AUTOSPHERE AI | TECHNICAL RESOURCESEnterprise-Grade Architecture, Built on Access Control & Traceability
Voice services, model inference, enterprise knowledge, service APIs, data governance, and operational monitoring form an AI customer service architecture that can be validated and expanded in stages.
AUTOSPHERE SYSTEM BLUEPRINT ● Architecture OverviewCHANNEL Phone Voice · LINE · WebVOICE Speech Recognition · Interruption Handling · Voice Response · Human TransferAI CORE Intent Understanding · Model Routing · Enterprise Knowledge · Controlled ToolsDATA & API CRM · DMS · ERP · Booking · Tickets · Parts DataGOVERNANCE Access Control · Audit · Versioning · Monitoring · FailoverTRACE Sources & RecordsCONTROL Access & ConfirmationOBSERVE Latency & QualityTECHNICAL ARCHITECTURE OVERVIEWFrom Customer Contact to Service Completion
Each layer can be independently validated, monitored, and replaced, avoiding dependency on a single model or external system.
Customer Channels Phone · LINE · Web→Voice / Text Processing Recognition · Synthesis · Context→AI Model Routing Fast · Deep · Human→Knowledge & Enterprise Data Documents · APIs · Permissions→Service Execution Response · Write · TransferVOICE & SERVICE LAYERNatural Conversations with Controlled Service Execution
Voice Services
Supports speech recognition, voice responses, and conversational context.
- Interruption & barge-in handling
- Human transfer by phone
- Call records & summaries
Enterprise Knowledge
Retrieves approved enterprise content to generate traceable answers.
- FAQs & SOPs
- Product catalogs & service terms
- Version & source tracking
Service Tools
Real-time status and write operations are completed through enterprise APIs.
- Booking & time-slot inquiries
- Tickets & repair status
- Execution result confirmation
AI MODEL ROUTINGSelect the Right Processing Path Based on Complexity
Not every request should be handled by the same model. Routing strategies must balance speed, cost, accuracy, and service risk.
FAST PATH Frequently Asked Questions
Use fast models with approved knowledge to handle business information and standard questions.
DATA PATH Real-Time Queries
Use enterprise APIs to retrieve booking, inventory, or ticket status in real time.
DEEP PATH Complex Questions
Multi-document or multi-step requests enter deeper reasoning and verification workflows.
HUMAN PATH Human Handling
High-risk, sensitive, or professionally judgment-dependent requests are transferred to human agents.
SECURITY & GOVERNANCEData Isolation, Operation Confirmation & Complete Auditing
Security must be implemented across identity, data, tools, and operation results—not only through model prompts.
Data & Access Governance
- Enterprise & brand tenant isolation
- Store & role-based permissions
- Data row & index isolation
- Sensitive information masking
- Knowledge version management
- Query & response logging
Transaction & Write Safety
- Customer intent confirmation
- Identity & authorization verification
- Idempotency controls to prevent duplication
- Timeout & retry limits
- Complete input & output auditing
- Confirm success before reporting completion
RELIABILITY & FAULT DEGRADATIONWhen External Services Fail, AI Does Not Pretend the Task Is Complete
When models, telecom services, or enterprise APIs become unavailable, the system switches to controlled fallback workflows.
Detect Failure
Monitor timeouts, error responses, model status, and external APIs.
Create Follow-Up Task
When confirmation is unavailable, create a pending task, callback request, or customer service ticket.
Human Fallback
Preserve the conversation and query results, then transfer the case to a human agent.
DEPLOYMENT & COMPUTEConfigure Infrastructure Based on Data Responsibility and Workload
Deployment methods should be evaluated according to enterprise security policies, existing systems, latency targets, and usage scale.
Cloud Deployment
Suitable for pilots and general enterprise use, enabling faster resource allocation and service scaling.
Private Deployment
Suitable for enterprises requiring stronger data isolation and environment control.
Hybrid Deployment
Sensitive data remains within the enterprise environment, while other services can be flexibly deployed according to requirements.
Compute Configuration Principles
RTX can serve as a baseline for testing voice and general-purpose services. HGX B300 may be considered for complex reasoning and large-model workloads. Final deployment decisions should be based on model size, quantization method, concurrent workload, latency targets, and stress-test results.CAPACITY VALIDATION SCENARIOStress-Test and Validate Stability Using Measurable Conditions
20,000 Calls / Day Test Scenario4 min AI Service Segment per Call~334 Peak Concurrent Processing ScenarioImportant Note: The figures above represent capacity-planning and stress-testing scenarios. They do not represent current customer volume and do not constitute a formal capacity guarantee. Actual capacity should be determined based on test reports after integration with telecom services, AI models, enterprise APIs, and the deployment environment.TESTING & ACCEPTANCEValidate Quality, Performance & Security Metrics
Each development stage should have measurable acceptance criteria, with testing records and improvement evidence retained.
Speech recognition accuracyAnswer hit rate & source citationEnterprise API operation success rateAverage & peak response timeHuman transfer success rateIncorrect-answer & refusal mechanismsPeak-load & stability testingExternal service interruption testingAccess isolation & complete auditing
