-flight, engineering, and operational data in an analytics-rich environment, Skywise provides airlines with an industry data platform to address aircraft operations challenges. How the Skywise platform allows operators worldwide to take advantage of a better aircraft experience based on data insights and make sustainable decisions on their operations. Combined with the latest technologies of the Airbus Skywise Core Platform, S.FP+ is a new step in the digitalization of tech ops towards becoming a data-driven organization. It is an integrated solution aimed at improving technical operations team collaboration, aircraft dispatch, unscheduled event anticipation, and fleet reliability. Based on extended aircraft sensor data generated in-flight and integrated with maintenance information data.

Integrate, manage, secure, and analyze all of your enterprise data. Organizations have data. Lots of it. Structured data like log files, spreadsheets, and tables. Unstructured data like emails, documents, images, and videos. This data is typically stored in disconnected systems, where it rapidly diversifies in type, increases in volume, and becomes more difficult to use every day. The people who rely on this data don't think in terms of rows, columns, or raw text. They think in terms of their organization's mission and the challenges they face. They need a way to ask questions about their data and receive answers in a language they understand. Enter the Palantir Gotham Platform. Palantir Gotham integrates and transforms data, regardless of type or volume, into a single, coherent data asset. As data flows into the platform, it is enriched and mapped into meaningfully defined objects — people, places, things, and events — and the relationships that connect them.