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Introducing
Graphlogic RAG & LLM

Combine powers of Generative AI and traditional Conversational AI to create highly personalized and contextually relevant experiences.
Introducing 
Graphlogic RAG & LLM

How it works?

Generative AI focuses on creating original content and generating human-like responses, while Conversational AI aims to facilitate natural and engaging interactions with users.

By combining these technologies, businesses can provide highly personalized and contextually relevant experiences to their customers.

Benefits of
GL GPT & LLM MODELS

Knowledge Base automation
Knowledge Base automation
Build and update knowledge-base bots on the go – even extensive and frequently changed knowledge bases are now easy to search through with AI bots.
→ fast launch, less manual resources required
Training and test data generation
Training and test data generation
Get example phrases from Generative AI to form datasets for training and testing of Conversational AI NLU module – specifically beneficial when there are not enough historic data.
→ 10x faster training, improved recognition
Personalized responses
Personalized responses
Provide personalized communication to everyone: professional / patient / empathetic / quirky / stick to the task / playing trivia – depending on who bots are talking to.
→ improved CSAT, higher engagement
Multilingual support
Multilingual support
Instantly translate training, testing datasets and responses in each new language using Generative AI capabilities – support customers worldwide in the languages of their choice.
→ improved CSAT
Sentiment detection
Sentiment detection
Analyze customer and employee sentiment and detect early signs of frustration and burnout to enforce timely interventions and proactive support from humans.
→ improved CSAT, higher FCR

How to launch?

TA Industries

Profound impacts are expected across business functions in high-data industries where personalization and time-to-market for customer solutions do matter. 
Some of the industries where GPT and LLM models could have significant implications include:

Retail
Retail
Captivate customers with Generative AI-powered personalized messaging and also navigate them through large product catalogues.
Banking
Banking
Provide straight-to-the-point support based on proprietary knowledge such as banking policies and historic customer interactions.
Healthcare
Healthcare
Generate synthetic training & testing data for medical students, enable image recognition to process medical prescriptions.
Manufacturing
Manufacturing
Provide operators and technicians with technical support, training simulation, and guidance during repair procedures.
Telecom
Telecom
Ensure personalized up-to-date support with regard to pricing options and streamline marketing broadcasting.
Education
Education
Guide through and summarize relevant content from various educational resources, generate personalized assessments and quizzes.
Insurance
Insurance
Navigate through extensive policies with precision and automate onboarding and upskilling processes for new employees.

Frequently Asked Questions

How to define if I need a Knowledge base Gen AI bot?
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.
How to prepare the knowledge base?
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.
Does GenAI replace traditional NLU modules?
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.
Is it possible to mix up Gen AI bot with traditional NLU and rule-based scenarios?
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.
Is the Gen AI bot deployment secure?
Our Generative AI and LLM models can be deployed on-premise to ensure absolute data protection and security.
Could it be customised for company-specific terminology?
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.
Can I integrate GPT from other vendors?
Yes, you can bring you custom models in the backend: OpenAI, Azure OpenAI, Anthropic, YaGPT, or any other.

See also:

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