Laika AI

Technical Architecture

Laika AI's technical architecture is designed to be scalable, secure, and efficient, leveraging the latest advancements in artificial intelligence, blockchain technology, cloud computing, and data analytics. This section outlines the key components and how they interact to deliver a seamless experience to our users.
Core Components:
  1. 1.
    Data Ingestion and Processing:
    • Sources: Real-time data from various cryptocurrency exchanges, blockchain networks, news feeds, and social media.
    • Ingestion Pipelines: Utilizing Kafka for scalable and reliable data streaming.
    • Processing: Employing Apache Spark for high-velocity data processing, ensuring timely and accurate analysis.
  2. 2.
    Artificial Intelligence and Machine Learning:
    • Model Training: Utilizing TensorFlow and PyTorch for developing sophisticated AI models.
    • Deployment: Models are deployed using Docker containers for isolation and Kubernetes for orchestration, ensuring scalability and resilience.
    • Continuous Learning: AI models are continuously updated with new data, enhancing their predictive accuracy over time.
  3. 3.
    Blockchain Integration:
    • Smart Contract Analysis: Ethereum and other blockchain network integrations for accessing and analyzing smart contract data.
    • Transactions Monitoring: Real-time monitoring of blockchain transactions for security and analytics purposes.
  4. 4.
    Database and Storage:
    • MongoDB Atlas: For flexible, scalable NoSQL database solutions, enabling efficient handling of diverse data sets.
    • Cloud Storage: Utilizing Google Cloud Storage for secure and scalable data storage solutions.
  5. 5.
    User Interface and Experience:
    • Front-End: Developed with React.js for a responsive and intuitive user interface.
    • API Gateway: RESTful APIs for seamless integration and interaction between front-end applications and back-end services.
  6. 6.
    Security and Compliance:
    • Encryption: End-to-end encryption for data in transit and at rest.
    • Compliance: Adherence to GDPR, KYC, and AML standards for user data protection and legal compliance.
  7. 7.
    Cloud Infrastructure:
    • Google Cloud Platform: Leveraging GCP for scalable cloud computing resources.
    • Load Balancing: Implementing Google Cloud Load Balancing for distributing user traffic across resources, ensuring high availability and reliability.
  8. 8.
    Monitoring and Maintenance:
    • Logging: Extensive logging using Elasticsearch, Logstash, and Kibana (ELK Stack) for real-time monitoring and analytics.
    • Performance Monitoring: Google Cloud’s operations suite for monitoring system performance and health.
Scalability and Flexibility:
The architecture is designed for scalability, handling the increasing load and user base efficiently. Microservices architecture enables easy integration of new features and technologies, keeping the platform agile and adaptive to market changes.
Last modified 1mo ago