Core research.
Engineering supremacy.
We do not build basic API wrappers. We execute applied machine learning research to optimize token latency, spatial vision pipelines, and secure model serving for institutional scale.
Primary R&D Vectors
Private Inference Optimization
Optimizing vLLM and TensorRT-LLM runtimes to serve massive 70B+ parameter models on commodity enterprise hardware with zero latency regression.
Agentic Consensus Workflows
Architecting autonomous swarms that collaborate via self-correcting reasoning loops, perfect for heavy spatial e-commerce and logistics processing.
Spatial & Document Cognition
Training vision-language neural networks to parse complex scanned industrial blueprints, balance sheets, and clinical reports at 99%+ accuracy.
Inference Quantization Gains
We benchmark and optimize model latency. By deploying custom-compiled weights (AWQ / GPTQ) onto optimized private node clusters, we achieve massive latency decreases and scaling advantages.
Quantized Llama-3-70B
Inference Efficiency
Technical Whitepapers
Download our active R&D blueprints showing real lab evaluation numbers.
The 2025 Enterprise Quantization Playbook
A technical evaluation of running AWQ and GPTQ quantized model weights on secure on-premise compute nodes.
Multi-Agent Consensus Networks in Supply Chain Logistics
Exploring deterministic self-healing loops to automate inventory tracking and zero-latency procurement.
Collaborate on applied AI research
Looking to partner for cross-border applied AI diagnostics, SleepML, or multi-agent swarm R&D? Connect with our scientific team.