ML Optimization
Refactor and optimize models to reduce latency, memory, compute, or footprint while keeping performance under control. Techniques include quantization, pruning, distillation, and architectural tuning.
Consulting & Collaboration
We support teams in building efficient, hardware-ready ML pipelines. Our work centers on model optimization, FPGA acceleration, and reproducible engineering practices—helping teams replace ad-hoc steps with clear, validated workflows from prototype to deployment.
How We Can Help
FPGA / Edge Deployment
Guide the pipeline that takes your models from research environments into FPGA or edge devices, using clean, reproducible workflows.
Workflow & Architecture
Structure your ML-to-hardware processes, tools, and responsibilities so teams can move with clarity instead of ad-hoc scripts.
Training + Consulting
Combine tailored training with project-focused consulting—learning while building something real.
How Collaboration Typically Works
- 1. Context & goals: we map your current systems, constraints, and desired outcomes.
- 2. Assessment: identify bottlenecks, risks, and quick wins.
- 3. Plan: propose a concrete roadmap (sprint or multi-week collaboration).
- 4. Build: work together on models, tooling, and deployment pipelines.
- 5. Transfer: deliver clear documentation and reproducible workflows.
Let’s Talk
If you’re exploring ML + hardware, working with FPGA or edge devices, or want clearer AI workflows, we can help you design a collaboration that fits your context.
Share a short description of your project, your team, and your timeframe, and we’ll schedule a call.
Contact for consulting