CHAI: Chat + AI

Quant traders building AI Platform
Palo Alto, CA

Strategic Round II Complete: CHAI AI secured additional funding rounds, bringing its total investment to date to over $55M dollars to expand both compute and talent acquisition.

[ Daily Active Users Growth ]

Incentives & Scale

RESEARCH

All platforms work best with the right incentives. At CHAI, we've tried paying developers, but the biggest motivators remain high-quality feedback, recognition, and the satisfaction of building a popular LLM. Our scale enables the critical mass of feedback and models needed to create strong feedback loops.

OCT 2022

APR 2023

OCT 2023

APR 2024

OCT 2024

APR 2025

NOV 2022

CHAI Launches on App Store

We were the first to launch a consumer AI platform, allowing users to create their own ChatAIs—ahead of Character AI and ChatGPT.

FEB 2023

Deploys First In-House 6B LLM

Open-sourced LLMs no longer satisfied our users' requirements, as the LLMs needed to be adapted for social and engagement purposes. We saw a +10% engagement boost from our own in-house model.

MAR 2023

Deploys Best-of-4 Reward Model

We continued to iterate on RLHF (Reinforcement Learning with Human Feedback), training a reward model directly on user signals. This led to a huge boost in our day 30 user retention.

APR 2023

Larger Model Upgrade - 13B Architecture

We found that a bigger model leads to better depth, therefore better retention. We re-trained our LLM from scratch and saw another +10% engagement boost.

MAY 2023

PPO Model Deployed

Using Proximal Policy Optimization, a reinforcement learning technique, we optimized our base foundation model to decrease the probability a chat session ends.

JUNE 2023

Deploys Reward Model XL

Continued to scale up our reward model. Trained with 100 million signals to decrease user retry rate and increase chat session length.

OCT 2023

Efficient Inference & Custom GPU Orchestration

Off-the-shelf load balancing and vLLM were no longer sufficient to support our user base at 500K DAU scale. We implemented custom CUDA kernels together with our own GPU orchestration system.

NOV 2023

Increased GPU Reservation

We hit a scaling issue due to high demand from our users. We reserved an additional 1,000 A100 GPUs from our provider to scale reliably.

NOV 2023

Deployed Model Blending

CHAI invented model blending—ensembling different LLMs trained on different targets at the conversation level. This outperformed GPT-3 in user retention.

DEC 2023

BO8 Reward Model Deployed

With increased cluster capacity, we implemented Best-of-8 rejection sampling, utilizing our upgraded reward model to its full extent.

MAR 2024

DPO Model Deployed

Utilizing Direct Preference Optimization with user preference datasets, we boosted engagement by 20%. The performance stacked well with our existing reward model.

AUG 2024

Upgraded All Existing Blends to DPO

Building on the success of DPO, we iterated on optimization targets and data selection, and successfully deployed DPO across all production blends.

SEP 2024

13B Reward Model Deployed

With increased GPU capacity due to cluster upgrades, we were able to serve larger reward models for all users.

OCT 2024

10x 24B Models Deployed

We upgraded our existing production blend to 24B models. With blending enabled, we saw a surge in daily active users and day 30 retention.

JAN 2025

Model Mesh Orchestrator Deployed

To support over 1M Daily Active Users, Model Mesh—an in-house cluster orchestration platform—was deployed to handle multi-cluster, multi-GPU-type serving of hundreds of LLMs in production.

MAR 2025

GRPO Deployed

GRPO (Group Relative Policy Optimization) is an upgrade from Direct Preference Optimization, resulting in a +15% engagement improvement.

[ Product ]

Building Platform for Social AI

We believe in platforms. There is huge demand for AI that is not only factually correct but also entertaining and social.

IOS ANDROID

Coming Soon...

[ GPU Cluster ]

1.4 EXAFLOPS GPU CLUSTER
FOR AI INFERENCE

CLUSTER

At CHAI, we serve hundreds of in-house trained LLMs across several GPU chip types from both AMD and Nvidia. While open-source solutions such as vLLM work well for simple workloads, we've found that we can further improve upon vLLM by almost an order of magnitude through several optimizations, such as custom kernels and compute-efficient attention approximations.

NUMBER OF GPUS

5000 GPUs

NUMBER OF TOKENS SERVED

1.2T Tokens / Day

NUMBER OF UNIQUE LLMS SERVED

51K LLMs

CLUSTER COMPUTE PERFORMANCE

>1.4 Exaflops

NVIDIA A100

NVIDIA L40S

AMD Mi325x

AMD Mi300x

Current openings

JOBS

Who we are

NEWS