AI ENGINEERING
Efficiently orchestrate data processing, training, and inference.
Multimodal AI, robotics, and self-driving systems are becoming increasingly important. Behind these applications are large volumes of data and diverse processing stages that need to work together.
The challenge is orchestration: moving data efficiently, connecting each stage, and making effective use of CPU and GPU resources throughout the pipeline.
FROM ANALYSIS TO IMPLEMENTATION
Data may be distributed across storage systems and multiple clouds, far from the GPUs that need it. Where data lives, how it is stored, and how each stage retrieves it all affect pipeline performance.
We design data placement, formats, and access patterns around the volume and characteristics of your data. This includes organizing data for efficient reads and streaming access, reducing unnecessary transfers, and making it available where processing happens.
AI pipelines combine different kinds of work: CPU-based data processing, GPU inference for tasks such as filtering and captioning, and model training. Each stage has different resource needs. Batch-only stages can also hold up the pipeline, requiring an entire dataset to finish processing before the next stage can begin.
We match resources to the work—using CPUs for lightweight processing and data streaming, and GPUs for stages that require neural network inference or training. Where dependencies allow, we connect stages through streaming or incremental processing so downstream work can begin sooner.
Throughout the pipeline, we focus on feeding data to GPUs efficiently, reducing idle time, and keeping resources productive.
LET'S WORK TOGETHER
Our specialists bring hands-on experience orchestrating workloads across hundreds to thousands of CPU and GPU machines.
We work with your team from identifying bottlenecks and proposing optimization strategies through implementation, building pipelines around your data, infrastructure, and operational needs.
Projects are scoped and quoted individually.