Google and open-weight models specialists for your dedicated team.

Where Google and open-weight models fits in a long-term product team.

The most useful property of open weights is the size range. Many production tasks, such as classification, extraction, routing and short-text summarisation, are handled well by a small model that runs cheaply, and using a frontier model for them pays much more for capability the task doesn't need.

Licensing needs careful reading. "Open weights" covers a range from fully permissive to licences with usage restrictions, scale thresholds or field-of-use limits. A model chosen without checking can become a commercial problem well after it is embedded in a product.

What your dedicated team does with Google and open-weight models.

Model selection should be empirical. A small model evaluated on the real task often matches a much larger one at a fraction of the cost and latency, and that comparison takes days.

Running that evaluation properly is part of what our AI engineers do when you hire AI engineers through us.

Common Google and open-weight models use cases.

• Small models for bounded tasks Classification and extraction handled at a fraction of frontier-model cost.
• Licences checked before adoption Usage restrictions understood, because they are a commercial problem once embedded.
• On-device and edge inference Models small enough to run where no connection or no data egress is permitted.

Adding Google and open-weight models skills to your team.

Model evaluation is explicit scope, with model size matched to the task by measurement.

Every Google and open-weight models specialist we assign works remotely inside your tools as a dedicated member of your team, with the management on our side. It is how companies hire remote developers without opening an offshore office of their own.