Lexopedia AI optimizes and orchestrates multiple advanced LLMs (Large Language Models), including:

These models are continuously updated to ensure optimal speed, accuracy, and relevance for various research and problem-solving tasks.

Here are some common questions about DeepBrainz-R1:

DeepBrainz-R1 is our custom-built agentic reasoning model developed by DeepBrainz AI & Labs, based on DeepSeek-R1. We've significantly enhanced its capabilities to reduce hallucinations while maintaining and improving reasoning, math, and coding performance.

DeepBrainz-R1 is currently in its pre-alpha stage. This means it is actively undergoing post-training and rigorous internal testing to further enhance its capabilities and stability.

At this time, DeepBrainz-R1 is accessible exclusively within Lexopedia AI. It serves as Lexopedia AI's Default Flagship Thinking model, powering the advanced agentic reasoning capabilities you experience on the platform.

Yes, we have plans to launch DeepBrainz-R1 via an API for developers. Once we have a more stable and robust version of DeepBrainz-R1 that meets our high standards, we will make it available for external integration. Please stay tuned for future announcements regarding API access.

Lexopedia AI's Triple Ops Mode is its core agentic architecture that powers agentic research workflows across three operational modes:

The Model Context Protocol (MCP) is an open standard designed to connect AI models directly with external data sources and tools. It uses a host-client-server architecture (often leveraging JSON-RPC) so that applications—like large language models—can access real-time, contextual data, much like plugging into a "USB-C port for AI." This standardized connection enables more accurate and dynamic responses by providing the necessary context while ensuring secure and efficient communication.

As stated, the Model Context Protocol (MCP) Host-Client-Servers integration is based on Anthropic's open standard protocol. It is part of Lexopedia AI's architecture that helps ensure access to the most accurate, current, and reliable information while maintaining privacy and security. It's currently being built to enhance the platform's ability to handle complex research queries, support multi-step reasoning, and streamline integration between various systems.

Vector Similarity Search is a technique that identifies similar items by comparing their positions in a multi-dimensional space. These positions are represented by numerical vectors.

Embeddings are those numerical vectors generated by machine learning models that capture the underlying semantic meaning of data—like words or images. Together, they enable systems to find information based on meaning rather than just matching text or keywords.

Lexopedia AI uses vector similarity search and embeddings to understand the meaning behind queries instead of just matching keywords. Embeddings convert text into numerical vectors that capture semantic content, and vector similarity search finds items with similar vectors. This approach delivers highly relevant, context-aware results, enhancing the accuracy of research outcomes.

Lexopedia AI provides access to over 125 autopilot modes tailored for various research needs, including:

The Files Upload & Analysis feature allows you to easily upload documents and files by clicking the 'Clip' icon in the file upload section. Lexopedia AI then performs deep analysis to extract key insights and contextual information from your uploaded materials.

Lexopedia AI seamlessly processes complex mathematical equations and coding challenges through:

Image generation features are currently in development and will be available soon. Once launched, Lexopedia AI will be able to generate images based on textual descriptions, which will be useful for visual research and documentation.

Add your profile on Settings page to tailor Lexopedia AI’s responses to your needs, such as preferred location, language, styles, and preferences.

Lexopedia AI can be customized through the Settings page. For additional information on custom instructions, please consult the Personalization section under General FAQs. The Custom Autopilot feature will be available soon to enable further customization of Lexopedia Autopilots.

Yes, Lexopedia AI is designed with conversational capabilities, supporting interactive and context-aware dialogue-based research. It remembers the context of previous queries for a seamless conversation experience.

AI Data Retention is a setting that allows Lexopedia AI to use your research data to improve its models. You have full control over this setting and can turn it off to exclude your data from being used for model improvement.

Tokens are units of text used in natural language processing. Lexopedia AI can process a substantial number of tokens simultaneously, providing detailed responses to complex queries and research requests.

Standard users cannot directly adjust parameters like temperature, top p, or stop tokens. These settings are optimized automatically based on the chosen research mode. For advanced customization, developers will be able to explore Lexopedia AI's API when it becomes available.

Lexopedia AI continuously optimizes its existing models and constantly adds new models that are updated with up-to-date training data to enhance performance and capabilities.

No, Lexopedia AI ensures user data is not used for training third-party models, adhering to stringent privacy measures and India's data localization norms.

Lexopedia AI collects minimal user data necessary to improve service quality and personalize your experience. All data is protected under stringent privacy policies, and you have control over how your data is used through the AI Data Retention settings.

Lexopedia AI prioritizes privacy and security through:

Yes, Lexopedia AI adheres to robots.txt directives, respecting website rules on automated access.

For technical issues or bugs, you can:

- Use the In-app messenger at the bottom right corner of the app/site

- Email support@deepbrainz.com with a detailed description of the issue, steps to reproduce it, and relevant screenshots

Report security issues via:

- In-app messenger

- Email to support@deepbrainz.com

Information on any bug bounty programs will be available on our security page.

Currently, Lexopedia AI operates independently and does not support integration with ChatGPT accounts or other third-party AI platforms. It uses its own set of plugins and tools designed specifically for its agentic-reasoning-first approach.

Yes, API access is planned for future release. This will provide integration opportunities with open-source LLMs and various research tools, enabling developers to build custom solutions using Lexopedia AI's capabilities.

Browser extensions and native mobile/desktop apps are currently in development and will be announced soon. These will enhance the accessibility and usability of Lexopedia AI across different platforms and devices.

For further information, subscriptions, and support, visit Lexopedia AI.

Upcoming features include everything listed under Upcoming Features in Getting Started or General FAQs including advanced multi-agent workflow & task automation.

The official launch of subscription plans (Starter, Pro, Pro+, Academia Pro+, Enterprise Pro+, Academia Pro* and Enterprise Pro*) is scheduled for June 2025, as per the Getting Started guide.

Mobile apps for Android and iOS are currently under development.

For additional technical support, please contact us via the In-app messenger or at support@deepbrainz.com.