Layer 07/08
Train SLMs
Fine-tune small, private language models on your own data — inside your own walls.
The problem
General-purpose frontier models are expensive, hard to govern, and require sending data to a third party. For many enterprise tasks a smaller, specialised, private model is cheaper, faster, and safer.
model economics, per task
frontier llm
Private SLM
cost
data
governance
speed
What it does
Fine-tune and distil small language models on your proprietary data with a guided training kit.
Keep training and inference entirely within your environment — including fully air-gapped.
Evaluate SLMs against larger models on your own tasks to prove they are good enough.
Version, deploy, and monitor private models alongside hosted LLMs from one place.
How it connects
SLMs trained here become models available in Create Agents, and are governed by exactly the same firewall, access, and audit layers as any other model.