In research
The long work begins.
We are training our own models, beginning with the domains we know most deeply: compliance, finance and enterprise work.
We start where we have real problems and real data to learn from: the compliance, finance and enterprise work behind RegulixOne and RegulixBooks. Models are trained for those tasks first, evaluated against them, and widened only when they hold up.
A global model is planned. Its name, capabilities and availability will be published only when they are real. Until then it is shown here as In research.
Questions we are researching
- How far can models trained on focused, high-quality domain data go before general scale is needed?
- How should a model show its reasoning so a compliance officer or auditor can check it?
- How do we measure progress honestly, on the tasks that matter to the people who will use the work?
How we approach it
- Own use cases first
- Every model begins with a task inside Futurixone's own products.
- Evaluation before claims
- Nothing is described as a capability until it has been measured and reviewed.
- Human oversight
- In every AI system we build, consequential decisions remain under human control.