AIMatrix Services

Beyond our core products, AIMatrix offers specialized services that transform raw business data into structured, actionable knowledge. These services bridge the gap between information chaos and intelligent automation, supported by robust technical architecture and enterprise-grade implementation.

Service Portfolio

Knowledge Creation Services

Transform raw data into structured, intelligent knowledge with enterprise-grade processing capabilities:

Knowledge Activation Services

Put your knowledge to work with AI-powered applications and robust backend infrastructure:

Technical Architecture

Microservices Architecture

AIMatrix services are designed as cloud-native microservices with enterprise-grade scalability:

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   API Gateway   │────│  Load Balancer  │────│ Service Mesh    │
│   (Kong/Nginx)  │    │   (HAProxy)     │    │  (Istio)        │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         └───────────────────────┼───────────────────────┘
                                 │
    ┌────────────────────────────┼────────────────────────────┐
    │                            │                            │
┌─────────┐              ┌─────────┐                ┌─────────┐
│ Agent   │              │ Knowledge│                │Content  │
│ Service │              │ Service  │                │Service  │
└─────────┘              └─────────┘                └─────────┘
    │                            │                            │
┌─────────┐              ┌─────────┐                ┌─────────┐
│PostgreSQL│              │Vector DB │                │MongoDB  │
│         │              │(Pinecone)│                │         │
└─────────┘              └─────────┘                └─────────┘

Core Technology Stack

Runtime & Orchestration

• Kubernetes: Container orchestration and scaling
• Docker: Containerization and deployment
• Istio: Service mesh for security and observability
• NGINX/Kong: API gateway and load balancing

Development Languages

• Kotlin/Spring Boot: Primary microservices development
• Python/FastAPI: AI/ML processing services
• TypeScript/Node.js: Frontend and content services
• Go: High-performance networking services

Data & Storage

• PostgreSQL: Primary relational database
• Redis: Caching and session management
• Vector Databases: Pinecone, Weaviate for semantic search
• Apache Kafka: Event streaming and message queues
• Neo4j: Graph database for knowledge relationships

AI & Machine Learning

• PyTorch/TensorFlow: Deep learning frameworks
• Hugging Face Transformers: Pre-trained model integration
• MLflow: Experiment tracking and model management
• ONNX: Model optimization and deployment

API Standards & Security

REST API Design

• Consistent response formats with structured error handling
• OpenAPI 3.0 specifications for all services
• Rate limiting and request throttling
• Comprehensive input validation and sanitization

Authentication & Authorization

• JWT-based authentication with refresh tokens
• OAuth2/OIDC integration for enterprise SSO
• Role-based access control (RBAC) with fine-grained permissions
• API key management for service-to-service communication

Security Implementation

• mTLS for service-to-service communication
• End-to-end encryption for sensitive data
• Comprehensive audit logging and compliance
• Regular security scanning and vulnerability assessments

Knowledge Lifecycle Management

Knowledge Units Architecture

🧬 Knowledge Capsule (Micro-unit)

• Size: 1-5 minutes reading / 200-600 tokens
• Storage: Vector embeddings + metadata + relationships
• Content: One atomic idea, fact, or procedure
• Implementation: Content-addressed storage with SHA-256 hashing
• Purpose: Smallest reusable knowledge unit for AI consumption

📘 Knowledge Volume (Mid-unit)

• Size: 30-60 minutes reading / 10-20 capsules
• Storage: Manifest with capsule references and dependency graph
• Content: Complete topic or domain coverage
• Implementation: Graph-based relationship mapping
• Purpose: Comprehensive resource on specific subjects

📚 Knowledge Library (Macro-unit)

• Size: Unlimited curated collection
• Storage: Distributed catalog with search indices
• Content: Organized catalog of volumes and capsules
• Implementation: Elasticsearch with Neo4j relationships
• Purpose: Centralized organizational intelligence

Service Integration Flow

Complete Knowledge Lifecycle: Raw data sources including Google Drive, meeting videos, and ERP/CRM systems feed into the Knowledge Pipeline and Video Intelligence services through secure connectors and real-time processing. These are processed into knowledge capsules using advanced NLP and computer vision, then organized into volumes through graph-based relationships, and catalogued in comprehensive libraries with semantic search. The structured knowledge powers AI agents, digital twins, and analytics systems through high-performance APIs, creating a unified intelligence ecosystem with enterprise-grade security and compliance.

Implementation & Deployment

Kubernetes Deployment Strategy

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# Service deployment template
apiVersion: apps/v1
kind: Deployment
metadata:
  name: knowledge-service
  namespace: aimatrix
spec:
  replicas: 3
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 0
  template:
    metadata:
      labels:
        app: knowledge-service
        version: v1.0.0
    spec:
      containers:
      - name: knowledge-service
        image: aimatrix/knowledge-service:1.0.0
        ports:
        - containerPort: 8080
          name: http
        - containerPort: 9090
          name: metrics
        env:
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: database-credentials
              key: url
        resources:
          requests:
            memory: "512Mi"
            cpu: "250m"
          limits:
            memory: "1Gi"
            cpu: "500m"
        livenessProbe:
          httpGet:
            path: /health/live
            port: 8080
          initialDelaySeconds: 30
          periodSeconds: 30
        readinessProbe:
          httpGet:
            path: /health/ready
            port: 8080
          initialDelaySeconds: 15
          periodSeconds: 10

Performance & Monitoring

Observability Stack

• Prometheus: Metrics collection and alerting
• Grafana: Visualization and dashboards
• OpenTelemetry: Distributed tracing
• ELK Stack: Centralized logging and analysis

Performance Optimization

• Multi-level caching with Redis and in-memory caches
• Database optimization with proper indexing and partitioning
• Connection pooling and resource management
• Auto-scaling based on metrics and demand

Investment & Pricing

Flexible Service Models

Knowledge Creation Services

• Knowledge Pipeline: From $2,000/month (Starter) to Custom Enterprise
• Video Intelligence: From $500/month (Basic) to Custom Premium
• Knowledge Library: From $1,000/month (Small) to Custom Enterprise

Knowledge Activation Services

• Course Generation: From $800/month to Custom Enterprise
• Content Publishing: From $600/month to Custom Premium
• MCP Development: Custom development starting at $15K
• Software Intelligence: From $1,200/month to Custom Enterprise
• AI Model Training: Custom projects starting at $25K
• Data Hub Integration: Custom implementation starting at $20K

Bundle Options

• Complete Knowledge Suite: 30% savings with all creation services
• Activation Bundle: 25% savings with all activation services
• Enterprise Package: Custom pricing with full suite and premium support

Implementation Investment

Service Development Lifecycle

1. Design Phase: API specification, architecture review, security assessment
2. Implementation Phase: Agile development with continuous integration
3. Deployment Phase: Kubernetes deployment, monitoring setup, security configuration
4. Operations Phase: 24/7 monitoring, performance optimization, ongoing support

Professional Services

• Implementation Consulting: $200-300/hour
• Custom Development: $150-250/hour
• Training & Support: $100-150/hour
• Maintenance & Updates: 15-25% of initial implementation cost annually

Success Metrics & ROI

Typical Results Across Customer Base

Business Impact Examples

Finance Department

• Input: Invoices, receipts, reports, emails
• Processing: Automated extraction with 99.7% accuracy
• Output: Financial knowledge base powering AI accountants
• ROI: 275% reduction in processing time, 90% fewer errors

Sales Team

• Input: Call recordings, CRM data, customer emails
• Processing: Real-time conversation analysis and insight extraction
• Output: Sales playbooks and customer intelligence systems
• ROI: 45% increase in conversion rates, 60% faster onboarding

Operations

• Input: Process documents, training videos, SOPs
• Processing: Automated procedure extraction and optimization
• Output: Operational knowledge powering process automation
• ROI: 65% reduction in manual tasks, 40% faster problem resolution

Getting Started

Service Assessment & Planning

Step 1: Technical Assessment

• Current data infrastructure analysis
• Integration requirements evaluation
• Security and compliance review
• Performance and scalability planning

Step 2: Pilot Implementation

• Select 2-3 high-impact use cases
• Deploy containerized services in test environment
• Connect sample data sources through secure connectors
• Measure performance and business impact

Step 3: Production Deployment

• Full Kubernetes deployment with monitoring
• Enterprise security and compliance configuration
• Integration with existing systems via APIs
• Ongoing optimization and scaling

Enterprise Support Options

Professional Services

• Architecture Consulting: Enterprise-grade design and planning
• Implementation Support: Full deployment assistance
• Custom Development: Specialized service development
• Training Programs: Technical and user training

Managed Services

• 24/7 Operations Support: Complete service management
• Performance Optimization: Continuous improvement
• Security Management: Ongoing security and compliance
• Version Management: Automated updates and maintenance

Contact & Technical Resources

Solutions Architecture Team

• Email: architects@aimatrix.com
• Phone: 1-800-AMX-SERVICES (1-800-269-7378)
• Schedule Architecture Review: Book technical consultation

Developer Resources

• API Documentation: api.aimatrix.com/services
• Technical Specs: docs.aimatrix.com/services
• GitHub Examples: github.com/aimatrix/service-examples
• Developer Community: community.aimatrix.com

Enterprise Sales

• Enterprise Solutions: enterprise@aimatrix.com
• Custom Development: custom@aimatrix.com
• Partnership Opportunities: partners@aimatrix.com

AIMatrix Services: Where Enterprise Data Transforms Into Intelligent Business Operations