详细内容
Strategic Round II
Complete
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
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.
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.
[ 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.



CHAI WEB
Coming Soon...
[ GPU Cluster ]
1.4 EXAFLOPS GPU CLUSTER FOR AI INFERENCE
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
5000GPUs
NUMBER OF TOKENS SERVED
1.2TTokens / Day
NUMBER OF UNIQUE LLMS SERVED
51KLLMs
CLUSTER COMPUTE PERFORMANCE
1.4Exaflops
NVIDIA A100
NVIDIA L40S
AMD Mi325x
AMD Mi300x
Current openings
Who we are

William Beauchamp
Founder / CEO
Cambridge University

Jacqueline Zhu
Chief of Staff
Harvard University

Tom Lu
AI Researcher
Cambridge University
Ex-Quant

Christie-Carol Beauchamp
Mobile Engineer
ex-Meta

Robert Irvine
AI Researcher
Cambridge University

Alexandre Daoud
Backend AI Engineer
Cambridge University

Vineet Mudupalli
AI Engineer
Cambridge University
ex-Quant

Zongyi Liu
AI Researcher
Kaggle Master
ex-Quant

Nischay Dhankhar
AI Researcher
3x Kaggle Grandmaster

Ebony Zhang
AI Researcher
Cambridge University
ATLAS - LHC - CERN










