We build real-time AI systems at the GPU level. Custom CUDA kernels, optimized inference pipelines, multi-modal architectures, and game engine integrations. The kind of work that requires understanding both the research and the metal.
Most AI consultants hand you a Jupyter notebook and call it done. We deliver production systems with benchmarks, deployment guides, and code that your team can actually maintain.
Whether you're optimizing an existing pipeline or building from scratch, we bring research-grade thinking to production-grade engineering.
| Service | Description | Price Range |
|---|---|---|
| Discovery / Audit | Deep technical audit of existing AI/ML systems — performance, architecture, deployment readiness, cost | $8,000–15,000 |
| System Design | AI/ML pipeline from scratch — model selection, data strategy, inference architecture | $12,000–25,000 |
| GPU Optimization | Profile and optimize CUDA/TensorRT/PyTorch inference pipelines; custom kernels where off-the-shelf isn't fast enough | $15,000–30,000 |
| AI Pipeline Design | End-to-end AI systems — data ingestion, model serving, distributed inference, hot-swap architectures | $20,000–40,000 |
| Multi-Modal Systems | Systems processing multiple input types (vision, audio, sensor data) in unified real-time pipelines | $20,000–40,000 |
| Research-to-Production | Take a research paper or prototype and make it ship in production — quantization, performance, deployment engineering | $15,000–35,000 |
Medical imaging AI teams with FDA/regulatory submission cycles, industrial computer vision companies hitting inference-cost pressure, robotics and autonomy companies, research labs needing production engineering, Canadian sovereign-compute deployments. Game studios — see Real-time AI for Game Engines.