AI Model Fine-Tuning & Custom Training
Adapt model behavior when prompting and retrieval are not enough for a repeated, measurable task.
Why businesses need it
Fine-tuning is not the first answer to every AI problem. It becomes useful when examples are consistent, the desired behavior is measurable, and a general model repeatedly misses the same pattern.
What it does
- Evaluate whether prompting, retrieval, or fine-tuning is the right approach
- Prepare, clean, label, and split example data
- Train and compare candidate models against a held-out evaluation set
How Wavefront implements it
- Map: Document the current process, owner, decisions, exceptions, and result before selecting tools.
- Connect: Use the approved data and systems already responsible for the customer or operational record.
- Build: Configure a measured training pipeline with data provenance, evaluation criteria, and rollback options.
- Prove: Test real examples, edge cases, handoffs, and measures before expanding the workflow.
Industries that use this service
Frequently asked questions
- What is included in AI Model Fine-Tuning & Custom Training?
- Adapt model behavior when prompting and retrieval are not enough for a repeated, measurable task. Wavefront scopes the workflow, connects the required systems, configures the operating rules, tests the handoffs, and supports launch and improvement.
- Do we need to replace our current software?
- Usually not. Wavefront first identifies what should stay, what should connect, and what is creating the operational problem. Replacement is recommended only when the existing system prevents a reliable workflow.
- How does Wavefront keep people in control?
- Every implementation defines permissions, confidence thresholds, approvals, escalation paths, auditability, and the decisions that must remain human.