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