Curated training inputs
Use approved examples, terminology, task patterns, and process context selected for the intended outcome.
Use curated organisational data to improve domain language, preferred response patterns, task behaviour, and process alignment inside a versioned and reversible lifecycle.
Adapt approved open-source language models using curated organisational data to improve domain vocabulary, response style, task performance, and process alignment.
Governed adaptation lifecycle
Stage 1 / 7
Approved data
Select organisational content and examples that are approved for model adaptation.
Stage checkpoints
Confirm the task, owner, and success criteria
Record source, licence, and data provenance
Flag restricted or sensitive content for review
Datasets · training jobs · checkpoints · model registry · endpoints stay in your customer-controlled environment.
Domain language
Improve understanding of organisation-specific vocabulary and context.
Task performance
Adapt model behaviour to well-defined business tasks and quality criteria.
Process alignment
Reflect approved response styles, workflows, and operational expectations.
Use approved examples, terminology, task patterns, and process context selected for the intended outcome.
Evaluate domain vocabulary, response style, task performance, and workflow alignment before release.
Keep model changes explicit, testable, reversible, and separate from automatic production learning.
Curate
Prepare approved, task-relevant organisational examples and evaluation criteria.
Adapt and evaluate
Train the candidate version and compare it against agreed quality measures.
Approve and release
Promote a version deliberately, monitor its use, and retain rollback options.
Walk through the relevant data, workflow, governance, and deployment boundaries with the Vassure Ai team.