Solutions

Drive value from data across all enterprise functions using Nexus' microservices

A system of intelligence across your organization

Software that helps companies establish their customer interaction goals.

Ability to measure the fitness of data for use across the eco-system.

A database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data

Nexus provides best-in class, pre-built models for any natural language processing (NLP) and image processing (CV) task or use case. NLP models include machine translation, sentiment analysis, question answering, named entity recognition, and text classification. CV models include segmentation, detection, classification, and recognition. Nexus also supports Bring Your Own Model (BYOM) to wrap existing data science artifacts in its MLOps environment.

We have out-of-the-box connectors for virtually every meaningful cloud service or system, such as Tableau, PowerBI, and Qlik. Nexus converts business-friendly data requests into optimized technical queries, enabling a "no code” mechanism for data access and discovery.

Data lakes aggregate and replicate data, including personally identifiable information (PII), to enable its analysis

Nexus assimilates first and third-party data sources, including publicly available information, to provide a 360-degree view of people and their associated entities. Nexus automatically surfaces intelligence relevant to decision makers.

Nexus automatically surfaces meaningful intelligence from data across multiple sources. Users and decision-makers can leverage the intelligence within their data without knowledge of SQL queries or programming skills.

Machine learning algorithms build models based on sample data.

Nexus applies ML and statistic models to analyze data in flight, in real time, the moment it enters the data ecosystem.

Monitoring is typically a myopic process performed at the application or individual level of the source. It fails to provide a holistic, end-to-end view of the data.

Organizations are awash in an ocean of data, but they can’t use it efficiently. Real-time processing of data can be an insurmountable challenge.

Software that provides financial-related information on organizations and entities.

Software that enables marketing organizations to centralize and streamline their processes.

Orchestration software requires detailed knowledge of both the software systems and the enablers that are being aggregated.

The training process is computationally expensive and requires GPUs.

Platform integration can be a brittle, labor-intensive process to build and maintain.

Current solutions cannot perform real-time data correlation across an entire data ecosystem and thus have a narrow view on the context of events.

Nexus natively implements workflow, security, ML/AI, and integration functionality into a single platform, streamlining the ability to create data workflows.

Machine learning models require computing resources to train.

Nexus’ holistic nature of data analysis enables ML automation. Models can be quickly built and deployed within workflows.

Synthetic media is a state-of-the-art technology that requires significant expertise and investment.

Labor intensive to process, cleanse, and verify the integrity and quality of the data.

Large amounts of high quality, labeled data is required to train effective machine learning models.

Organizations inadvertently store PII on systems outside the trusted, authoritative source, thereby creating privacy, security, and compliance risks.

Nexus collects and analyzes data in real time and provides automated alerts, notifications, and health checks.

Software that secures and protects a computer, network, or computing-enabled device.

There is typically a delay or latency in gathering information, and additional tools may be needed for event notifications and other feedback.

Data infrastructure is the entire backend computing support system required to process, store, transfer, and safeguard data. It is an essential aspect of data processing and analysis. Businesses and organizations cannot create value out of data without having the proper data infrastructures.

Current sales enablement solutions are prone to flawed, limited data, providing questionable insight and low-impact observations.

Nexus data mesh architecture seamless integrates with upstream and downstream systems, connecting the silos and enabling analysts to interact with the totality of their data.

Graph databases are expensive and complex to create.

Rigid definitions of people do not take advantage of all discoverable data points and cannot properly resolve ambiguities in aggregated data.

Software that provides information on people and entities.

Nexus enables organizations to semantically understand the wealth of information in their unstructured data, and then use that knowledge to contextualize and correlate their entire data ecosystem. It provides a holistic view of enterprise data that unleashes new insights and speeds decisioning.

Current monitoring solutions cannot perform real-time data correlation and thus have a narrow view on the context of events.

Nexus does not replicate authoritative data to enable its analysis, transformation, and enrichment. Nexus' ability to analyze data in real time absolves an enterprise from unnecessary infrastructure costs, without undermining speed, reliability, or even compliance.

Open source software is developed in a decentralized and collaborative way, relying on peer review and community production. It has become a movement and a way of working that finds new ways to solve problems in communities and industries.

The Nexus Knowledge Layer frees the analyst from the mundane operations, enabling them to focus on the fundamental business problems. Domain and database expertise not required.

Does not resemble the highly structured relational databases of the past. Locks value away in unstructured data such as documents, free text, or spoken language. Manually analyzing and processing this unstructured data is demanding, inefficient, and prone to errors. Contains copies of data outside of its authoritative source.

Software that wraps the mathematics necessary to create specialized ML models.

Nexus automatically surfaces meaningful intelligence from data across multiple sources. It enables real-time detection of emerging communities or anomalies relevant to marketing strategy and execution.

AI/ML frameworks require data scientists and domain experts to have knowledge of enterprise architecture, scalability practices, and auditing.

The process of deploying machine learning models requires significant hands-on expertise and involvement with the production workflow.

Geographical entities are subject to problems with ambiguities and do not consider context, especially within unstructured data.

Nexus does not persist sensitive data, such as PII. This data remains at rest on the intended authoritative system with the appropriate governance controls. Nexus's ability to categorize object-level information at the moment of existence can prevent sensitive information from ever coming to rest in a data store or system.

Software that extracts information and derives insights from geo-spatial data.

Nexus deploys machine learning to process data instantly before it’s stored anywhere. By using ML, Nexus can ingest and decompose data in-flight, without the need for a data lake or warehouse. Any type of data, any format, any system, any structure, in the cloud or on premises.

Supports the integration of ML models to facilitate data analysis.

Compliance solutions can document governance rules, but they cannot enforce the rules in a production environment without expensive customization.

Torch.AI has pioneered an approach for acquiring and enriching publicly available information (PAI) that enables intelligence analysts to hone operational targets more efficiently and effectively. This process gathers structured and unstructured data (e.g., text, audio, video) in various languages from throughout the internet and dark web, uses machine learning models to extract and correlate entities and their relationships in a knowledge graph, and makes this data available for analyst search and discovery.

Visualization tools are non-extensible, and their configuration requires an understanding of the underlying complex data model.

Data that is continuously generated by different sources.

Software that provides insights into the operation of a system.

Nexus fully manages the deployment of machine learning models into production workflows through a low-code or no-code interface.

The different products in this domain use specialized methods to retrieve data, but they rely on humans to reactively define and execute the queries on the centralized data store.

Nexus seamlessly pairs with compliance solutions to enact and enforce defined policies on data in motion and at rest to ensure fine-grained governance on any data type and any system.

Building and deploying effective ML models requires significant expertise and investment.

Unmanaged data lakes create a data swamp littered with hard-to-understand data across silos. There is no single place to find, understand, and govern data across the ecosystem. Extracting intelligence requires a permanent transformation process, typically configured by human engineers and consuming additional computing resources.

For appropriate event-generation accuracy and timeliness, Nexus correlates data across first and third party data sources, enabling whole-entity analysis.

Software with defined decision logic used to automate an intelligent process.

Connecting disparate, siloed data stores so that they can share information.

Nexus semantically understands unstructured feedback through channels such as social media. It can correlate sentiment with resolved entities in real time to create a holistic analysis of customer engagement.

Modern machine learning models can perform tasks related to computer vision (CV), speech analysis, and natural language processing (NLP).

A storage repository that holds a vast amount of raw data in its native format until it is needed for analytics applications.

Nexus uses geographical data to resolve entity information and correlate locations with external data sets.

Marketing solutions have manually defined schemas and rules which are bound by human expertise.