AI Data Fusion injects enterprise data into Large Language Models – on demand and in real time – to ground GenAI apps and deliver responses users trust.
Enhancing AI with Data
Large Language Models (LLMs), like GPT, Gemini, Lamda, and Claude, are the deep learning AI systems that serve GenAI apps.
The drawback of using LLMs in the enterprise, however, is that they’re known to “hallucinate”, providing misinformation and unfounded responses – with incredible confidence – that damage user trust and a company’s brand reputation.
K2view AI Data Fusion makes GenAI apps work for your business by integrating your company’s internal enterprise data with the publicly available information the LLM is trained on. LLM grounding with enterprise data leads to far more accurate and personalized responses to queries than the model could generate based on its publicly available information.
K2view AI Data Fusion extends conventional Retrieval-Augmented Generation (RAG) by injecting relevant context-rich data from your enterprise systems to the LLM. It augments LLMs with real-time, high-quality, and compliant data to grow sales and customer intimacy, while minimizing GenAI hallucinations.
AI Data Fusion Use Cases
AI Data Fusion makes multi-source enterprise data GenAI-ready, for a wide variety of customer-centric use cases, including:
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Customer service chatbot
AI Data Fusion elevates the conventional conversational AI bots with a better user experience and lower customer care costs. How? By increasing first contact resolution rates and decreasing the overall number of services calls.
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Marketing campaigns
Marketing teams generate hyper-personalized campaigns to grow sales and strengthen customer relationships.
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Fraud detection
Governance, risk, and compliance teams detect fraud in customer transactions based on real-time events, behavior profiles, and historical data.
Make Enterprise Data AI-Ready with Micro-Databases
K2view AI Data Fusion unifies and organizes multi-source enterprise data by business entities – customers, orders, loans, products, or anything else that is important to the business. An entity’s data can be queried by, or infused into, the LLM as a contextual prompt in milliseconds.
The data for each business entity is stored in its own high-performance Micro-Database™, which provides:
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A 360° view of all the data related a particular entity, from all sources, including legacy and cloud-based apps
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Freshness and relevance, ensuring that the Micro-Database data is always up to date – a key component of customer-facing GenAI apps
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Real-time speed, enabling the LLM to perform high-concurrency, low-latency queries
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Security and privacy, by applying role-based access controls and user-based data segregation to the entity data
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Seamless change management, due to automated schema drift propagation with zero downtime
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Low TCO, as the result of data compression, flexible deployment modes, and minimal hardware and maintenance requirements
AI Data Fusion Previewed at 2024 Gartner Data & Analytics Summit
K2view revealed its RAG GenAI innovation at the Gartner Data & Analytics Summit, March 11-13, 2024 in Orlando, FL, at its session, “Make GenAI Work for Your Business with Real-Time Enterprise Data” and via the company’s press release, “K2view Announces AI Data Fusion, Market-First Solution for Grounding GenAI Apps with Enterprise Data”.
Learn more about K2view AI Data Fusion
