Choose the workflow
Define the decision, the acceptable error, and the volume that makes improvement worth paying for.
03 / DVAR MODELS / FINE-TUNING
Make the repeatable work cheaper and better.
The eval decides when a workflow earns its own model. When it does, we co-build a smaller model around your examples, edge cases, language, and operating constraints, then keep improving it with production feedback. Dvar brings the data, evaluation, training, and operating support alongside your team.
DVAR FINE-TUNINGCHECKPOINT LOOP
01Scopebaseline
02Curateexamples
03Tuneadapter
04Evaluateheld-out
05Operatefeedback
Production feedback returns to the next checkpoint.
A SERVICE, NOT A TOGGLE
Fine-tuning only works when the workflow is clear.
We start with a baseline and a held-out evaluation set. If a prompt, retrieval, or routing change solves the problem, we say so. If the model should learn it, we train it.
Build the evidence
Turn real examples, reviewer feedback, and failure modes into a clean training and evaluation set.
Ship the checkpoint
Deploy the tuned model behind Dvar Inference and feed production outcomes into the next checkpoint.
GOOD CANDIDATES
High-volume.
Repeatable. Measurable.
Claims, classifications, and routing
Internal policy and domain language
Document extraction and review queues
Agent steps with a stable definition of done
TALK TO AN ENGINEER
Everyone sells an API.
Dvar sends an engineer.
Bring one real workload. We build the eval with your team from your own traffic, benchmark the models against it, and stay on the account from pilot through production.
Book a workload review