A platform for building and deploying social AI chatbots.
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A platform for building and deploying social AI chatbots.
CHAI is an AI platform focused on building and deploying social AI chatbots. It provides a comprehensive infrastructure for developing, training, and serving large language models (LLMs) optimized for social interactions. The platform leverages in-house trained LLMs and model blending techniques to enhance user engagement and retention. Key features include custom CUDA kernels for efficient inference, GPU orchestration for scaling, and reward models trained on user signals to optimize chatbot behavior. CHAI's architecture supports multi-cluster, multi-GPU-type serving of hundreds of LLMs, enabling personalized and engaging user experiences. It utilizes reinforcement learning techniques such as PPO, DPO, and GRPO to continuously improve model performance based on user feedback and preferences. CHAI's platform also incorporates advanced features like model blending and Best-of-8 rejection sampling to further enhance the quality of chatbot interactions.
A platform for building and deploying social AI chatbots.
Quick visual proof for CHAI. Helps non-technical users understand the interface faster.
CHAI is an AI platform focused on building and deploying social AI chatbots.
Explore all tools that specialize in llm training. This domain focus ensures CHAI delivers optimized results for this specific requirement.
Open side-by-side comparison first, then move to deeper alternatives guidance.
Ensembling different LLMs trained on different targets at the conversation level to improve user retention.
Utilizing an upgraded reward model to select the best response from 8 generated candidates.
Optimized CUDA kernels for efficient inference on NVIDIA GPUs.
In-house cluster orchestration platform for handling multi-cluster, multi-GPU-type serving of hundreds of LLMs.
An upgrade from Direct Preference Optimization that results in engagement improvement by learning from user preferences.
Training reward models directly on user signals, such as retry rate and chat session length, to optimize chatbot behavior.
Provides users with a supportive and engaging chatbot that can help them manage stress and anxiety.
Train an LLM on mental wellness resources and therapeutic techniques.
Implement a reward model that encourages empathetic and supportive responses.
Deploy the chatbot on CHAI's platform and allow users to interact with it.
Monitor user feedback and iterate on the model to improve its effectiveness.
Allows brands to create engaging and interactive experiences for their target audience.
Define the virtual influencer's persona and target audience.
Train an LLM on relevant brand information and marketing content.
Implement a reward model that encourages brand-aligned and engaging responses.
Deploy the virtual influencer on CHAI's platform and promote it through social media.
Provides students with personalized and interactive learning experiences.
Train an LLM on relevant educational materials and pedagogical techniques.
Implement a reward model that encourages accurate and helpful responses.
Deploy the AI tutor on CHAI's platform and allow students to interact with it.
Monitor student progress and iterate on the model to improve its teaching effectiveness.
Providing more engaging and interactive NPC (non-player character) experiences in games.
Train an LLM on game lore, character dialogue, and player interactions.
Implement a reward model that encourages immersive and entertaining responses.
Integrate the social AI into the game environment and allow players to interact with it.
Monitor player feedback and iterate on the model to improve the NPC's behavior and dialogue.
Delivering news content tailored to individual user interests and preferences.
Train an LLM on a diverse range of news articles and user profiles.
Implement a reward model that prioritizes relevant and engaging news content.
Deploy the news aggregator on CHAI's platform and allow users to customize their news feeds.
Monitor user engagement and iterate on the model to improve the accuracy and relevance of news recommendations.
Providing instant and efficient customer support, reducing wait times and improving customer satisfaction.
Train an LLM on customer service knowledge bases and common customer inquiries.
Implement a reward model that encourages helpful and accurate responses.
Integrate the chatbot into the customer service platform and allow customers to interact with it.
Monitor customer satisfaction and iterate on the model to improve its problem-solving capabilities.
Create a CHAI developer account.
Define the purpose and persona of your social AI chatbot.
Train your LLM using CHAI's platform and tools.
Implement reward models based on user feedback.
Deploy your chatbot to CHAI's platform.
Monitor performance metrics and iterate on your model.
Utilize custom CUDA kernels and GPU orchestration for optimized inference.
All Set
Ready to go
Verified feedback from other users.
“Users praise CHAI for its engaging and personalized chatbot experiences.”
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