AI Strategy, Model Selection & Engineering
Decide which model path is worth paying for before you build on it.
Who it’s for
Teams that know AI should play a part but have not settled on a model, a hosting mode, or whether fine-tuning is justified.
What the work can include
- An assessment of candidate models against quality, latency, throughput, memory and inference-cost requirements
- A recommendation among prompting, retrieval, tool use, a different model, or fine-tuning and other optimization only where the assessment justifies it
- Implementation of the chosen path, including fine-tuning when it is warranted
Fine-tuning is optional. It is not the default.
When this assessment says no
If the use case does not justify a model, the recommendation says so.
How it starts
Usually a bounded assessment, which can lead into a scoped build. Start with an assessment.