Knowledge-Based GenAI: The Key to Competent Enterprise AI

Ever since conversational AI broke into the mainstream with the launch of chatGPT, large language models have undergone massive improvements in terms of their performance. Ask the latest LLMs how to stack nine eggs, a laptop, books, and a pencil, and they’ll suggest ways to do so without spilling any yolk. Still, organizations continue to grapple with integrating generative AI into their operations, a fact that can be frustratingly apparent when you are stuck in a conversation with a lackluster AI-powered customer support agent. 

Done right, however, GenAI can be transformative, delivering accurate, reliable performance trusted by users both within and beyond organizations while building out their competitive edge. The benefits of GenAI in the enterprise are far reaching, including: 

• Streamlined information gathering and summarization
• Improved decision-making
• Facilitated access to otherwise hidden content
• Automated generation of domain-specific insights
• Enhanced operational efficiency
• Increased employee and customer satisfaction

In this blog, we take a look at what’s behind knowledge-based GenAI and outline what it takes to build the highly accurate generative AI solutions required to deliver on their promise. Finally, we outline how the Squirro Enterprise GenAI Platform enables truly knowledge-based GenAI to ensure interactions are grounded in facts and build trust. 

How Inaccurate AI Affects Operations

But first, you might be wondering, what’s wrong with deploying GenAI solutions that fail to capture the full wealth of available enterprise data? Well, if your goals are accuracy, reliability, and trust, you might be disappointed. 

Lack of Contextual Relevance: The quality of a GenAI application depends largely on the quality of the information that its underlying LLM is provided in the prompt. If the LLM is not provided sufficient industry-specific data, it is likely to generate generic or inaccurate responses. 

Security and Compliance Risks: Unless adequate measures are in place, it can quickly become impossible for organizations to control the output of every instance of their GenAI applications. 

Poor Decision-Making: As users increase their trust in insights generated by GenAI-powered knowledge management solutions, their decision-making can be swayed by inaccurate or incomplete information, with pile-on effects in terms of corporate performance.

Subpar Customer Experience: Poorly implemented outward facing GenAI solutions, e.g., those used in customer support applications, that overlook essential corporate data make for clumsy conversation partners, can undermine user trust, and may cause reputational damage.

What is Knowledge-Based GenAI?

Knowledge-based GenAI applies generative AI techniques that comprehensively leverage existing knowledge bases to enhance the accuracy, reliability, relevance, and explainability of the generated responses. While foundational large language models such as ChatGPT, Claude-3, and Llama can generate conversational responses to just about any question based on their vast training data, knowledge-based GenAIs are able to force the LLM to respond based on corporate data. 

Simply tethering outputs to corporate data is, however, not enough. Instead, GenAI solutions need to be precision-engineered to deliver maximum accuracy at all levels. Doing so enables them to deliver more tailored responses that systematically leverage all relevant knowledge available in the organization and domain-specific insights building on various types of information, often distributed across disparate data silos. Not only does this reduce hallucinations, it also helps identify and mitigate risks and ensure adherence to corporate policies and external regulations. 

How Squirro Enables Knowledge-Based GenAI 

The Squirro Enterprise GenAI Platform is fully geared to empowering our customers to build knowledge-based GenAI applications. At its core is a retrieval-augmented generation (RAG) AI engine and a large language model (LLM) that work in tandem to provide enterprise-ready generative AI capabilities. When a user enters a query, the platform first scours all available corporate data for relevant information to support the query. It then gathers and processes this information before passing it on to the LLM, which goes on to generate a response anchored in corporate data.

But it doesn’t stop there. The platform features multiple additional technological components that can intervene to further enhance the accuracy and relevance of the knowledge-based GenAI output. 

• Knowledge Graphs allow the platform to consider hidden patterns, relationships, and hierarchies within the data when formulating its responses. 
• AI Guardrails help enforce compliance with corporate policies and third-party regulations, ensuring safe and responsible performance of GenAI applications. 
• With the Agent Framework, the platform can deploy custom tools to accomplish complex data processing tasks beyond standard retrieval augmented generation. 
• Access to Operational Data enables the platform to integrate real-time structured data stored in enterprise data platforms. 
• A robust Privacy Layer ensures that personally identifiable information (PII) is prevented from being sent to the LLM, safeguarding sensitive data across all interactions. 

Harnessing the Power of Knowledge-Based GenAI

By now, the transformative potential of GenAI is proven. We’ve seen customers achieve ROI in months after rolling out their solutions and free up hundreds of millions of dollars in value. With applications spanning all industries, corporate activities, and geographical markets, the race is on for organizations to beat their competition to harness its power, giving themselves and their teams a leg up and standing out from the crowd. 

But when it comes to the accuracy, reliability, and trust that a solution achieves, the devil is in the details. Or, more specifically, in the precise technological implementation of the GenAI platform, in details that can only be resolved over time, fine-tuning solutions in challenging and demanding market environments. At Squirro, we have built this “trial by fire” into our strategy. 

It is why we work in what are among the world’s most heavily regulated industries, why we partner with and acquire mature technology providers. And it’s a strategy that has paid off, not least with our recognition as a representative vendor in the 2024 Gartner Market Guide for Conversational AI solutions. Download the Guide here to find out why.