The Future of AI Text Analysis: Graph-Based vs. Conversational AI

Learn why graph-based AI is the future of natural language understanding and generation, and how Lettria’s platform can help you leverage knowledge graphs, language models, and custom NLP for your business needs.


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Natural language understanding and artificial intelligence for text analysis have witnessed remarkable advances thanks to large language models (LLMs). These models can generate fluent and coherent texts on various topics and domains, as well as perform a range of natural language processing (NLP) tasks. However, the current paradigm of generative AI for text is limited by the conversational interface, which often fails to capture the rich semantics and pragmatics of natural language. In this blog post, we argue that the future of generative AI for text lies in a hybrid approach that combines graph-based knowledge integration, such as knowledge graphs, and conversational AI.


At Lettria, we have developed a novel platform that combines knowledge graph technology with LLMs to create smarter AI models that generate insights from both knowledge and text. Our platform leverages the power of knowledge graphs to represent and store structured information from various sources, as well as the ability of LLMs to extract and infer new knowledge from unstructured texts. By connecting knowledge graphs with pre-trained language models and custom NLP models tailored to your needs, Lettria delivers a “text to knowledge graph” solution enabling:

- Knowledge graph enrichment: LLMs can discover new relationships, attributes, and connections in text, enriching knowledge graphs with highly accurate information. As models process more data, knowledge graphs become truly intelligent.

- Text structuration: LLMs excel at NLP tasks like disambiguation, co-reference resolution, and parsing - turning unstructured text into structured data and seamlessly integrating it with knowledge graphs.

- Question answering: LLMs, especially those fine-tuned on domain-specific data, provide a natural interface for asking complex questions and gaining knowledge graph-based answers with contextual understanding.

Rather than relying on pre-built conversational AI or using LLMs in isolation, connecting generative models, knowledge graphs, and custom NLP enables responsibly developing solutions that align with business needs. Our hybrid human-AI approach focuses resources on augmenting knowledge and structuring data for a transparent, collaborative system improving over time.

Graph-Based vs Conversational AI: Which Is Right For Your Business?

While graph-based and conversational AI both enable intelligent interfaces, the approaches have significant advantages and disadvantages depending on a company's needs. Understanding use cases where each excels helps determine the optimal solution for developing AI capabilities responsibly.

Graph-based AI connects knowledge as a network of semantic relationships, navigating complexity at scale. This provides:


- Explainability: Graph-based knowledge offers transparency into how the AI generates results or answers questions. This visibility into the "reasoning process" builds trust and enables troubleshooting.

- Scalability: Graphs can scale infinitely as new knowledge connects, suited for domains where information is growing or changing rapidly.

- Flexibility: Graph-based knowledge adapts as relationships or data change, ideal for dynamic use cases. The AI system evolves to new contexts.


- Complex implementation: Developing knowledge graphs requires technical expertise and time. The approach may be over-engineered for simple use cases.

Conversational AI provides an intuitive user interface for querying information or requesting actions. This is ideal when:


- Simplicity: Conversational AI typically has a fast, easy implementation, well-suited for customer service or basic questions at scale.

- Accessibility: The conversational interface is highly accessible for many users across platforms and devices.


- Limited context: Conversational AI struggles with complex questions or discussions requiring an understanding of relationships, context or history. Responses are often generic without personalization.

- Rigidity: Conversational interfaces depend on pre-defined behavioral paths and responses. They lack flexibility for new contexts, limiting value in dynamic use cases.

- Black box: Conversational AI systems can be considered as "black boxes" because their inner workings and decision-making processes are often opaque and difficult to understand. This lack of transparency makes it challenging to troubleshoot issues or align the AI system to new objectives.

Clearly, both paradigms have their advantages and disadvantages, depending on the use case and the domain. Here are some examples of use cases where conversational AI or graph-based AI are more suitable:

- Customer service: Conversational AI excels at handling basic, common questions for many users with a simple, accessible interface. However, it may struggle with complex, uncommon, or ambiguous questions that require deeper understanding or reasoning. In these cases, graph-based AI can provide more context, personalization, and explainability, leveraging knowledge graphs to find relevant information from various sources.

- Virtual assistant: Conversational AI can handle generic queries or requests and trigger simple actions like setting reminders, playing music, or ordering food. However, it may not be suitable for sophisticated tasks that require integration of multiple information sources, such as planning a trip or finding the best deals. Graph-based AI can provide more intelligence and flexibility, connecting information from different domains and services.

- Diagnosis and recommendations: In domains like healthcare and finance, which require understanding complex user histories, relationships between symptoms or risk factors, and data-supported recommendations, graph-based AI is ideal. It offers accuracy, reliability, and explainability and can scale as new conditions, treatments, or insights emerge. Conversational AI may not provide the same level of accuracy and reliability, especially when dealing with sensitive or critical information.

As we can see, conversational AI and graph-based AI have different strengths and weaknesses when it comes to natural language understanding and generation. However, they are not mutually exclusive. In fact, they can be integrated to create hybrid solutions that combine the best of both worlds. For example, Neo4j shows how knowledge graphs can power conversational AI by providing contextual awareness, personalization, natural language understanding, and multimodal interactions. Similarly, Deloitte predicts that the future of conversational AI will involve more integration of knowledge graphs and other sources of structured data to enable more intelligent and human-like interactions.

Knowledge Graphs: Natural Language Understanding at Scale

Knowledge graphs are a powerful way of storing information as a network of semantic connections, enabling AI systems to navigate complex relationships across domains. However, knowledge graphs alone are not enough to capture the full potential of natural language understanding and generation. Integrating knowledge graphs with language models and custom NLP provides multiple benefits:

- Data integrity: Knowledge graphs structure information, validating accuracy through semantic relationships and user feedback. Integrating text-based insights improves data quality over time, as the AI system learns from new sources and contexts.

- Scalability: Graph-based knowledge can scale infinitely, connecting new information in a decentralized yet systematically organized manner. Each new connection and insight adds value, as the AI system expands its knowledge base and capabilities.

- Adaptability: Knowledge graphs adapt as information and relationships change, providing dynamic knowledge that evolves with business and customer needs. Integrating NLP models enables fast, accurate evolution, as the AI system updates its understanding and generation of natural language.

- Explainability: Graph-based knowledge offers a transparent view of connections and relationships between information, enabling explainability even as the system gains complexity. Users can see exactly why and how the AI generates particular insights or answers, as well as provide feedback and corrections.

Our platform leverages knowledge graphs and models together for an approach that eliminates “black box” AI. Our hybrid solution provides businesses complete visibility into the NLP capabilities driving decisions and automation, ensuring that resources develop transparent systems earning trust over the long term. Rather than relying on conversational interfaces in isolation, integrating knowledge graphs, language models, and custom NLP provides a foundation for collaborative human-AI partnerships powering innovation.

Conclusion: The Future of AI Text Analysis is Hybrid

LLMs and conversational AI have enabled massive progress in natural language understanding, but relying on either approach alone has significant limitations. LLMs struggle with data privacy, security, and explainability, while conversational AI lacks the context and flexibility to handle complex language needs.

At Lettria, we believe the future of AI for text analysis depends on hybrid human-AI systems combining the strengths of multiple approaches. Our platform integrates knowledge graphs, LLMs, and custom NLP models tailored to your needs. This provides:

Explainability and trust
Knowledge graphs offer transparency into our AI's insights and recommendations. Users understand the reasoning behind each result.

Scalability and adaptability
Graph-based knowledge evolves dynamically with new data and relationships, suited for fast-changing environments. Our AI capabilities scale and adapt to new contexts.

Data integrity and privacy
Knowledge graphs validate information through semantic connections, and custom models are designed using your data. We ensure integrity, security and governance.

Cost and resource efficiency
Our optimized hybrid approach requires fewer resources than running broad LLMs, reducing expenses and environmental impact. Advanced NLP is achievable for organizations of all sizes.

Rather than relying on pre-built models alone, our methodology focuses on developing AI tailored to your needs through combining knowledge graphs, LLMs, and specialized NLP. The result is an integrated solution providing insights that align with your business goals and build trust in the long term.

If you're looking to gain actionable insights from your text data in a responsible, cost-effective way, Lettria offers an innovative NLP platform for developing hybrid AI tailored to your needs. The future of artificial intelligence depends on flexible, transparent systems that evolve with the dynamic challenges of your industry. Lettria leads the way by delivering natural language understanding focused on your priorities — optimizing the knowledge and methods that matter most.

Contact us today to discuss how our hybrid NLP approach can power natural language understanding with built-in explainability - ensuring that your AI journey leads to a strategic partnership, not a black box. You can also check out our platform and success stories, plus our recent blog posts about our philosophy regarding LLMs (below) to learn more about how we apply our graph-based AI to various domains and challenges.

From Chatbots to Knowledge Graphs: Why Analysts Need a New Way to Access and Analyze Data

LLMs are at a Crossroads, and Lettria Helps Users Find Their Way Forward

Our Take on Large Language Models for Text Analysis


Knowledge graphs are a powerful way of storing information as a network of semantic connections, enabling AI systems to navigate complex relationships across domains.

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