GRAKN.AI allows you to accurately understanding of a query’s intent and the meaning of its terms.
As companies increasingly have access to massive volumes of both internally generated and externally relevant data, the task of searching through that data to find relevant results becomes increasingly complex. By building systems for both employees and customers that can understand the meaning of natural language queries, companies can streamline their operations, gain strategic business advantage, and improve customer experiences.
Unfortunately, traditional search technologies are not suited to this sort of meaningful search. With its pioneering use of machine reasoning and knowledge graph technology, GRAKN.AI provides a platform for companies to develop sophisticated semantic search tools for their information-—and with dramatically reduced engineering effort. By taking complex data and giving it a flexible and meaningful model in a knowledge graph, context can be built in to ensure the accurate understanding of a query’s intent and the meaning of its terms. By performing machine reasoning and analytics over the knowledge graph, further disambiguation within searches can be achieved, and new connections can be discovered and explored within data.
Automated Fraud Detection
GRAKN.AI allows you to identify hidden links across multiple transactions.
Across both the retail and financial services sectors, loss due to fraudulent customer behaviour is a constant challenge. While fraud has always existed, the growth of online automated systems has provided new opportunities for fraudsters. Unfortunately, traditional human methods of detecting fraud do not adequately scale to large customer bases, and tabular database technologies cannot deal with the fundamentally complex networks, heterogeneous user profiles, and aberrant transactional patterns that allow fraud to flourish.
Instead, what are needed are intelligent systems that make sense of complex customer data and detect anomalies within transactions in real-time—before there can be financial harm. GRAKN.AI provides exactly the sort of infrastructure to handle this data complexity. By turning data into a knowledge graph with a flexible data model, powerful machine reasoning can identify hidden links across multiple transactions, data can be easily integrated across sources, and new information can be continually added to ensure that the system adapts more intelligently and dynamically than fraudsters can.
GRAKN.AI allows you to disambiguate queries, retrieve context-specific knowledge, and generate the most useful answers.
In the past few years, there have been game-changing advancements in natural language processing, opening the doors to a new era of both theoretically sophisticated and highly useful chatbots. By straightforwardly and accurately providing useful answers to natural language queries, chatbots could automate many business tasks and limit the need for proprietary, limited-use interfaces. In principle, chatbots will transform our interaction with enterprises, both as customers and as employees, across almost every industry.
Despite their great promise, chatbots will only truly succeed if they can efficiently and accurately contextualize and understand user queries and thereby provide appropriate and useful responses. In order for a chatbot to contextualize enterprise data, the data’s underlying complexity must be suitably modeled, and the system must be able to make logical inferences across data. By using GRAKN.AI as the underlying data store, chatbot developers can harness the power of knowledge graphs and machine reasoning to ensure to readily disambiguate queries, retrieve context-specific knowledge, and generate the most useful answers possible for each user, whether they’re a frontline customer service agent or an executive.
Advanced Drug Discovery
GRAKN.AI provides an integrated database for research.
Pharmaceutical research and development is an incredibly competitive, costly, time intensive, and informationally complex process. With ever-increasing availability of high-quality biological and chemical data on compounds, targets, interactions, genetics, and diseases, researchers can use new data sources to their advantage. By removing unlikely candidate compounds as early as possible in the drug discovery process, time and money can be saved, and population health can be improved.
Yet, to make effective use of this data, pharmaceutical researchers must have a way of first integrating disparate data sources and then gaining knowledge from that data. By using GRAKN.AI, drug discovery can be advanced through the power of knowledge graphs and machine reasoning. First, biological and chemical data can be aggregated and integrated, modeled in all its complexity and contextual specificity, and flexibly added to as needed. The knowledge graph thus provides an integrated database for research. By performing logical inferences and network analytics across the knowledge graph, researchers can intelligently reason with complex data and find useful patterns, aiding the selection of safe and likely effective candidate compounds for subsequent phases of testing.
Dynamic Risk Analysis
GRAKN.AI allows for for dynamic risk management in strategic decisions.
In the past few years, risk management and regulatory compliance have gained renewed interest in the financial services industry. Firms are actively pursuing technical strategies to ensure that legal requirements are met in a scalable and automated way. At the same time, with growing ambiguity and uncertainty in an increasingly complex and interconnected world, firms want to leverage their informational assets to improve business decisions and maximize returns. Unfortunately, traditional data infrastructure solutions do not meet the needs of twenty-first century risk management and compliance.
To meet these aims, a technical solution that scales, that can flexibly model complexity, that can perform high-level network analytics, and that can find implicit connections within data is needed. GRAKN.AI provides such a solution, providing functional unity to large volumes of complex and heterogeneous data. Internal and external data can be integrated and reasoned over, and new regulations can be added easily, allowing for dynamic risk management in strategic decisions. Meanwhile, network analytics can be performed on the knowledge graph to gain holistic perspective and detect anomalies.
Other key areas where GRAKN.AI can help include:
User-Centric Systems: From personalized healthcare to Customer 360 systems in retail, GRAKN.AI can build precise context around users, and provide different analytic entry-points for different business units.
Data Exploration Tools: By integrating disparate data sets and making implicit connections within them visible, researchers in fields from public health to law can use GRAKN.AI to explore patterns, test hypotheses, and gain unexpected insight.
Content-based Recommendation Engines: Through the context and connectivity of the knowledge graph, and the inferential capabilities of Graql, GRAKN.AI can be used to easily build advanced recommendation systems for media, improving user experience and maximising content engagement.
Knowledge Management Systems: Keeping track of complex structures and dependencies within organizations can be achieved with GRAKN.AI, allowing for organized, contextualized, and highly-interconnected information management systems that adapt in real-time and which contain highly reusable knowledge.