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ADR-19-Architecture-Characteristics.md

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ADR 19: Architecture Characteristics for ClearView System

Date:

2024-09-30

Status:

Accepted

Context:

The ClearView application aims to provide an intelligent recruitment platform that leverages AI LLM (Large Language Model) for resume recommendations, candidate scoring, and job matching while integrating seamlessly with multiple HRMS (Human Resource Management Systems) platforms. Given the sensitivity of data, the complexity of workflows, and the need for personalized and adaptive experiences, the architectural design must prioritize security, performance, and interoperability. These characteristics will address the system's requirements for handling large datasets, real-time AI interactions, and seamless integrations with external HRMS.

Architectural Characteristics

  1. Security

    • Rationale: ClearView handles sensitive personal and company data, making security paramount. The system must protect against unauthorized access, data breaches, and AI model exploitation, and comply with data privacy regulations such as GDPR and CCPA.
    • Approach:
      • Data encryption (in transit and at rest) for sensitive data.
      • Role-based access control (RBAC) and multi-factor authentication for user access.
      • Regular security audits and vulnerability assessments.
      • Logging and monitoring for security events, with alerts for any suspicious activities.
    • Impact: Ensures data confidentiality, integrity, and availability, meeting regulatory requirements and preventing reputational damage.

  2. Performance

    • Rationale: The system must provide low-latency responses for AI-driven recommendations, real-time resume parsing, goal setting, and report generation, even under peak loads. Performance is also essential to support seamless HRMS integration without lag.
    • Approach:
      • Use of high-performance AI infrastructure (e.g., GPUs for AI processing).
      • In-memory data caching to reduce latency in frequent data retrieval.
      • Asynchronous data fetching from HRMS systems and parallel processing for AI tasks.
      • Optimized database indexing for fast data queries.
    • Impact: Improves user experience by reducing waiting times, allows faster hiring decisions, and ensures consistent performance regardless of load.

  3. Scalability

    • Rationale: The system needs to accommodate varying loads in terms of candidate applications, job postings, and HRMS data integration. Scalability ensures the system remains responsive as data volumes grow.
    • Approach:
      • Microservices-based architecture to enable independent scaling of components.
      • Containerization and orchestration using Kubernetes for dynamic resource management.
      • Auto-scaling mechanisms to handle peak workloads efficiently.
    • Impact: Supports growing business needs, allows for quick adaptation to changes in data volume, and optimizes cost through dynamic resource allocation.

  4. Interoperability

    • Rationale: ClearView integrates with multiple HRMS platforms to fetch job data and candidate information. Ensuring smooth interoperability is key to providing a unified experience and maintaining consistent data exchange.
    • Approach:
      • Standardized API interfaces (e.g., REST, GraphQL) for external HRMS systems.
      • Data transformation and mapping layer to standardize data from various formats.
      • Use of middleware for protocol translation and message brokering.
    • Impact: Facilitates easier integration with new HRMS platforms, reduces integration complexity, and ensures consistent data quality across different systems.

  5. Data Integrity

    • Rationale: As the system processes personal data and anonymizes user information, maintaining data integrity is crucial for accuracy in AI-driven recommendations and compliance with data protection laws.
    • Approach:
      • Implement transactional data processing with ACID (Atomicity, Consistency, Isolation, Durability) properties.
      • Regular data validation checks and consistency checks across integrated systems.
      • Versioning of data models to support backward compatibility.
    • Impact: Prevents data loss or corruption, ensuring accurate AI processing and consistent results across workflows.

  6. Adaptability

    • Rationale: The system must be flexible enough to handle diverse resume formats, different HRMS standards, and personalized content requirements to cater to a variety of user needs.
    • Approach:
      • AI models fine-tuned for diverse data types and scenarios.
      • Rule-based fallback mechanisms for non-standard inputs.
      • Configuration-driven workflows to support different integration requirements.
    • Impact: Enhances system flexibility, allowing for smooth integration with various data formats and third-party systems.

  7. Workflow

    • Rationale: Integration with AI LLM and multiple HRMS systems requires complex workflows for data fetching, scoring, and generating recommendations. These workflows need to be efficiently orchestrated to minimize errors and ensure consistency.
    • Approach:
      • Use of an event-driven architecture for asynchronous process handling.
      • Workflow automation tools for managing complex processes (e.g., Apache Airflow).
      • Detailed logging and traceability to monitor the execution of workflows.
    • Impact: Streamlines operations, reduces manual interventions, and ensures accurate execution of automated tasks.

Top Three Architectural Characteristics

  1. Scalability:

    • Supports growing business demands by efficiently managing increased data and traffic.
    • Ensures that AI processing can expand to handle more complex tasks and larger datasets.
    • Enables the system to dynamically adjust resources to maintain performance during these high-demand periods.

  2. Performance:

    • Ensures low-latency interactions with AI and HRMS systems.
    • Supports fast processing of large datasets.
    • Improves user experience and decision-making speed.

  3. Interoperability:

    • Enables seamless integration with multiple HRMS systems.
    • Facilitates standardized data exchange.
    • Future-proofs the system for new integrations.

Alternatives Considered

  1. Monolithic Architecture: Rejected due to the need for flexible scaling and independent service management.
  2. On-Premises Deployment: Rejected because of the higher cost and reduced scalability compared to cloud-native solutions.
  3. Traditional Rule-Based Systems: Rejected in favor of AI-based solutions for better adaptability and semantic processing.

Consequences

  • Positive:

    • Secure and compliant system capable of handling large-scale data.
    • Improved performance in real-time resume processing and HRMS integration.
    • Flexible integrations with existing and new HRMS platforms.

  • Negative:

    • Initial setup may be complex due to security configurations and integration requirements.
    • AI-driven processing can be resource-intensive, potentially increasing costs.
    • High degree of complexity in managing workflows with multiple integrations.