Labs
Cerebus Technologies maintains several internal research programs and development tools that support our product engineering work and contribute to the broader AI development community.
Inference Optimization
We operate a dedicated local inference environment built on NVIDIA Blackwell architecture with 128 GB of unified memory, purpose-built for rapid experimentation with large language models. Our work has included systematic benchmarking across quantization strategies and inference engines, contributing findings to community research initiatives studying performance characteristics of open-weight models at scale.
We have executed over 300 structured experiments evaluating model performance across multiple architectures, with results contributed to open-source meta-analysis efforts in the AI research community.
Multi-Model Code Review
We have developed an internal orchestration framework that coordinates multiple AI models in a structured code review pipeline, enabling automated quality assessment with cross-model consensus scoring. This system feeds directly into our development workflow, enforcing architectural standards and catching regressions before they reach production.
Distributed Development Coordination
Our internal development infrastructure uses a coordination layer that synchronizes work across multiple machines and operating systems via encrypted mesh networking, enabling seamless context transfer between development environments and inference hardware.