Resonance Technology
FAQ
Direct answers for B2B operations leaders evaluating AI agents, workflow automation, and domain-tuned models for live business processes.
Businesses with high-volume, repeatable workflows across internal systems benefit most from custom AI agents.
Resonance builds AI agents for B2B operations teams that need faster work across CRM, ERP, support tools, and proprietary knowledge systems, especially when manual routing, review, and follow-up slow the business down.
We integrate AI by connecting agents and workflow automation directly to the systems your teams already use.
That usually means combining API access, business rules, retrieval, and review paths so AI can gather context, draft actions, update approved records, and hand work back to people inside CRM, ERP, support tools, and internal applications.
A domain-tuned model makes sense when generic models do not reliably match your workflows, language, or decision standards.
Resonance uses domain-tuned and fine-tuned models when teams need stronger accuracy on proprietary terminology, structured business logic, or repeatable judgments that affect operations, compliance, or customer experience.
We keep AI workflows reliable by adding observability, evals, controls, and human review where the process requires it.
Production AI systems need more than prompts. We design review paths, fallback logic, logging, monitoring, and tool permissions around live workflows so outputs are traceable, measurable, and safer to run inside real operations.
Launch timing depends on workflow complexity, integration depth, and review requirements, but the first production use case should be narrow and measurable.
The process starts by identifying the highest-value workflow, validating it in production conditions, and deploying with observability and controls instead of trying to automate a broad transformation program all at once.
We measure AI automation ROI against the operational metric the workflow is supposed to improve.
Typical metrics include reduced manual handling time, faster response or approval cycles, lower rework, improved throughput, better SLA attainment, and cleaner pipeline or case management across CRM, ERP, support, and internal process work.
Yes, we build review and approval steps into AI systems whenever the workflow needs oversight, escalation, or sign-off.
That includes approval gates for sensitive actions, exception handling for uncertain outputs, and escalation paths that let teams keep control while still automating the repetitive parts of the workflow.
A chatbot mainly answers or drafts messages, while AI workflow automation completes structured process steps across systems under defined business rules.
Resonance focuses on production workflows: retrieving approved context, preparing decisions, updating systems when allowed, escalating exceptions, and measuring operational outcomes.
AI Strategy Call
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.