LLMs capabilities using the RAG architecture
ChatFAQ utilizes Large Language Models (LLMs) and the Retrieval Augmented Generation (RAG) architecture to provide domain-specific chatbot services. These services are generating human-like responses tailored to specific business contexts. Our framework empowers brands to deliver personalized and efficient communication at scale.
.png)
Your Brand Business Domain
By understanding the brand's business domain, the AI model can assist customers with specific and tailored information, helping them get better information to make informed decisions.
.png)
Meeting your Customer Needs
Stay ahead of evolving customer demands by leveraging ChatFAQ's analysis and AI recommendations. Identify opportunities and address issues through detailed reports based on customer feedback and interactions.
.png)
Controlling Data privacy
ChatFAQ implements robust data privacy measures, such as data encryption, self-hosting, and anonymization techniques to ensure user queries and company information are kept secure and confidential.
Top capabilities
Automatic knowledge expansion
Upload your business context through a URL, CSV or PDF files and ChatFAQ will generate your knowledge base and its utterances to expand your dataset to improve the AI model accuracy. Based on the user interactions and automated clustering, ChatFAQ will suggest you to expand your knowledge base to improve user interactions.
Powerful and secured natural language pipeline
ChatFAQ pipeline is architected on a flexible multi-stage processing that transforms user questions into embedding vectors that are matched by an intent-correlation model. Once the user intent is understood and proper business context has been built, a custom prompt engineering is applied to perform NLG generation with hallucination and adversarial prompt preventions.
Best selection of AI models
Running LLM models is a complex and costly task both on local or cloud deployments. Among the best open-source LLM models, ChatFAQ selects 2 models for development and production purpose in order to achieve the best performance (in number of generated tokens per second) with the minimum hardware footprint (memory and CPU/GPU requirements).
.png)
