Explore two published examples of model specialization. Customer names are redacted in this presentation; original sources and comparison details are preserved.
These are industry research references, not Finetuned customer engagements or endorsements.
A post-trained open-weight model improved a specific fraud workload against a frontier-model baseline.
Task-specific training improved cited document answers while reducing the cost of every review-table cell.
Anonymized adaptations of public research, not Finetuned customer engagements. Results are workload-specific; comparison details and original sources are included in each case.
Discuss a similar workflowA specialist can outperform a larger general model on a demanding task. The decision still depends on your data, evaluation set, runtime and quality threshold.
Compare against the model and workflow you would otherwise use, with the same task constraints.
Include correctness, evidence, missed cases and escalation behavior relevant to the domain.
Count inference, compute, hosting, maintenance and human review alongside project investment.
Validate latency, access controls, data boundaries and performance under realistic conditions.
We’ll help define the result worth proving.
Plan your model