Introducing Graphlogic
RAG & LLM

If some or all of the following factors apply to you, having a Gen-AI based Knowledge Bot would be beneficial:

– A Large volume of information: You have a large volume of frequently changed information that needs to be organized efficiently

– Frequent queries: Your company spends a lot of time handling repetitive requests from customers, or team members about specific topics or processes

– Need for Scalability: You anticipate a need for scalability in managing and disseminating information as your organization grows

– 24/7 availability: You need a solution that can provide support round-the-clock

– Cost and efficiency: You are looking for a cost-effective solution to manage information and provide support and do not want hire additional staff for this purpose.

Preparing a knowledge base to be automated by a bot involves specific considerations to ensure compatibility and effectiveness:

– Use cases and scenarios: Identify common use cases and scenarios for which users are likely to seek assistance from the knowledge base bot. Create content specifically tailored to address these use cases, including FAQs, troubleshooting guides, and step-by-step instructions.

– Integrations: Based on the identified use cases, define applications to connect to with the goal of providing more valuable and personalized information for users.

– Updatability: Update your knowledge base so that it remains accurate, relevant, and up-to-date as well as the knowledge base bot. Incorporate new information, and refine conversational scenarios based on user feedback and interactions.

– Integration with the bot platform: Choose a chatbot platform that supports integration with your knowledge base, Gen AI engine, and provides tools for prototyping necessary conversational scenarios.

This is more about complementing than replacing: Generative AI focuses on creating content and managing large databases, while traditional Conversational AI facilitates recognition and engaging interactions.

Absolutely! Combining Generative AI with traditional NLU and rule-based bots provides several benefits including:

– Faster bot development: Use Generative AI to quickly prepare datasets to train and test NLU modules and enable faster time-to-market for bot solutions.

– Focus on goals: Generative AI produce natural responses, allowing for more engaging conversations. However, it could be of higher importance to ensure users follow strict goal-oriented scenarios and take specific target actions. That is where rule-based bots are more relevant and effective.

– Higher personalization: Generative AI can adapt responses based on the user’s input, context, sentiment, language preferences, or specific rules relevant to the conversation.

– Scalability: Rule-based systems can handle specific scenarios efficiently, while NLU and Generative AI handle a wider range of inputs. Combining both approaches allows for more queries being handled.

Our Generative AI and LLM models can be deployed on-premise to ensure absolute data protection and security.

Yes, you can connect your website or knowledge base or upload any context-enriched documents for bots to behave based on your unique brand and industry.

Yes, you can bring you custom models in the backend: OpenAI, Azure OpenAI, Anthropic, YaGPT, or any other.