Enterprise Conversational AI Platforms
The Conversational AI market is growing at an unprecedented rate with an estimated CAGR of 24.24% between 2024 and 2034. From a B2B perspective, conversational AI platforms are especially crucial for the workplace, where chatbots are becoming the most widespread use of AI in business today. In a recent prediction, Gartner wrote “By 2026, more than 50% of enterprise applications will be conversational, up from less than 5% today.”
In short, these numbers are staggering and only point to the urgency that is needed for companies to invest in Conversational AI platforms.
There are many versions of enterprise Conversational AI platforms on the market, but one in particular that is proving to be most effective is a platform that is built on Graph Retrieval Augmented Generation (RAG). With Graph RAG powered Conversational AI, businesses can now access advanced functionalities like contextual assistance, summarization, and content recommendations.
Why choose a Conversational AI solution for your organization?
Aside from the competitive advantage that Conversational AI brings to your organization, it’s also relatively easy to customize it to your particular need and use case.
- Wide range of use cases: Enterprise Conversational AI platforms are now versatile tools that extend beyond simple chatbots. They support everything from contact center automation to customer self-service portals to employee knowledge hubs and recommender systems.
- Enterprise-readiness: A good Conversational AI solution (we mean one that uses Graph RAG here) comprises a number of technologies ranging from Generative AI, large language models, knowledge graphs. Altogether, these capabilities grant you the flexibility and scalability needed to meet your changing demands.
- Better cost efficiency: Conversational AI platforms help reduce operational costs and overhead by automating tasks that would otherwise keep employees busy. Employees can put their focus on delivering quality services to your customers.
Experience the best side of Conversational AI with Graph RAG
On top of the above mentioned benefits, Graph RAG powered Conversational AI solutions provide an additional layer of benefits that are exclusive to this methodology. Before we dive into these benefits, let’s first show you what a Graph RAG looks like.
Between the company’s databases and front-end applications sits the semantic layer, which provides the knowledge graph that is used for indexing. The semantic layer is unique to our Graph RAG approach because it is based off of the company’s knowledge domain, which is modeled by the right people. Information, and thus concepts, are collected by the experts who will actively use the solution and are organized into the knowledge model based on the way these experts understand them. It takes into account the specific language the company uses and the unique relations between business objects.
The Graph RAG layer that sits on top of this semantic layer then draws from this curated model which is then enriched with context by the knowledge graph. The result is a more precise output that takes into account the user intent and all the relevant documents associated with the user’s query.
Some highlights of this approach include:
- Retained knowledge and expertise: Unlike other AI solutions that organize data without taking domain-specific particularities into account, knowledge graphs help you optimize content and knowledge discovery in a domain that closely resembles your business. Your team can refine the model based on the invaluable expertise they’ve gained in their role so that you don’t risk losing knowledge in your changing workplace.
- Personalization: In a business context, even the best tuned LLM and the very best search algorithm will fail if the user’s intent cannot be interpreted correctly. The contextual information provided by the knowledge graph helps to direct the focus of a Conversational AI system so that it only displays what the user is looking for.
- More cost-effective than other models: LLMs traditionally require a significant amount of computing power, time, and machine learning expertise to continuously train the model. Especially in the case of Graph RAG, we rely on a combination of Prompt Engineering and RAG to train models, both of which are independent of costly LLM customization and limited in cost-saving maintenance of knowledge base and graphs.
Combining knowledge graphs with vector databases for your enterprise Conversational AI platform
These days, many RAG architectures are combining LLMs with vector databases. Since a vector database is optimized for efficiency and fast retrieval (to a certain end), it sacrifices accuracy and depth of answers that would otherwise be found in a Graph RAG. A Graph RAG can still compute answers quickly AND ALSO provides the accuracy that is needed for a business situation.
Therefore, we believe that a Conversational AI platform that uses vectors AND knowledge graphs gives you the best bang for your buck. The knowledge graph enhances a vector-based system in the following ways:
Scalability
Vector databases do not scale well with large datasets. As a company collects more data, the algorithm becomes increasingly inefficient. A knowledge graph only requires a simple update to the taxonomy so that it can be synced and indexed automatically.
Dimensionality
Whereas knowledge graphs excel at retrieval of complex data, vectors struggle to find meaningful patterns in high-dimensional spaces, leading to less accurate results.
Refining the Answers
Graph-based retrieval relies on natural language processing and machine learning to understand the query and identify the most relevant information. You can better follow up with and engage with the LLM to refine its response.
In fact, Gartner argues that knowledge graphs are critical to the success of AI solutions and positioned knowledge graphs at the very center of their Impact Radars for Emerging Tech, Artificial Intelligence, Generative AI, and Conversational AI.
In the “Hype Cycle for Generative AI, 2024” which assesses different technological innovations in the Generative AI space. The report summarizes tools and approaches that can be used as well as their impact and position on the Hype Cycle.
About Graph RAG, Analyst Afraz Jaffri writes, “Currently [without GraphRAG], moving applications from 70% to 80% accuracy onwards requires multiple iterations and tuning of parameters. KGs [knowledge graphs] can decrease the number of iteration cycles. KGs are key to supporting AI-ready data.”
Source: Gartner® Hype Cycle™ for Generative AI, 2024, Arun Chandrasekaran, Leinar Ramos, et. al
Why Graph RAG for Conversational AI? The quick facts
More accurate answers
Graph RAGs provide enriched context and metadata for the dataset, resulting in more accurate and comprehensive responses. This minimizes issues like LLM hallucinations (a.k.a. irrelevant or nonsensical answers).
Traceability of information
The answers generated by a Graph RAG are traceable back to specific sources and relationships within the knowledge graph, improving transparency and trust in the information provided.
Efficiency in content and product management
Graph-infused LLMs facilitate the planning, creation, and reuse of content by improving findability and speeding up the publishing process. The same can be applied to product management, where Graph RAG can help consolidate information across the entire product life cycle in order to provide enterprises with an augmented view over their processes. The LLM can help management gain clarity and create meaningful links from their data in order to make better decisions for product longevity.
Improved employee satisfaction and retention
By automating routine tasks and providing reliable, context-rich insights, Graph RAGs help employees focus on more meaningful work, thereby increasing job satisfaction and reducing turnover.
Read more about our enterprise Conversational AI platforms in our eBook
The PoolParty Team has put together its own Conversational AI for the workplace called knowledge-hub.eco, a demo application that allows you to leverage the high conversational performance of an LLM with the reliability and trust of a knowledge graph. See knowledge-hub.eco’s features in our eBook!