Why Choose AdvisorLens?

Read why AdvisorLens is better than the standard generative AI

We build AdvisorLens with these three key points in mind:

  1. Explainability: Our platform delves into a wealth of private documents, offering you a perspective that’s as unique as your needs. Every answer provided is rooted in factual information sourced directly from your extensive document repository. You are always in control of any answers you receive from our platform. Say goodbye to hallucinations in responses.
  2. Infinite Knowledge: At AdvisorLens, there are no limits. We believe in the power of knowledge, which is why our knowledge base constantly expands. The more reference documents you have, the more robust and informed our conversations become.
  3. Professional Human-Friendly Dialogue: We understand the importance of a professional yet human touch. AdvisorLens seamlessly blends artificial intelligence with human-friendly dialogue. Our platform levrages the state-of-the-art LLM technologies, ensuring every conversation feels natural and insightful.

AdvisorLens can be applied to various NLP applications, including chatbots, question-answering systems, and content generation, where correct information retrieval and natural language generation are critical. The key advantages AdvisorLens provides include:

  • Improved relevance and accuracy: By incorporating a retrieval component, AdvisorLens models can access external knowledge sources, ensuring the generated text is grounded in accurate and up-to-date information. This leads to more contextually relevant and accurate responses, reducing hallucinations in question answering and content generation.
  • Contextual coherence: AdvisorLens provide context for the generation process, making generating coherent and contextually appropriate text easier. This leads to more cohesive and understandable responses, as the generation component can build upon the retrieved information.
  • Handling open-domain queries: AdvisorLens models excel in taking open-domain questions where the required information may not be in the training data. The retrieval component can fetch relevant information from a vast knowledge base, allowing the model to provide answers or generate content on various topics.
  • Reduced generation bias: Incorporating retrieval can help mitigate some inherent biases in purely generative models. By relying on existing information from a diverse range of sources, AdvisorLens models can generate less biased and more objective responses.
  • Efficient computation: Retrieval-based models can be computationally efficient for tasks where the knowledge base is already available and structured. Instead of generating responses from scratch, they can retrieve and adapt existing information, reducing the computational cost.
  • Multi-modal capabilities: AdvisorLens models can be extended to work with multiple modalities, such as database and images. This allows them to generate contextually relevant text to textual and visual content, opening up possibilities for applications in image captioning, content summarization, and more.
  • Customization and fine-tuning: AdvisorLens models can be customized for specific domains or use cases. This adaptability makes them suitable for various applications, including domain-specific chatbots, customer support, and information retrieval systems.
  • Human-AI Collaboration: AdvisorLens models can assist humans in information retrieval tasks by quickly summarizing and presenting relevant information from a knowledge base, reducing the time and effort required for manual search.
  • Symbolic knowledge integration: AdvisorLens seamlessly integrates with semantic technologies and knowledge graphs to always obtain the references and motivation behind the provided answers.
Last modified October 19, 2023: feat: added section why and how (93d2a8e)