From your workflow down to the GPU kernel.
An AI project can stop at plugging a hosted model into a workflow. We work further down as well: the workflow and applications, the data, the models, the serving stack and the hardware they run on. Each layer is designed and tested for the job it has to do.
Reference pattern
The layers we work across
Wiring a hosted model into a workflow covers the first three. The other three are where cost, speed and fit are decided once a system runs on hardware you control: adapting the model to the task, serving it efficiently, and getting full use out of the GPU. Every build selects its own components after evaluation; the pages below explain the techniques with worked examples.
Where a plugged-in model usually stops
- 1Workflows and applicationsThe process as your team runs it: the approvals, the systems it touches, the screen people use.
- 2Data and retrievalPipelines, canonical records, document reading, and search that respects permissions.
- 3ModelsOpen-weight or hosted, chosen and evaluated on examples from your own work.
Where we keep going
- 4Model adaptationFine-tuning, preference tuning and distillation, where evaluation shows a gap worth closing.
- 5Serving and inferenceEngines, batching, caching, quantisation and capacity sized for your GPUs.
- 6GPU kernels and hardwareProfiling first, then fused and attention kernels where a bottleneck shows up.
Read the engineering notes
- PrinciplesThe nine rules every build followsThe nine principles behind every system we build, from mapping the work before choosing a model to keeping every model replaceable.Read
- Systems architectureDocuments, retrieval, workflows, data and deployment, as one designA reference architecture for private AI systems: document pipelines, hybrid retrieval, durable agent workflows, data integration, deployment topologies and security.Read
- Model adaptation and evaluationChoosing, adapting and measuring models on your own dataOpen-weight model choice and licences, the adaptation ladder, LoRA and QLoRA, DPO and GRPO, evaluation built from your own data, and governance mapping.Read
- Inference performanceServing engines, scheduling, quantisation and capacity sizingHow modern serving engines batch and cache, when speculative decoding helps, how quantisation trades memory for accuracy, and how to size a deployment.Read
- GPU kernel engineeringRooflines, CUDA, Triton, attention kernels and verificationWhen a custom kernel is worth writing, how to read a roofline, CUDA and Triton fundamentals, FlashAttention-style kernels, portability and verification.Read
Get ahead with Ales Analytics
Tell us what you want to build. Our engineers take it from the first conversation to a system your team relies on every day.