Intake and identity
Normalize voice, chat, email or form input. Resolve customer identity, language, account and conversation state before the agent touches any system.
AI CUSTOMER SERVICE AGENTS · ENTERPRISE SUPPORT
Resolve customer requests—not just conversations.
Createting connects customer conversations to verified knowledge, approved tools, operating policies and human escalation. The result is an AI support agent that can complete bounded work across voice, chat, email and service systems.
• Tool-level permissions• Evidence-grounded answers• Approval and deny paths• Complete audit trace
Resolution run
CASE-18427Customer request“My replacement never arrived. Can you resend it to the new address?”Web chat · authenticated account · existing order
01
Understand intentReplacement delivery issue + address change
CONFIDENT02
Verify evidenceOrder, carrier event, replacement policy and account identity
4 SOURCES03
Evaluate policyReship allowed; address mutation requires approval
APPROVAL04
Execute toolsCreate replacement order and update delivery address
WAITINGHuman decision requestedApprove address change and replacement order?
DenyApprove
Definition
An AI customer service agent is a governed system that can understand a request, retrieve supporting evidence, perform explicitly approved actions and transfer unresolved cases to a human with full context.
THE RESOLUTION OPERATING MODEL
One request. Six controlled decisions.
The valuable unit is not a generated answer. It is a correctly resolved case with evidence, permissions, business-system updates and an accountable fallback.
Channels · authentication · context
Intent and risk
Classify the requested outcome and assign a risk tier. A password reset and a disputed invoice should never share the same autonomy policy.
Evidence retrieval
Collect the exact policy, account state, transaction data and prior case history required to justify the response or action.
Policy decision
Evaluate allowed, denied and approval-required actions. The policy layer—not the language model—defines the operational boundary.
Action or escalation
Execute a structured tool call or transfer the case with a summary, evidence set, attempted actions and recommended next step.
Observe and improve
Record the decision path, tool result, model route, latency and human correction. Improvement comes from traces, not prompt guesswork.
Interactive resolution run · representative data
Put a request through the Resolution Contract.
Choose a common service case or write a request. The preview shows how evidence, policy, tools and escalation combine before an action is taken.
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Configured simulation using representative data. It performs no real lookup, customer identification, payment operation or external action.
Incoming request
Duplicate payment refund for order CT-1842
1
Identity and account contextVerified
Account session and order ownership match.
2
Evidence retrieval3 sources
Order ledgerPayment eventsRefund policy v4.2
3
Resolution ContractAllowed
Duplicate charge confirmed. Refund remains below the approval threshold.
4
Tool actionPrepared
Toolpayments.refund
Scope€42.00 duplicate payment
ControlAutomatic · audited
✓
Resolution ready
The approved refund action can execute and the customer receives a confirmation with the audit reference.
Evidence snapshotPolicy v4.2Tool parametersAudit event
Use the full platform workspace to inspect Mission Control, Orchestrator, Pathways, Thinkways and Agent Studio.Explore the platform workspace →
CREATETING RESOLUTION CONTRACT
Every automated intent receives an explicit operating contract.
The contract converts a vague “AI agent” into a reviewable support process. It can be tested before production and audited after every run.
Explore tools, governance and deployment in the documentation →Intent contractreplace_missing_order
Required evidenceIdentity, order state, carrier event, replacement policy
Allowed actionsCreate replacement, send confirmation, update ticket
Approval actionsChange delivery address, waive exception fee
Denied actionsIssue cash refund above limit, edit payment method
Escalation triggersConflicting evidence, repeat claim, identity mismatch
Success conditionReplacement confirmed and customer notified
Audit outputSources, decisions, tool payloads, approvals, final state
WHERE THE SYSTEM EARNS TRUST
Automate the repeatable path. Preserve judgment where it matters.
A support agent should expand only when evidence quality, tool reliability and escalation performance have been demonstrated for the specific intent.
WorkflowWhat the agent can completeControl boundary
Order and delivery supportTrack shipments, explain status, update tickets, create approved replacementsEscalate fraud signals, repeated claims and exception refunds
Appointment and booking changesFind availability, reschedule, cancel and confirm across channelsRequire approval for fee waivers or protected appointments
Account and subscription serviceRetrieve plan details, update permitted fields and execute standard changesDeny credential, payment and ownership changes outside policy
Technical supportDiagnose known issues, run approved checks and guide structured remediationTransfer unknown failures with logs, steps attempted and relevant environment data
Billing clarificationExplain invoices, identify known discrepancies and open structured correction casesEscalate disputed liability, material credits and regulated decisions
PLATFORM ARCHITECTURE
Built as an operating layer across the systems already in use.
Createting does not require every support system to be replaced. Agents use scoped tools and policies to work across existing channels, knowledge and business applications.
Customer channelsVoiceChatEmailMessaging
→
Createting Agent RuntimeOrchestrate · reason · route
KnowledgeEvidence retrieval
ToolsStructured actions
PoliciesAutonomy controls
ModelsWorkload routing
→
Business systemsCRM / HelpdeskERP / BillingBookingDatabases
SUPPORT AUTOMATION OPPORTUNITY MODEL
Estimate the workload worth investigating.
This model does not promise savings. It converts current support volume into a transparent estimate of repeatable workload, time consumption and operational value that could justify a bounded pilot.
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Addressable workload4,500 conversationsA bounded pilot is likely worth evaluating.
Handling time represented675 h / month
Operational value represented€23,625 / month
Equivalent capacity≈ 4.2 FTE
Recommended pilot scope1 high-volume intent
Interpretation
Start with one frequent request where the correct answer, required evidence, allowed tools and escalation route are already known.
A MEASURABLE PILOT
Prove resolution quality before expanding coverage.
A pilot should be narrow enough to diagnose failure and meaningful enough to show whether the operating model deserves production investment.
Resolution outcomeCorrect completion rate by intent
Evidence qualityResponses supported by required sources
Tool reliabilitySuccessful actions and valid system state
Escalation qualityCorrect timing, summary and context transfer
Customer experienceContainment without obstructing human access
EconomicsCost and handling time per resolved case
Week 1Map intent, data, tools and failure modes
Weeks 2–3Build the resolution contract and test suite
Weeks 4–6Run controlled traffic and review traces
DecisionExpand, revise or stop based on measured evidence
WHY CREATETING
One team across agents, integrations and infrastructure.
The support workflow is only one layer. Reliable deployment also requires model strategy, latency, tool contracts, data boundaries, observability and ongoing operations.
01
Platform
Design agents, tools, knowledge, autonomy levels, human approvals and audit trails within one governed runtime.
Explore the agent platform →02
Implementation
Connect CRM, helpdesk, telephony, ERP, databases and internal APIs around the actual support process.
Map the architecture →03
Infrastructure
Choose API-first, hybrid or private deployment based on workload economics, control and operational requirements.
Assess infrastructure options →IMPLEMENTATION QUESTIONS
What enterprise teams usually need to resolve first.
Does an AI customer service agent replace the support team?
No. A sound deployment removes repeatable handling and improves context transfer. Human specialists remain responsible for judgment, sensitive exceptions and cases outside the defined operating contract.
Can it work with an existing CRM or helpdesk?
Yes. Createting connects existing systems through scoped tools and APIs. Replacement is optional; the normal starting point is to preserve the systems of record.
How are hallucinations and incorrect actions limited?
Responses are constrained by required evidence, structured tool schemas, allow/deny policies, approval steps, confidence thresholds, evaluations and complete traces. No single control is sufficient on its own.
What should the first support automation pilot cover?
Choose one high-volume intent with stable data, an objective successful outcome, reliable tools and a known human escalation route. Avoid starting with the most ambiguous or politically sensitive case.
Can the system run privately or in the EU?
Yes. The architecture can be designed around managed APIs, hybrid routing, EU environments or dedicated infrastructure depending on data, latency and cost requirements.
START WITH ONE RESOLUTION CONTRACT
Bring one repetitive support workflow. Leave with a pilot architecture.
The review maps the intent, evidence, tools, policies, escalation path and measurements required to determine whether an AI customer service agent is technically and economically justified.
