Best-fit workflows
- Proprietary terminology and operating procedures
- Repeatable judgments that need consistent standards
- Private or latency-sensitive deployments
Resonance Technology
Service
Domain-tuned models for company-specific work
Domain-tuned models and retrieval systems that make AI understand company terminology, operating rules, documents, and repeatable decisions.
Direct answer: Resonance builds domain-tuned AI systems when generic models do not reliably match the language, policies, decisions, or workflows inside a business.
Who it is for
Systems it connects
- knowledge bases
- document stores
- vector databases
- Postgres
- cloud storage
- internal applications
How production reliability is handled
Reliability controls
- Golden datasets and regression evals
- Retrieval quality checks
- Model and prompt versioning
- Cost, latency, and privacy controls
ROI metrics
- Answer accuracy on domain questions
- Escalation and correction rate
- Cost per resolved task
- Latency for production workflows
Workflow examples
- Answer policy and SOP questions with citations to approved source material
- Classify documents or requests using company-specific categories
- Support smaller specialized models for predictable internal tasks
Anonymized proof: For anonymized model work, proof comes from evaluation sets, retrieval audits, error analysis, latency measurements, and the business decisions the model supports.
Common questions
When is fine-tuning better than retrieval alone?
Fine-tuning helps when the model must consistently follow domain-specific language, classification rules, or output patterns that retrieval alone does not fix.
Retrieval is often the first layer for fresh facts. Fine-tuning is useful when behavior, terminology, and repeatable judgment need to become more consistent.
Can domain-tuned models run privately?
Yes, some domain-tuned systems can use private deployment patterns depending on model choice, latency needs, data controls, and infrastructure constraints.
The right architecture depends on sensitivity, scale, quality requirements, and the cost profile of the workflow.
AI Strategy Call
Review the workflow you want AI to improve
Share the operational process, bottleneck, or outcome you want to improve. We look for fit, integration risk, review requirements, and the most practical first production use case.