About
Making AI lighter for everyone
Lumoxic AI builds tools that make machine learning models smaller, faster, and more energy-efficient — without sacrificing the intelligence you trained them for.
Our Mission
The optimization problem is real
Most AI teams spend weeks switching between quantization tools, pruning libraries, and distillation frameworks — each with different APIs, different model format requirements, and different levels of maturity.
We built Lumoxic to solve that. One platform that handles every optimization technique through a single API. Upload a model, tell us where it needs to run, and get back a production-ready optimized version.
Team Members
Avg Compression
Avg Energy Saved
Beta Users
Leadership
Meet the Founder
Frances Hosker
CEO & Founder
Building tools that make AI deployment practical and sustainable. Focused on bridging the gap between research-grade models and production-ready inference.
@franceshoskerPrinciples
What drives us
Efficiency Over Complexity
The best optimization is the one you don't have to think about. We automate the hard parts and let you focus on your model's purpose.
Measurable Impact
Every claim we make comes with numbers. Size reduction, latency improvement, energy savings — all benchmarked, all verifiable.
Developer Experience First
One SDK, one API call, clear documentation. If it takes more than 5 minutes to get your first result, we've failed.
Responsible AI
Smaller models consume less energy. We track and report the carbon impact of every optimization to make green AI tangible.
Team
Our Areas
ML Engineering
Quantization algorithms, pruning strategies, distillation pipelines
Systems & Infrastructure
API platform, optimization runtime, model serving
Research
Novel compression techniques, energy-aware training, hardware-specific optimization
Product & Design
Developer experience, documentation, dashboard
Timeline
Our Journey
Concept & Research
Initial research into unified model optimization pipelines. Identified the fragmentation problem in ML deployment tooling.
Prototype
Built first quantization + pruning pipeline that could handle PyTorch and ONNX models through a single interface.
Distillation Engine
Added automated knowledge distillation with teacher-student training, completing the three core optimization techniques.
Energy Benchmarking
Launched energy profiling module — per-inference Joule measurement for real carbon-aware deployment decisions.
Public Beta
API opened to early adopters. 100+ models optimized in the first month with average 6.3x compression.
Want to work with us?
We're always looking for collaborators and partners who share our mission of making AI more efficient and accessible.
Get in Touch