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06AI Models & Research
Model work is research with a delivery date. We run disciplined experiments, fine-tune and evaluate models against the task that matters, and build the inference systems that serve them reliably. We work with established open and commercial models — and are explicit about what a model can and cannot do.
Reproducible experiments with tracked data, parameters and results.
Adapting existing models to a domain, a format or a task with measured gains.
Serving, batching, caching and latency budgets for models in production.
Task-specific evaluation suites that catch regressions before users do.
Collection, cleaning, labelling and versioning of training and evaluation data.
Planning, memory and tool-use patterns for agents that need to stay predictable.
Scope
A metric tied to the real task is agreed before any training run.
Every intervention is compared against the simplest thing that could work.
Only measured improvements graduate into the serving stack.
Typical stack
Usually not. We work with established open and commercial models — selecting, fine-tuning and evaluating them for your task — and build the inference systems that serve them.
With task-specific evaluation suites agreed before training, compared against a simple baseline. Only measured improvements go into production.
Yes. We design inference systems for managed platforms, private cloud or dedicated servers, with latency, cost and data-residency requirements in mind.