CHAI AI
Business Profile
Core Value Proposition
CHAI AI provides a platform to build, deploy, and scale social AI-powered chat experiences and user-generated ChatAIs (UGAI) at large scale.
Target Customer
Consumers who want to create and interact with customizable ChatAIs; developers and researchers building AI platforms; quant traders and finance teams exploring AI tooling.
Key Differentiator
In-house, end-to-end social AI platform with Model Mesh orchestration; vast GPU-backed compute (up to 5000 GPUs; >1.4 exaflops); thousands of production LLMs (51K unique LLMs); advanced model blending (Best-of-8, 10x24B); RLHF/DPO-based improvements driven by user signals; first consumer AI platform enabling user-generated ChatAIs ahead of major competitors.
Implementation Timeline
Milestones and deployments include: Nov 2022 β CHAI Launches on App Store (consumer ChatAIs); Feb 2023 β First in-house 6B LLM; Mar 2023 β Best-of-4 reward model; May 2023 β 13B architecture; Jun 2023 β PPO model; Oct 2023 β Efficient inference with custom GPU orchestration; Nov 2023 β Increased GPU reservations (1000 A100); Dec 2023 β BO8 reward model; Mar 2024 β DPO model deployed; Aug 2024 β Upgraded all blends to DPO; Sep 2024 β 13B reward model; Oct 2024 β 10x 24B models deployed; Jan 2025 β Model Mesh orchestrator deployed; Mar 2025 β GRPO deployed.
Pain Points
- 1Need to deliver engaging, social, and factually correct AI at scale to millions of users
- 2Requirement to manage and serve hundreds to thousands of LLMs with low latency and high reliability
- 3Need for advanced model improvement loops (RLHF, DPO, Rejection Sampling) driven by user signals and efficient infrastructure
Key Features
- 1Platform to build and deploy social AI/ChatAIs with iOS, Android, and web access
- 2Model blending and ensemble approaches (e.g., 10x24B models) to boost engagement
- 3Model Mesh: in-house cluster orchestration enabling multi-cluster, multi-GPU-type serving at high throughput (>10K requests/sec)
- 4Custom CUDA kernels and GPU orchestration for efficient inference (beyond off-the-shelf solutions like vLLM)
- 5Large-scale LLM management (5000 GPUs, 1.2T tokens/day, 51K unique LLMs) with scalable infrastructure
Target Customer Industries
Decision Makers
- 1CEO
- 2Founder
- 3Engineer
- 4Researcher
Client Results
- 1Users: grew from 1 million in early 2022 to over 10 million by 2025
- 2Active users: Daily Active Users (DAU) grew from 800K to 2M; average session length increased from ~60 to ~90 minutes
- 3Engagement improvements: 60% higher engagement with 10x24B blended models vs GPT-4o; 20% engagement boost from DPO; 15% engagement improvement from GRPO
- 4Scale metrics: over 1.2 trillion tokens served per day; >10K requests per second across GPUs and model architectures
- 5Infrastructure: 5000 GPUs in cluster; >1.4 exaflops of compute performance; 51K unique LLMs served
Competitive Advantages
- 1Proprietary in-house GPU cluster and custom CUDA kernels enabling higher performance than generic solutions
- 2Model Mesh: scalable, multi-GPU-type serving to support hundreds of LLMs with high throughput
- 3Model blending and advanced RLHF/DPO techniques yielding significant engagement improvements
- 4Early mover advantage as a consumer AI platform enabling user-generated ChatAIs
- 5Large, active user base with long average session times, enabling rapid iteration and monetization opportunities
Key Benefits
Additional Product Information
Product Description
CHAI AI is a leading platform for building, researching, and deploying social AI and large language models, including a consumer-facing app store experience, in-house LLMs, model blending, and scalable inference via a dedicated GPU cluster and orchestration layer.
Target Market
Consumers and developers building social AI experiences; researchers and engineers designing scalable AI platforms; finance/quant teams exploring AI-driven tooling.
Unique Value Proposition
A scalable, social-focused AI platform with end-to-end in-house research, thousands of production LLMs, advanced model blending, and bespoke GPU orchestration to deliver engaging AI experiences at scale.
Technical Requirements
Requires a large GPU compute infrastructure (5,000 GPUs noted); custom CUDA kernels and in-house inference/ Orchestration (Model Mesh); large-scale multi-LLM serving; production-ready on iOS, Android, and web.
Pricing Model
Not specified
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