Custom AI Agents in 3 Clicks
Running On DF11
Reduced Memory Footprint by 30%
The first mathematically proven method to reduce memory requirements for LLMs without any accuracy loss. Run bigger models on smaller hardware with perfect fidelity.
Standard Format
DF11 Format
30% Memory Savings
100% Mathematically Lossless
Works with Any LLM
Standard Hardware Compatible
100% Mathematically Lossless
Works with Any LLM
Standard Hardware Compatible
IMPLEMENTATION SCENARIOS
(Democratized AI)
Run larger models on limited hardware, trading a bit oflatency for a big memory win
Run 70B parameter models on consumer GPUs
(Cost Efficiency)
Cut memory use and bandwidth while preserving full fidelity and throughput.
30% reduction in GPU memory requirements
(Democratized AI)
Enable advanced AI capabilities on resource-constrained devices.
Run 70B parameter models on consumer GPUs
(Optimized ROI)
Reduce costs and increase model capacity in cloud environments.
30% reduction in GPU instance costs.
We're a frontier AI agent research lab firmly focused on advancing agent technology beyond current limitations. We believe that the current approaches most companies are taking with prompt engineering and RAG are ultimately a dead end for creating truly effective AI agents.
Our team brings together top researchers in model reinforcement learning, fine-tuning, synthetic data generation, performance optimization, and distributed systems. We're uniquely positioned at the intersection of cutting-edge research and practical enterprise applications.
