General-purpose AI models are impressive until you need them to do something specific to your domain. A support model that doesn't understand your product. A classification model that doesn't know your categories. A summarisation model that doesn't match your house style. At that point, most teams face the same problem: fine-tuning is the right answer, but the infrastructure to do it well — track experiments, version models, evaluate outputs — is expensive and time-consuming to build yourself.
Fine-tuning and model tracking are part of askFinz's Train workspace, now in beta.
The gap this fills
Most teams approaching fine-tuning for the first time discover the same thing: the training step is only a fraction of the work. Before it, you need clean, well-structured data. During it, you need to track which run used which configuration — or the experiments become impossible to reproduce. After it, you need to evaluate whether the new version is actually better than the last, and on what dimensions. Without tooling that handles this as a complete loop, teams end up doing it in spreadsheets and shared drives, which quickly becomes untenable.
The Train workspace is designed to handle that full cycle, not just the training step.
What the Train workspace does
- Manage training data in one place. Upload examples, review and label them, and maintain dataset versions — so you always know which data a given model was trained on.
- Track every run automatically. Each training job records its configuration, duration, and metrics. Comparing two runs is a matter of selecting them side by side, not reconstructing from logs.
- Evaluate outputs before promoting a model. Run structured evaluations against held-out examples and see where the new version improves or regresses — before it touches a live workflow.
- Version and roll back. Every model checkpoint is stored and labelled. If a new version underperforms in production, rolling back is a deliberate, documented action — not a scramble.
Who this is for
Product teams who have trained a model once and found the second iteration harder to manage. Research teams who run many experiments and need reproducibility without a dedicated MLOps engineer. Businesses that have proprietary data — customer conversations, internal documents, labelled examples — and want AI that actually reflects it.
The beta is open now for teams who want to shape how the platform develops. Early access means early influence on the feature set.
How it fits the workspace
Because Train is part of askFinz, a fine-tuned model can move directly into your AI workflows — the same session that trains the model can use it in Chat, test it against Research queries, or connect it to an automated workflow. The model isn't stuck in an isolated training environment; it becomes a working part of your toolset.
Request beta access to be part of the early group.
Further reading
- Drag-and-drop AI workflow automation — how a fine-tuned model connects to automated workflows once it's trained.
- AI for wealth & finance teams — an example of how domain-specific AI changes the quality of the output.
- Hugging Face's open documentation on fine-tuning is a thorough reference for teams exploring the technical foundations of the approach.