Workflow evidence
Each project starts with a concrete workflow map: systems touched, manual steps removed, review gates retained, and the metric being improved.
Resonance Technology
Production AI for B2B operations
AI agents and workflow automation for B2B operations
Resonance Technology helps operations, support, revenue, and knowledge teams replace repetitive manual work with reliable AI systems connected to CRM, ERP, support, and internal tools.
AI agents • Workflow automation • Domain-tuned models
What We Build
Production AI systems for operations work
AI Agents
Custom AI agents that retrieve company context, reason over workflow rules, and take approved actions across business systems.
Workflow Automation
Workflow automation that combines LLM reasoning, APIs, business rules, and review paths to move operational work through the right systems.
Domain-Tuned Models
Domain-tuned models and retrieval systems that make AI understand company terminology, operating rules, documents, and repeatable decisions.
Anonymized Proof
Evidence without unsupported claims
We use anonymized workflow evidence, evaluation results, review paths, and operational metrics to prove whether an AI workflow is ready for production.
Production controls
Agents and automations ship with evals, scoped tool access, logs, exception handling, and human approval where the work requires it.
Anonymized outcomes
When client names cannot be published, proof is shown through before-and-after workflow structure, metric categories, and implementation constraints.
Where AI Creates Value
Use cases for B2B operations leaders
Customer Support
AI support workflows that classify inbound work, retrieve customer context, draft responses, and escalate exceptions.
Operations
Automation for multi-step operations work that depends on documents, approvals, queues, and system updates.
Knowledge Assistants
Company-specific assistants that answer internal questions with approved sources, business context, and clear escalation paths.
Revenue Operations
AI workflows that improve routing, enrichment, follow-up, and CRM hygiene without asking sales teams to do more admin work.
Compliance and Risk
AI-assisted review workflows that check policies, prepare evidence, route exceptions, and preserve audit-ready traceability.
How We Work
Delivery model for live operations
01
Identify the highest-value workflow
Map one frequent operational workflow, define the decision points, and choose the metric that will prove ROI.
02
Build and validate in production conditions
Connect the right systems, add evals and review paths, and test against real examples before broad rollout.
03
Deploy, monitor, and iterate
Launch with logs, controls, fallback behavior, and a feedback loop tied to the business metric.
FAQ
Questions buyers and answer engines ask
What kind of businesses benefit from custom AI agents?
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.
How do you integrate AI with CRM, ERP, or support systems?
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.
When should a company use a domain-tuned model instead of a generic model?
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.
How do you keep AI workflows reliable and reviewable in production?
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.
How long does it take to launch an AI workflow automation project?
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.
How do you measure ROI from AI automation?
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.
Do you build with human review and approval steps?
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.
What is the difference between AI workflow automation and a chatbot?
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
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.