Core Processing Flows

sequenceDiagram participant C as Client Application participant G as Gateway participant E as ESB participant S as Speech Processing participant V as Vision Processing participant I as Sensory Integration C->>G: Initialize Processing G->>E: Route Request par Multimodal Processing E->>S: Process Speech E->>V: Process Vision E->>I: Process Sensory end S-->>E: Speech Analysis V-->>E: Vision Analysis I-->>E: Sensory Analysis E->>E: Integrate Results E->>G: Combined Analysis G->>C: Processing Complete

Reference Implementation: STRATEVITA

The following sequence diagrams demonstrate how STRATEVITA leverages IAXOV's capabilities for competency assessment. This serves as one example of how IAXOV's multimodal processing can be applied in enterprise applications.

sequenceDiagram participant C as Client participant G as Gateway participant I as IAXOV Services participant A as AI Agent participant V as Validation System C->>G: Initialize Assessment G->>I: Create Session I->>A: Initialize AI Agent A->>I: Ready Status I->>C: Join Session rect rgb(200, 255, 200) Note over C,V: Assessment Process C->>A: Participant Joins A->>A: Context Establishment A->>A: Initial Assessment loop Deep Dive A->>C: Structured Interaction C->>A: Response A->>V: Validate Response V->>A: Validation Result end end rect rgb(255, 220, 220) Note over A,V: Quality Assurance A->>V: Submit Assessment V->>V: Multi-LLM Review V->>V: Bias Detection V->>V: Evidence Validation V->>I: Final Assessment end I->>C: Assessment Complete

Data Processing Pipeline

graph TD A[Input Streams] --> B[Signal Processing] B --> C[Feature Extraction] C --> D[Context Analysis] D --> E[Pattern Recognition] E --> F[Multi-Model Integration] F --> G[Quality Validation] G --> H[Result Synthesis] H --> I[Final Output] style A fill:#f9f,stroke:#333,stroke-width:2px style B fill:#bbf,stroke:#333,stroke-width:2px style C fill:#dfd,stroke:#333,stroke-width:2px style D fill:#ffd,stroke:#333,stroke-width:2px style E fill:#ddd,stroke:#333,stroke-width:2px style F fill:#ddd,stroke:#333,stroke-width:2px style G fill:#ddd,stroke:#333,stroke-width:2px style H fill:#ddd,stroke:#333,stroke-width:2px style I fill:#ddd,stroke:#333,stroke-width:2px

Asynchronous Multi-Model Validation

sequenceDiagram participant C as Client participant A as AI Agent participant Q as Validation Queue participant V as Validators participant D as Database participant R as RL Pipeline C->>A: User Input A->>A: Generate Response A-->>C: Immediate Response A->>Q: Queue for Validation Note over A,C: Real-time Flow Note over Q,R: Background Processing Q->>V: Distribute Tasks par Parallel Validation V->>V: Bias Check V->>V: Factual Verification V->>V: Quality Assessment end V->>D: Store Results D->>R: Feed Training Data R->>R: Quality Analysis R->>R: Model Training R->>A: Model Updates

Reinforcement Learning Pipeline

sequenceDiagram participant D as Database participant F as Data Filter participant P as Preprocessor participant T as Trainer participant M as Model Registry D->>F: Raw Validation Data F->>F: Quality Filtering F->>P: High-Quality Examples P->>P: Feature Extraction P->>P: Data Augmentation P->>T: Training Data T->>T: Model Training T->>T: Performance Evaluation alt Meets Quality Threshold T->>M: Register New Model M->>M: Version Control M->>M: Deployment Prep else Below Threshold T->>P: Request More Data end

Quality Metrics Collection

graph TD A[Validation Results] --> B[Real-time Metrics] A --> C[Historical Analysis] B --> D[Response Quality] B --> E[Bias Detection] B --> F[Factual Accuracy] C --> G[Quality Trends] C --> H[Issue Patterns] C --> I[Performance Metrics] G --> J[Model Updates] H --> J I --> J style A fill:#f9f,stroke:#333,stroke-width:2px style B fill:#bbf,stroke:#333,stroke-width:2px style C fill:#dfd,stroke:#333,stroke-width:2px style J fill:#ffd,stroke:#333,stroke-width:2px