CHAI

Overview

CHAI is a leading AI research and consumer platform focused on conversational generative artificial intelligence. Based in Palo Alto, CA, CHAI operates a consumer product that enables users to build, customize, and interact with AI characters and stories. The company was the first to launch such a consumer AI platform on the App Store in November 2022, preceding comparable products from other major AI companies. Since then, CHAI has grown into a high-revenue B2C business, reaching an annual revenue run rate of $70 million in 2026, while completing multiple strategic funding rounds totaling over $55 million.

From a technical perspective, CHAI’s platform is built on a substantial in-house machine learning stack. In February 2023, CHAI deployed its first in-house 6B parameter large language model, after finding that open-sourced models were not sufficient for social and engagement use cases. This deployment yielded a +10% engagement boost. In March 2023, the company trained a best-of-4 reward model directly on user signals, leading to significant improvements in day-30 user retention. The model was subsequently upgraded to a 13B architecture, again improving engagement and retention. CHAI also deploys Proximal Policy Optimization (PPO) to minimize the probability of a chat session ending, and scaled reward models were trained on over 100 million human feedback signals to reduce user retry rates and increase session lengths.

CHAI’s infrastructure is equally advanced. By October 2023, off-the-shelf load balancing and vLLM were no longer enough to support 500,000 daily active users. CHAI implemented custom CUDA kernels and built its own GPU orchestration system. Later, the company reserved an additional 1,000 A100 GPUs and introduced model blending, a proprietary technique for combining model outputs. CHAI now operates a 1.4 exaflop cluster to support both large-scale model training and real-time inference. The research team continues to explore RLHF, reward modeling, inference efficiency, and LLM optimization, all while running a growing consumer platform.

Ultimately, CHAI serves as both a consumer product and a research lab. Users can create their own AI characters and embed them in interactive stories, while researchers gain real-world data and insights into how large language models behave in consumer-facing settings. The platform's technical achievements in LLM deployment, RLHF, and GPU orchestration make it a notable reference for the broader conversational AI industry. Whether for entertainment, creative expression, or applied research, CHAI offers a rare combination of engineering depth, commercial traction, and user-centric design.

Core capabilities

AI Character & Story Creation

Users can create, customize, and interact with their own AI characters and embed them in story-driven experiences.

In-House Large Language Models

Deploys proprietary LLMs, including 6B and 13B parameter architectures, adapted for social engagement and retention.

RLHF with Reward Models

Uses reinforcement learning from human feedback and reward models (best-of-4 and XL) trained on user signals to improve retention and reduce retry rates.

Proximal Policy Optimization

Applies PPO reinforcement learning to optimize the base foundation model and reduce the probability of chat session termination.

Custom GPU Orchestration

Implements custom CUDA kernels and a proprietary GPU orchestration system to support large-scale inference workloads at over 500K DAU.

Model Blending

Invented model blending technique to combine outputs from multiple models for improved response quality and user engagement.

Exascale Compute Infrastructure

Operates a 1.4 exaflop cluster for large-scale model training and efficient real-time serving.

Applied Research Focus

Conducts applied research in conversational AI, RLHF, and LLM optimization, with findings embedded directly into product.

Early Market Leadership

First to launch a consumer AI character platform on the App Store, ahead of competitors.

Scalable B2C Business

Achieved $70M/yr revenue in 2026 with over $55M in funding, reflecting strong market validation.

Use cases

Creating personalized AI characters for ongoing conversations

Developing interactive story worlds with multiple AI-driven characters

Large-scale A/B testing of engagement and retention strategies using LLM variations

Experimenting with reward model tuning and RLHF in production environments

Benchmarking efficient GPU inference and large-scale model serving

Analyzing user behavior to adapt models for social and conversational purposes