diff --git a/LTI-ADFR/LTI-ADFR.md b/LTI-ADFR/LTI-ADFR.md new file mode 100644 index 0000000..9aeddcf --- /dev/null +++ b/LTI-ADFR/LTI-ADFR.md @@ -0,0 +1,66 @@ +Creating a highly effective Applicant Tracking System (ATS) requires an understanding of not only the essential functions of existing systems but also innovations that streamline the recruiting process, enhance collaboration, and introduce automation and AI tools. Let’s address each of the goals for defining an optimal ATS in detail. + +--- + +### 1. **Description of the ATS and Competitor Analysis** + +An **Applicant Tracking System (ATS)** is a software solution designed to streamline recruitment by organizing, tracking, and managing the entire hiring process—from job posting to candidate evaluation and hiring decisions. Modern ATS solutions offer features such as resume parsing, job posting distribution, interview scheduling, and applicant communications, while also enabling analytics to optimize recruitment performance. + +#### Key Competitors in the ATS Market: +1. **Workday Recruiting** - A part of the Workday HCM suite, focusing on end-to-end hiring with seamless integration with HR functions. Known for robust analytics, reporting, and integration with other Workday solutions. +2. **Greenhouse** - Known for its interview planning and candidate tracking. Greenhouse offers strong collaboration features and integrates with third-party applications for extended functionality. +3. **Lever** - A collaborative ATS with strong candidate relationship management (CRM) tools, Lever emphasizes diversity hiring and candidate nurturing with data-driven insights. +4. **BambooHR** - An ATS aimed at small to mid-sized companies, focusing on simplicity and ease of use, with integrated HR capabilities, though it may lack advanced AI capabilities. +5. **SmartRecruiters** - Popular for its user-friendly interface and comprehensive functionality, including global hiring support, mobile accessibility, and robust automation for applicant communication. + +While each of these solutions addresses recruitment needs effectively, there are areas where productivity enhancements, real-time collaboration, and intelligent automation can be further emphasized. The following sections outline how we can differentiate our ATS in these areas. + +--- + +### 2. **Added Value of the ATS** + +Our ATS aims to deliver significant **added value** through a combination of productivity improvements, real-time collaboration, automation, and AI-driven functionalities. Here’s how each component contributes: + +- **Productivity Boosts**: + - **Automated Workflow Management**: Automating repetitive tasks like resume screening, candidate outreach, and interview scheduling, reducing manual effort by 30-50%. + - **Task Assignment and Reminders**: Integrated task assignment for both recruiters and hiring managers, with reminder notifications to avoid bottlenecks. + - **Time-saving Templates**: Pre-set templates for emails, job descriptions, and candidate evaluations, ensuring consistency and speeding up communication. + +- **Real-Time Collaboration**: + - **Centralized Communication Hub**: A built-in chat and collaboration module for real-time discussions on candidate progress, interview feedback, and hiring decisions. + - **Live Document Sharing**: Allows recruiters and managers to access candidate profiles, evaluation forms, and interview feedback in real-time for prompt decision-making. + - **Role-based Access**: Ensures that sensitive data is accessible only to authorized users, fostering a secure collaborative environment. + +- **AI-Driven Enhancements**: + - **Smart Resume Parsing and Matching**: AI algorithms to quickly match resumes with job descriptions, prioritizing top candidates for recruiter review. + - **Predictive Analytics**: Analyzes historical data to predict the likelihood of a candidate’s success in the role, helping managers make informed decisions. + - **Sentiment Analysis**: Automatically scans feedback and candidate communications for sentiment to gauge candidate experience, enabling HR to address issues proactively. + +--- + +### 3. **Competitive Advantages of Our ATS Proposal** + +Our ATS aims to stand out with a strong emphasis on next-level automation, AI insights, and dynamic collaboration. Here are some of the core **competitive advantages**: + +1. **Advanced Automation for Efficiency Gains**: + - **Intelligent Process Automation**: Our ATS goes beyond simple task automation by using machine learning algorithms to adapt workflows. For example, after an initial candidate pool is set, the system can dynamically update candidate priority based on recruiter activity and feedback. + - **Automated Compliance and Diversity Monitoring**: Automatically monitors and flags any discrepancies in diversity hiring goals, ensuring compliance with legal and internal guidelines. + +2. **Enhanced Collaboration Tools for Real-Time Decision Making**: + - **Recruiter-Manager Collaborative Dashboards**: Customizable dashboards where recruiters and hiring managers can view real-time recruitment status, share insights, and act on pending tasks. + - **Real-Time Feedback and Decision Log**: Maintains a live feedback thread per candidate, so each decision and feedback point is logged and timestamped, enhancing transparency and collaboration. + +3. **AI-Powered Candidate Insights and Recommendations**: + - **Dynamic AI Assistance**: Our ATS leverages AI not only for resume parsing but for personalized recommendations for recruiter outreach strategies and candidate engagement, improving both quality and speed in hiring. + - **Predictive and Prescriptive Analytics**: Predictive insights help anticipate recruitment timelines and candidate fit, while prescriptive analytics suggest actionable steps to improve hiring outcomes. + - **Candidate “Fit” Scoring**: Through AI models, our ATS evaluates a candidate’s fit based on a range of data points, including resume content, assessment scores, and cultural alignment with the company, thus saving time in reviewing profiles that might be less suited. + +4. **Superior User Experience and Mobile Accessibility**: + - **Mobile-First Design**: Our ATS is designed to be mobile-responsive, allowing recruiters and managers to review candidates, schedule interviews, and communicate even when they’re away from their desks. + - **Intuitive and Adaptive UI**: An easy-to-use interface with adaptive learning, so frequent actions are placed upfront for quicker access, minimizing the need for user training and reducing time-to-value. + +--- + +### Summary + +Our ATS redefines the recruitment experience by addressing critical HR needs through enhanced productivity tools, real-time collaboration, and AI-powered automation. Key differentiators include advanced automation, intelligent candidate matching and predictive analytics, as well as a seamless, mobile-friendly interface designed for optimal user experience. These features combine to make the hiring process more efficient, effective, and collaborative, ultimately leading to better recruitment outcomes and a competitive edge in the ATS market. \ No newline at end of file diff --git a/LTI-ADFR/Requesting_questions/LTI-ADFR.md b/LTI-ADFR/Requesting_questions/LTI-ADFR.md new file mode 100644 index 0000000..60f5857 --- /dev/null +++ b/LTI-ADFR/Requesting_questions/LTI-ADFR.md @@ -0,0 +1,1002 @@ +With this additional information, we can develop an ATS focused on the IT hiring market, optimizing the recruitment process for roles that are often highly specialized, competitive, and in-demand. Here’s a structured approach covering the goals you've outlined: + +--- + +### 1. **ATS Description and Competitor Landscape** + +**Description:** +This ATS will be an advanced, configurable platform tailored to the unique challenges of recruiting IT professionals. It will offer end-to-end recruitment workflow customization, real-time collaboration tools for recruiters and hiring managers, and powerful AI-driven capabilities. By supporting complex workflows and facilitating seamless coordination, the ATS will enable hiring teams to focus on securing top talent quickly and efficiently. + +**Competitors**: +Here’s an overview of leading ATS platforms and areas for potential differentiation: + +- **TeamTailor**: Known for its user-friendly interface and branding capabilities. It supports some automation and collaboration but lacks deep, customizable AI for screening IT candidates. +- **Greenhouse**: Widely used for its robust data analytics and interview scheduling features. Greenhouse, however, has limited real-time collaboration and workflow customization options, making it less flexible for niche industries like IT. +- **Lever**: Strong in candidate relationship management (CRM), ideal for building talent pools and engaging passive candidates. Lever’s AI capabilities are somewhat limited, especially for industry-specific skills analysis. +- **SmartRecruiters**: A feature-rich ATS with AI-enabled recommendations and smart automation tools, but it is generally not as customizable in terms of workflows and role-based permissions. +- **Workday Recruiting**: Comprehensive functionality with integrations to other Workday modules (e.g., HR, payroll) and some AI capabilities. Its complex setup, however, can be a challenge for companies needing flexible and easy-to-adjust workflows. + +**Differentiation Opportunity**: +To stand out, our ATS could combine robust, configurable workflows with industry-specific AI (e.g., tech stack skill matching) and real-time collaboration, a blend few competitors fully offer. + +--- + +### 2. **Added Value of the ATS** + +Our ATS would offer the following added value, specifically aimed at IT-focused recruiting needs: + +- **Configurability and Workflow Flexibility**: Unlike traditional ATSs, this solution will allow recruiters and managers to tailor workflows according to specific roles, regions, or team preferences. This flexibility ensures that the ATS can handle the varying recruitment processes that may differ between IT consulting and corporate IT departments. + +- **Enhanced Candidate Screening**: By leveraging AI-driven candidate screening tools that analyze technical skills, past experiences, and specific certifications, this ATS will help match IT candidates more accurately to roles. This includes support for screening based on skills in coding languages, frameworks, and project experience relevant to the role. + +- **Seamless Real-Time Collaboration**: Built-in collaborative features will let recruiters, hiring managers, and stakeholders interact within the ATS, enabling discussions, shared notes, and feedback that streamline decision-making. Push notifications and integrated messaging allow for immediate action, enhancing responsiveness in a competitive hiring market. + +- **In-Depth Analytics and Custom Metrics**: Key metrics relevant to IT recruiting, like time-to-hire for specific technical roles, source effectiveness, and diversity metrics, will be tracked and displayed in customizable dashboards. This data will help hiring teams continuously refine their approach. + +--- + +### 3. **Competitive Advantages of Our ATS Proposal** + +Our ATS will incorporate distinct features that address both the complexities of the IT recruitment landscape and the need for flexibility, efficiency, and enhanced collaboration: + +1. **Industry-Specific AI Assistance**: + - Our AI module will be trained to understand IT-related qualifications, certifications, and skills, which are often highly specialized and change frequently. This industry-specific AI screening goes beyond general AI filtering to match candidates based on tech stacks, languages, frameworks, or even familiarity with specific methodologies (e.g., Agile, DevOps). + - It will provide automatic scoring for candidate resumes and suggest suitable candidates to managers in real-time, significantly cutting down the screening phase. + +2. **Automation Across the Hiring Pipeline**: + - We will automate recurring tasks, like candidate follow-ups, interview scheduling, and pipeline updates, allowing recruiters to manage a larger volume of candidates with reduced manual effort. + - Automation will also extend to sending updates or nudges to candidates based on their engagement level or inactivity, enhancing candidate experience and reducing drop-offs. + +3. **Configurable Workflows and Access Permissions**: + - Unlike many competitors, our ATS will offer highly configurable workflows that can adapt to specific team requirements or organizational policies, ensuring it’s valuable for both consultancy firms and IT departments in corporate environments. + - Hiring managers will have full pipeline visibility and granular control, enabling them to add comments, rate candidates, and approve or reject applicants seamlessly, which minimizes bottlenecks and improves accountability. + +4. **Real-Time Collaboration and Communication**: + - The system will feature built-in chat, notifications, and comment threads to facilitate active collaboration. For example, a recruiter and hiring manager can discuss a candidate’s progress and adjust the evaluation criteria on the go. + - Integration with communication tools (e.g., Slack, Microsoft Teams) will provide instant updates to teams, so everyone stays informed without logging into the ATS. + +5. **Advanced Reporting and Customizable Metrics Dashboards**: + - Custom dashboards will give users clear visibility into hiring trends, allowing them to track how the recruitment process performs in areas specific to IT hires (like role-based time-to-hire and technical skill gaps). + - Predictive analytics can help teams anticipate hiring bottlenecks and adjust proactively. For example, if time-to-fill for a specific skillset increases, the ATS can suggest expanding sourcing channels or tweaking the job description. + +--- + +### Summary + +Our ATS will stand out in the market by offering a unique combination of AI-driven IT-specific candidate screening, highly configurable workflows, and advanced real-time collaboration tools. This configuration supports recruiters, hiring managers, and HR leaders alike, boosting productivity, optimizing collaboration, and ensuring a smooth, efficient, and engaging experience for both candidates and hiring teams. + +This solution’s adaptability for various IT recruitment needs, combined with its proactive AI and analytics features, makes it a compelling choice for companies looking to stay competitive in attracting top IT talent. + + +--- + +### Main Features of the New ATS (in Order of Relevance) + +1. **Industry-Specific AI Candidate Screening** + - *Relevance*: High. IT hiring often requires candidates with niche skills, making tailored AI-driven screening highly valuable. + - *Details*: AI algorithms assess candidates for specific technical skills, certifications, and relevant experiences. This feature will reduce manual screening time, helping teams identify top candidates quickly. + +2. **Configurable Recruitment Workflows** + - *Relevance*: High. Different organizations and roles require varying recruitment steps, especially in IT. + - *Details*: Customizable workflows let recruiters design hiring processes tailored to specific roles or departments. This includes flexible steps for screening, interviewing, and decision-making to match each organization’s unique requirements. + +3. **Real-Time Collaboration Tools** + - *Relevance*: High. Fast feedback loops and collaboration are essential, especially in competitive hiring environments. + - *Details*: In-app communication features (like chat, comments, and tagging) enable recruiters and hiring managers to collaborate on candidate evaluations and streamline decision-making. + +4. **Automated Task Management** + - *Relevance*: Medium to High. Automating routine tasks increases productivity by reducing repetitive, time-consuming tasks. + - *Details*: Automations handle follow-up emails, interview scheduling, and candidate updates. They can also prompt hiring managers for feedback to keep the hiring process on track. + +5. **Customizable Analytics and Reporting** + - *Relevance*: Medium to High. Data-driven hiring decisions improve the quality and efficiency of the recruitment process. + - *Details*: Custom dashboards display key metrics like time-to-hire, cost-per-hire, source effectiveness, and diversity metrics. Predictive analytics can provide insights into pipeline bottlenecks and suggest actions. + +6. **Integration with Communication and Productivity Tools** + - *Relevance*: Medium. Seamless integration with existing tools simplifies the adoption of new software. + - *Details*: Integrations with platforms like Slack, Microsoft Teams, and email systems allow hiring updates and reminders to flow directly into these tools, helping recruiters and managers stay aligned without switching platforms. + +7. **Automated Candidate Engagement** + - *Relevance*: Medium. Reduces candidate drop-off and improves their experience, especially in competitive IT job markets. + - *Details*: AI-powered chatbots and automated messaging keep candidates informed and engaged throughout the process, reducing the chances of losing top talent. + +--- + +### Lean Canvas Diagram for the ATS Business Model + +Here’s a Lean Canvas to provide a snapshot of the ATS business model, focusing on core components and target user needs. + +| Section | Description | +|-----------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| **Problem** | - Difficult to screen IT candidates with specialized skills.
- Slow feedback and collaboration between recruiters and hiring managers.
- High dropout rates from candidates due to prolonged processes or poor engagement. | +| **Customer Segments** | - HR departments in IT consulting firms, tech companies, banks, insurance, and retail companies hiring tech talent.
- Hiring managers and technical leaders responsible for IT recruitment. | +| **Unique Value Proposition** | A customizable ATS for IT recruitment, featuring industry-specific AI screening, automated workflows, and real-time collaboration, enabling faster and more accurate hiring decisions for tech roles. | +| **Solution** | - AI screening tailored to tech roles for rapid identification of qualified candidates.
- Configurable workflows to suit varied hiring processes.
- Real-time collaboration tools that facilitate continuous feedback from hiring managers. | +| **Channels** | - Direct sales to large companies with high-volume IT hiring needs.
- Partnerships with HR consultancies and tech industry associations.
- Online marketing through industry-specific platforms (e.g., LinkedIn, HR tech forums). | +| **Revenue Streams** | - SaaS subscription model based on company size and monthly active users.
- Premium add-ons (e.g., enhanced AI reporting, specialized integrations).
- Custom onboarding and training services for enterprise clients. | +| **Key Metrics** | - Customer acquisition rate (new clients adopting the ATS).
- Time-to-hire reduction for users.
- Candidate satisfaction scores (via feedback tools integrated into the candidate journey). | +| **Cost Structure** | - Development and maintenance of AI algorithms and software infrastructure.
- Sales and marketing, particularly focused on B2B channels.
- Customer success and support services. | +| **Unfair Advantage** | - AI trained specifically for IT job market needs, offering skill-based screening and insights that are more precise than generic ATS tools.
- High configurability that accommodates diverse recruitment needs in a rapidly evolving industry. | + +--- + +This Lean Canvas highlights a solution that differentiates itself by its technical focus, advanced collaboration, and real-time insights tailored to the needs of IT hiring. + +--- + +I'll help you create detailed use cases and sequence diagrams for the top 3 features mentioned in the document: Industry-Specific AI Candidate Screening, Configurable Recruitment Workflows, and Real-Time Collaboration Tools. + + + +### 1. Industry-Specific AI Candidate Screening + +**Use Case Description:** +A recruiter receives a new application for a Senior Full Stack Developer position. The AI system automatically screens the candidate's resume against the job requirements, analyzing technical skills, certifications, and experience. + +```mermaid +sequenceDiagram + actor Recruiter + participant ATS + participant AI Engine + participant DB + participant NotificationService + + Recruiter->>ATS: Upload candidate resume + ATS->>AI Engine: Process resume + AI Engine->>AI Engine: Extract skills & experience + AI Engine->>DB: Fetch job requirements + DB-->>AI Engine: Return requirements + AI Engine->>AI Engine: Match skills against requirements + AI Engine->>AI Engine: Calculate match score + AI Engine-->>ATS: Return screening results + ATS->>DB: Store screening results + ATS->>NotificationService: Send screening completion alert + NotificationService-->>Recruiter: Notify screening complete + ATS-->>Recruiter: Display candidate match score & analysis + +``` + +### 2. Configurable Recruitment Workflows + +**Use Case Description:** +A hiring manager needs to create a custom workflow for hiring senior developers that includes additional technical assessment steps and multiple interview rounds. + +```mermaid +sequenceDiagram + actor Hiring Manager + participant ATS + participant WorkflowEngine + participant DB + participant Templates + + Hiring Manager->>ATS: Request workflow configuration + ATS->>Templates: Fetch workflow templates + Templates-->>ATS: Return available templates + ATS-->>Hiring Manager: Display template options + Hiring Manager->>ATS: Select base template + Hiring Manager->>ATS: Customize workflow steps + ATS->>WorkflowEngine: Validate workflow configuration + WorkflowEngine-->>ATS: Confirm validation + ATS->>DB: Save custom workflow + DB-->>ATS: Confirm save + ATS-->>Hiring Manager: Display workflow preview + Hiring Manager->>ATS: Approve workflow + ATS->>DB: Activate new workflow + DB-->>ATS: Confirm activation + ATS-->>Hiring Manager: Confirm workflow active + +``` + +### 3. Real-Time Collaboration Tools + +**Use Case Description:** +Multiple team members are reviewing a candidate's profile and need to discuss their evaluation and make a collective decision about moving forward. + +```mermaid +sequenceDiagram + actor Recruiter + actor Hiring Manager + actor Tech Lead + participant ATS + participant CollaborationService + participant NotificationService + participant DB + + Recruiter->>ATS: Open candidate profile + ATS->>CollaborationService: Initialize collaboration session + CollaborationService-->>ATS: Session created + Recruiter->>ATS: Add comment on technical experience + ATS->>CollaborationService: Broadcast comment + CollaborationService->>NotificationService: Notify participants + NotificationService-->>Hiring Manager: Send notification + NotificationService-->>Tech Lead: Send notification + Tech Lead->>ATS: Join collaboration session + Tech Lead->>ATS: Add technical assessment + ATS->>CollaborationService: Broadcast assessment + CollaborationService->>DB: Store feedback + Hiring Manager->>ATS: Review comments & assessment + Hiring Manager->>ATS: Make decision + ATS->>CollaborationService: Broadcast decision + CollaborationService->>DB: Update candidate status + CollaborationService->>NotificationService: Notify team + NotificationService-->>Recruiter: Send decision notification + NotificationService-->>Tech Lead: Send decision notification + +``` + +Each of these use cases demonstrates key functionality of the system: + +1. **AI Screening** shows how the system automatically processes and evaluates candidates, saving significant time for recruiters while ensuring consistent evaluation of technical skills. + +2. **Workflow Configuration** illustrates the system's flexibility in adapting to different hiring needs and organizational requirements, allowing hiring managers to create custom processes. + +3. **Real-Time Collaboration** demonstrates how multiple stakeholders can work together efficiently within the system, sharing feedback and making decisions quickly. + +--- + +I'll design a comprehensive data model that supports the AI Screening, Workflow Configuration, and Real-Time Collaboration use cases. I'll create it using a Mermaid entity-relationship diagram and then detail the entities with their attributes and relationships. + +```mermaid +erDiagram + Candidate ||--o{ Application : submits + Application ||--o{ ScreeningResult : has + Application ||--|| ApplicationStatus : has + Application ||--o{ Evaluation : receives + Job ||--o{ Application : receives + Job ||--|| WorkflowTemplate : uses + Job ||--o{ RequiredSkill : requires + + WorkflowTemplate ||--o{ WorkflowStage : contains + WorkflowStage ||--o{ StageAction : includes + + User ||--o{ Evaluation : creates + User ||--o{ Comment : writes + + Application ||--o{ Comment : has + Comment ||--o{ CommentReply : has + + Skill ||--o{ RequiredSkill : defines + Skill ||--o{ CandidateSkill : defines + Candidate ||--o{ CandidateSkill : has + + Organization ||--|{ User : employs + Organization ||--|{ Job : posts + +``` + + + +# Detailed Data Model Specification + +## Core Entities + +### Organization +- `organization_id` (PK) UUID +- `name` VARCHAR(255) +- `industry` VARCHAR(100) +- `created_at` TIMESTAMP +- `updated_at` TIMESTAMP + +### User +- `user_id` (PK) UUID +- `organization_id` (FK) UUID +- `email` VARCHAR(255) +- `first_name` VARCHAR(100) +- `last_name` VARCHAR(100) +- `role` ENUM ('RECRUITER', 'HIRING_MANAGER', 'TECH_LEAD', 'ADMIN') +- `created_at` TIMESTAMP +- `updated_at` TIMESTAMP + +### Candidate +- `candidate_id` (PK) UUID +- `email` VARCHAR(255) +- `first_name` VARCHAR(100) +- `last_name` VARCHAR(100) +- `phone` VARCHAR(50) +- `resume_url` VARCHAR(500) +- `linkedin_url` VARCHAR(255) +- `github_url` VARCHAR(255) +- `created_at` TIMESTAMP +- `updated_at` TIMESTAMP + +### Job +- `job_id` (PK) UUID +- `organization_id` (FK) UUID +- `workflow_template_id` (FK) UUID +- `title` VARCHAR(255) +- `description` TEXT +- `status` ENUM ('DRAFT', 'ACTIVE', 'PAUSED', 'CLOSED') +- `created_at` TIMESTAMP +- `updated_at` TIMESTAMP + +## Application Processing + +### Application +- `application_id` (PK) UUID +- `candidate_id` (FK) UUID +- `job_id` (FK) UUID +- `current_stage_id` (FK) UUID +- `status` ENUM ('NEW', 'SCREENING', 'IN_REVIEW', 'INTERVIEW', 'OFFER', 'REJECTED', 'WITHDRAWN') +- `applied_at` TIMESTAMP +- `created_at` TIMESTAMP +- `updated_at` TIMESTAMP + +### ScreeningResult +- `screening_id` (PK) UUID +- `application_id` (FK) UUID +- `overall_score` DECIMAL(5,2) +- `skill_matches` JSONB +- `experience_match` DECIMAL(5,2) +- `screening_notes` TEXT +- `created_at` TIMESTAMP +- `updated_at` TIMESTAMP + +### Evaluation +- `evaluation_id` (PK) UUID +- `application_id` (FK) UUID +- `user_id` (FK) UUID +- `stage_id` (FK) UUID +- `score` INTEGER +- `feedback` TEXT +- `decision` ENUM ('ADVANCE', 'REJECT', 'HOLD') +- `created_at` TIMESTAMP +- `updated_at` TIMESTAMP + +## Skills Management + +### Skill +- `skill_id` (PK) UUID +- `name` VARCHAR(100) +- `category` VARCHAR(100) +- `created_at` TIMESTAMP +- `updated_at` TIMESTAMP + +### RequiredSkill +- `required_skill_id` (PK) UUID +- `job_id` (FK) UUID +- `skill_id` (FK) UUID +- `importance_level` ENUM ('REQUIRED', 'PREFERRED', 'NICE_TO_HAVE') +- `years_required` INTEGER +- `created_at` TIMESTAMP +- `updated_at` TIMESTAMP + +### CandidateSkill +- `candidate_skill_id` (PK) UUID +- `candidate_id` (FK) UUID +- `skill_id` (FK) UUID +- `years_experience` INTEGER +- `proficiency_level` ENUM ('BEGINNER', 'INTERMEDIATE', 'ADVANCED', 'EXPERT') +- `created_at` TIMESTAMP +- `updated_at` TIMESTAMP + +## Workflow Management + +### WorkflowTemplate +- `workflow_template_id` (PK) UUID +- `organization_id` (FK) UUID +- `name` VARCHAR(255) +- `description` TEXT +- `is_active` BOOLEAN +- `created_at` TIMESTAMP +- `updated_at` TIMESTAMP + +### WorkflowStage +- `stage_id` (PK) UUID +- `workflow_template_id` (FK) UUID +- `name` VARCHAR(255) +- `order_index` INTEGER +- `is_required` BOOLEAN +- `created_at` TIMESTAMP +- `updated_at` TIMESTAMP + +### StageAction +- `action_id` (PK) UUID +- `stage_id` (FK) UUID +- `action_type` ENUM ('SCREENING', 'REVIEW', 'INTERVIEW', 'ASSESSMENT', 'DECISION') +- `order_index` INTEGER +- `is_required` BOOLEAN +- `created_at` TIMESTAMP +- `updated_at` TIMESTAMP + +## Collaboration + +### Comment +- `comment_id` (PK) UUID +- `application_id` (FK) UUID +- `user_id` (FK) UUID +- `content` TEXT +- `visibility` ENUM ('PUBLIC', 'INTERNAL', 'PRIVATE') +- `created_at` TIMESTAMP +- `updated_at` TIMESTAMP + +### CommentReply +- `reply_id` (PK) UUID +- `comment_id` (FK) UUID +- `user_id` (FK) UUID +- `content` TEXT +- `created_at` TIMESTAMP +- `updated_at` TIMESTAMP + +## Key Relationships + +1. **AI Screening Support:** + - Application -> ScreeningResult (1:N) + - Job -> RequiredSkill (1:N) + - Candidate -> CandidateSkill (1:N) + - Skill -> RequiredSkill (1:N) + - Skill -> CandidateSkill (1:N) + +2. **Workflow Configuration Support:** + - Organization -> WorkflowTemplate (1:N) + - WorkflowTemplate -> WorkflowStage (1:N) + - WorkflowStage -> StageAction (1:N) + - Job -> WorkflowTemplate (N:1) + - Application -> WorkflowStage (N:1) + +3. **Real-Time Collaboration Support:** + - User -> Comment (1:N) + - Application -> Comment (1:N) + - Comment -> CommentReply (1:N) + - User -> Evaluation (1:N) + - Application -> Evaluation (1:N) + + +This data model supports the three main use cases in the following ways: + +1. **AI Candidate Screening** +- Stores candidate skills and experience (CandidateSkill) +- Maintains job requirements (RequiredSkill) +- Records screening results and matches (ScreeningResult) +- Tracks the overall application status (Application) + +2. **Configurable Recruitment Workflows** +- Defines workflow templates (WorkflowTemplate) +- Configures stages and actions (WorkflowStage, StageAction) +- Links workflows to jobs (Job) +- Tracks application progress through stages (Application) + +3. **Real-Time Collaboration** +- Manages user roles and permissions (User) +- Supports comments and replies (Comment, CommentReply) +- Records evaluations and feedback (Evaluation) +- Tracks application status changes (Application) + +The model includes audit fields (created_at, updated_at) for all entities and uses UUIDs as primary keys to support distributed systems. It also implements soft deletion where necessary (is_active fields) and uses appropriate data types and constraints for each field. + +--- + +I'll create a high-level system design using AWS services, breaking down the main components and their interactions. I'll include both a system architecture diagram and a detailed explanation of each component. + +```mermaid +graph TB + subgraph "Client Layer" + Web["Web Application
(React)"] + Mobile["Mobile App
(React Native)"] + end + + subgraph "API Gateway Layer" + APIG["AWS API Gateway"] + end + + subgraph "Load Balancer" + ALB["Application Load Balancer"] + end + + subgraph "Application Layer" + ECS["ECS Fargate Cluster"] + subgraph "Microservices" + AUTH["Auth Service
(ECS)"] + CAND["Candidate Service
(ECS)"] + WORK["Workflow Service
(ECS)"] + SCREEN["Screening Service
(ECS)"] + COLLAB["Collaboration Service
(ECS)"] + end + end + + subgraph "Caching Layer" + REDIS["ElastiCache
Redis Cluster"] + end + + subgraph "Database Layer" + RDS["Amazon RDS
PostgreSQL"] + ELASTIC["Amazon OpenSearch
Service"] + end + + subgraph "AI/ML Layer" + SAGEMAKER["Amazon SageMaker"] + end + + subgraph "Storage Layer" + S3["Amazon S3"] + end + + subgraph "Message Queue" + SQS["Amazon SQS"] + end + + subgraph "Notification Service" + SNS["Amazon SNS"] + end + + Web --> APIG + Mobile --> APIG + APIG --> ALB + ALB --> ECS + ECS --> AUTH & CAND & WORK & SCREEN & COLLAB + + AUTH & CAND & WORK & SCREEN & COLLAB --> REDIS + AUTH & CAND & WORK & SCREEN & COLLAB --> RDS + SCREEN --> ELASTIC + SCREEN --> SAGEMAKER + + CAND --> S3 + AUTH & CAND & WORK & SCREEN & COLLAB --> SQS + SQS --> SNS + +``` + + + +# High-Level System Design Specification + +## 1. Client Layer +- **Web Application** + - React-based SPA hosted on Amazon S3 + CloudFront + - Implements real-time updates using WebSocket connections + - Responsive design for desktop and tablet users + +- **Mobile Application** + - React Native app for iOS and Android + - Push notification support using AWS SNS + - Offline capabilities for basic features + +## 2. API Gateway Layer +- **AWS API Gateway** + - RESTful API endpoints for all services + - WebSocket API for real-time features + - API key management and throttling + - Request validation and transformation + - OAuth2/JWT token validation + +## 3. Application Layer (Microservices) + +### Authentication Service +- **Technology**: Node.js on ECS Fargate +- **Responsibilities**: + - User authentication and authorization + - JWT token management + - Role-based access control + - Integration with corporate SSO + +### Candidate Service +- **Technology**: Java/Spring Boot on ECS Fargate +- **Responsibilities**: + - Candidate profile management + - Application processing + - Resume parsing and storage + - Skills management + +### Workflow Service +- **Technology**: Python/FastAPI on ECS Fargate +- **Responsibilities**: + - Workflow template management + - Stage and action configuration + - Application status tracking + - Workflow validation + +### Screening Service +- **Technology**: Python/Flask on ECS Fargate +- **Responsibilities**: + - AI-based resume screening + - Skills matching + - Experience evaluation + - Integration with SageMaker models + +### Collaboration Service +- **Technology**: Node.js on ECS Fargate +- **Responsibilities**: + - Real-time commenting system + - Evaluation management + - Notification dispatch + - Activity tracking + +## 4. Data Layer + +### Primary Database +- **Amazon RDS (PostgreSQL)** + - Multi-AZ deployment for high availability + - Read replicas for read-heavy operations + - Automated backups and point-in-time recovery + +### Search Engine +- **Amazon OpenSearch Service** + - Candidate and job search + - Full-text resume search + - Analytics and reporting + +### Caching Layer +- **Amazon ElastiCache (Redis)** + - Session management + - API response caching + - Real-time collaboration data + - Temporary data storage + +## 5. AI/ML Layer +- **Amazon SageMaker** + - Custom models for candidate screening + - Skills matching algorithms + - Experience evaluation models + - Continuous model training and improvement + +## 6. Storage Layer +- **Amazon S3** + - Resume storage + - Document attachments + - Application assets + - System backups + +## 7. Message Queue & Notification +- **Amazon SQS** + - Asynchronous task processing + - Service decoupling + - Load leveling + +- **Amazon SNS** + - Push notifications + - Email notifications + - SMS notifications + - WebSocket event broadcasting + +## 8. DevOps & Monitoring + +### Infrastructure as Code +- **AWS CDK/CloudFormation** + - Infrastructure provisioning + - Environment consistency + - Version control for infrastructure + +### Monitoring & Logging +- **AWS CloudWatch** + - Application and infrastructure monitoring + - Log aggregation + - Custom metrics and dashboards + - Alerts and notifications + +### CI/CD Pipeline +- **AWS CodePipeline** + - Automated testing + - Deployment automation + - Blue-green deployments + - Rollback capabilities + +## Security Features + +### Network Security +- VPC with private and public subnets +- Network ACLs and Security Groups +- AWS WAF for web application firewall +- AWS Shield for DDoS protection + +### Data Security +- Encryption at rest using AWS KMS +- SSL/TLS encryption in transit +- Secrets management using AWS Secrets Manager +- Regular security audits and compliance checks + +## Scalability & Performance + +### Auto Scaling +- ECS service auto-scaling +- RDS read replica auto-scaling +- ElastiCache cluster auto-scaling + +### Performance Optimization +- CloudFront CDN for static content +- ElastiCache for frequent data access +- Read replicas for database read operations +- SQS for handling peak loads + + +This high-level system design provides: + +1. **Scalability**: The microservices architecture on ECS Fargate allows independent scaling of components based on demand. + +2. **High Availability**: Multi-AZ deployments, load balancing, and auto-scaling ensure system reliability. + +3. **Real-time Capabilities**: WebSocket connections through API Gateway and ElastiCache support real-time collaboration features. + +4. **AI Integration**: SageMaker integration enables sophisticated candidate screening and matching. + +5. **Security**: Comprehensive security measures at network, application, and data levels. + +Key features for our three main use cases: + +1. **AI Candidate Screening** +- Screening Service processes resumes using SageMaker models +- OpenSearch enables efficient candidate searching +- S3 stores resume documents +- SQS/SNS handle async processing and notifications + +2. **Configurable Workflow** +- Workflow Service manages templates and configurations +- RDS stores workflow definitions +- Redis caches active workflows +- SQS ensures reliable state transitions + +3. **Real-time Collaboration** +- Collaboration Service manages real-time interactions +- WebSocket API enables instant updates +- Redis handles real-time data +- SNS delivers notifications + +--- + +I'll create a detailed C4 model for the AI Candidate Screening component, showing the Context, Container, Component, and Code levels where appropriate. + +```mermaid +C4Context + title Context diagram for AI Candidate Screening System + + Person(recruiter, "Recruiter", "HR professional who reviews and screens candidates") + Person(candidate, "Candidate", "Job applicant submitting their application") + + System(ats, "AI Screening System", "Automatically screens and ranks candidates based on job requirements") + + System_Ext(emailSystem, "Email System", "Sends notifications to recruiters and candidates") + System_Ext(linkedIn, "LinkedIn API", "Retrieves additional candidate information") + + Rel(candidate, ats, "Submits application and resume") + Rel(recruiter, ats, "Reviews screening results and manages candidates") + Rel(ats, emailSystem, "Sends notifications") + Rel(ats, linkedIn, "Validates and enriches candidate data") + +``` + +```mermaid +C4Container + title Container diagram for AI Candidate Screening System + + Person(recruiter, "Recruiter", "HR professional who reviews and screens candidates") + Person(candidate, "Candidate", "Job applicant submitting their application") + + Container(web_app, "Web Application", "React", "Provides UI for recruiters and candidates") + Container(api_gateway, "API Gateway", "AWS API Gateway", "Handles API requests and routing") + + Container(screening_service, "Screening Service", "Python/Flask", "Processes and analyzes candidate applications") + Container(ml_engine, "ML Engine", "Python/SageMaker", "Performs AI-based candidate evaluation") + Container(doc_processor, "Document Processor", "Python/AWS Textract", "Extracts text and structure from resumes") + + ContainerDb(postgres_db, "Application Database", "PostgreSQL", "Stores candidate and job data") + ContainerDb(redis_cache, "Cache", "Redis", "Stores screening results and session data") + ContainerDb(object_store, "Document Store", "AWS S3", "Stores resume files") + + Rel(recruiter, web_app, "Uses", "HTTPS") + Rel(candidate, web_app, "Submits application", "HTTPS") + + Rel(web_app, api_gateway, "Makes API calls to", "HTTPS") + Rel(api_gateway, screening_service, "Routes requests to", "HTTPS") + + Rel(screening_service, doc_processor, "Sends documents for processing", "gRPC") + Rel(screening_service, ml_engine, "Requests candidate evaluation", "gRPC") + + Rel(screening_service, postgres_db, "Reads/writes data", "SQL") + Rel(screening_service, redis_cache, "Caches results", "Redis Protocol") + Rel(doc_processor, object_store, "Stores/retrieves documents", "AWS SDK") + +``` + +```mermaid +C4Component + title Component diagram for Screening Service + + Container_Boundary(screening_service, "Screening Service") { + Component(api_controller, "API Controller", "Flask Blueprint", "Handles HTTP requests and responses") + Component(auth_middleware, "Auth Middleware", "JWT Validator", "Validates user authentication and permissions") + + Component(screening_manager, "Screening Manager", "Service Layer", "Orchestrates screening process") + Component(resume_analyzer, "Resume Analyzer", "Domain Service", "Analyzes resume content and structure") + Component(skill_matcher, "Skill Matcher", "Domain Service", "Matches candidate skills with job requirements") + Component(exp_evaluator, "Experience Evaluator", "Domain Service", "Evaluates candidate experience levels") + + Component(ml_client, "ML Client", "gRPC Client", "Communicates with ML Engine") + Component(doc_client, "Document Client", "gRPC Client", "Communicates with Document Processor") + + Component(data_access, "Data Access Layer", "SQLAlchemy", "Handles database operations") + Component(cache_manager, "Cache Manager", "Redis Client", "Manages cache operations") + + Rel(api_controller, auth_middleware, "Passes requests through") + Rel(api_controller, screening_manager, "Calls") + + Rel(screening_manager, resume_analyzer, "Uses") + Rel(screening_manager, skill_matcher, "Uses") + Rel(screening_manager, exp_evaluator, "Uses") + + Rel(resume_analyzer, ml_client, "Uses") + Rel(resume_analyzer, doc_client, "Uses") + + Rel(screening_manager, data_access, "Uses") + Rel(screening_manager, cache_manager, "Uses") + } + +``` + +```python +# Domain Models + +class Candidate: + def __init__(self, id: UUID, name: str, email: str): + self.id = id + self.name = name + self.email = email + self.skills: List[Skill] = [] + self.experience: List[Experience] = [] + self.education: List[Education] = [] + +class ScreeningResult: + def __init__(self, candidate_id: UUID, job_id: UUID): + self.id = UUID() + self.candidate_id = candidate_id + self.job_id = job_id + self.overall_score: float = 0.0 + self.skill_matches: Dict[str, float] = {} + self.experience_score: float = 0.0 + self.education_score: float = 0.0 + self.created_at: datetime = datetime.now() + +# Service Layer + +class ScreeningManager: + def __init__( + self, + resume_analyzer: ResumeAnalyzer, + skill_matcher: SkillMatcher, + exp_evaluator: ExperienceEvaluator, + repository: ScreeningRepository, + cache: CacheManager + ): + self.resume_analyzer = resume_analyzer + self.skill_matcher = skill_matcher + self.exp_evaluator = exp_evaluator + self.repository = repository + self.cache = cache + + async def screen_candidate( + self, + candidate_id: UUID, + job_id: UUID, + resume_url: str + ) -> ScreeningResult: + # Get candidate and job details + candidate = await self.repository.get_candidate(candidate_id) + job = await self.repository.get_job(job_id) + + # Check cache first + cached_result = await self.cache.get_screening_result(candidate_id, job_id) + if cached_result: + return cached_result + + # Analyze resume + resume_data = await self.resume_analyzer.analyze(resume_url) + + # Match skills + skill_scores = await self.skill_matcher.match_skills( + resume_data.skills, + job.required_skills + ) + + # Evaluate experience + exp_score = await self.exp_evaluator.evaluate( + resume_data.experience, + job.requirements + ) + + # Create screening result + result = ScreeningResult(candidate_id, job_id) + result.skill_matches = skill_scores + result.experience_score = exp_score + result.overall_score = self._calculate_overall_score( + skill_scores, + exp_score + ) + + # Save and cache result + await self.repository.save_screening_result(result) + await self.cache.set_screening_result(result) + + return result + + def _calculate_overall_score( + self, + skill_scores: Dict[str, float], + exp_score: float + ) -> float: + skill_avg = sum(skill_scores.values()) / len(skill_scores) + return (skill_avg * 0.7) + (exp_score * 0.3) + +# Infrastructure Layer + +class ScreeningRepository: + def __init__(self, db_session: Session): + self.db = db_session + + async def get_candidate(self, candidate_id: UUID) -> Candidate: + return await self.db.query(Candidate)\ + .filter(Candidate.id == candidate_id)\ + .first() + + async def save_screening_result(self, result: ScreeningResult) -> None: + self.db.add(result) + await self.db.commit() + +class CacheManager: + def __init__(self, redis_client: Redis): + self.redis = redis_client + + async def get_screening_result( + self, + candidate_id: UUID, + job_id: UUID + ) -> Optional[ScreeningResult]: + key = f"screening:{candidate_id}:{job_id}" + data = await self.redis.get(key) + return ScreeningResult.from_json(data) if data else None + + async def set_screening_result(self, result: ScreeningResult) -> None: + key = f"screening:{result.candidate_id}:{result.job_id}" + await self.redis.setex( + key, + timedelta(hours=24), + result.to_json() + ) + +``` + +This C4 model breakdown shows the AI Candidate Screening system at different levels of abstraction: + +1. **Context Level**: +- Shows the system's interactions with external actors (recruiters and candidates) +- Identifies external system dependencies (email and LinkedIn) +- Defines the system's main responsibilities + +2. **Container Level**: +- Breaks down the system into its major technical components +- Shows the primary technologies used +- Defines the main data stores and their purposes +- Illustrates the flow of data between containers + +3. **Component Level**: +- Details the internal structure of the Screening Service +- Shows the separation of concerns between components +- Illustrates the communication patterns between components +- Defines clear boundaries and responsibilities + +4. **Code Level**: +- Shows the key classes and their relationships +- Implements Domain-Driven Design patterns +- Demonstrates clean architecture principles +- Includes error handling and caching strategies + +Key features highlighted in the model: + +1. **Separation of Concerns**: +- Clear boundaries between presentation, business logic, and data access +- Independent services for different aspects of screening +- Modular components that can be developed and scaled independently + +2. **Scalability Considerations**: +- Caching layer for performance optimization +- Asynchronous processing for long-running tasks +- Microservices architecture for independent scaling + +3. **Security**: +- Authentication middleware +- Secure API Gateway +- Encrypted data storage + +4. **Performance**: +- Redis caching for screening results +- Efficient database access patterns +- Optimized document processing pipeline + +Would you like me to elaborate on any particular aspect of the C4 model or provide more detailed information about specific components? + diff --git a/LTI-ADFR/Requesting_questions/prompts.md b/LTI-ADFR/Requesting_questions/prompts.md new file mode 100644 index 0000000..bec4eca --- /dev/null +++ b/LTI-ADFR/Requesting_questions/prompts.md @@ -0,0 +1,41 @@ +# First prompt + +You are an expert Business Analyst. I want you to help me in the definition of the best ATS (Applicant Tracking System). This ATS must add improvements such as: + - increase the HR departments productivity. + - Improve the real time collaboration between recruiters and managers (that are the owners of the job positions). + - Automations in the process. + - AI assistance in specific tasks. + +I would like you to focus in these 3 goals: +1. A description of the Applican Tacking System and the main competitor tools or existing ATSs. +2. Added value of the ATS. +3. Competitive advantages of our ATS proposal. + +Please ask me any questions you have before giving your answers + +# Second prompt + +1. Our target audience is the industry of IT jobs, for any enterprise needing this type of IT professional, from big IT consultant firms to banks, insurance companies, retail companies, ... +2. The workflow must be configurable inside the tool. +3. The 3 options you provide are fine. +4. No, I only know TeamTailor, but I would like to know all the relevant competitors and where can we differentiate. +5. Yes, managers must have access to the whole recruiting pipeline, and have the ability to collaborate direclty into the tool. +6. The metrics you mention are ok. + +# Third prompt + +Perfect, now I would like to know: +- the main features of our new ATS in order of relevance +- A Lean Canvas diagram to understant better the business model + +# Fourth prompt (Change to Claude Sonnet 3.5) +Given the LTI system that is described in the attached document, I want you, as an expert Product Engineer, to describe the 3 use cases related to the 3 first features of the system. Please add a UML sequence diagram for each of them. + +# Fifth prompt +Ok, Now I would like you as a Software Engineer to create the Data model ans its entities, attributes and relations that can support the previous 3 use cases + +# Sixth prompt +Perfect, I need now to work in a high level system design, describing the main components. We will use Amazon Web Services to deploy all this components. + +# Seventh prompt +Now I would like to to work in the C4 model diagram of the firt component: AI Candidate Screening, trying to detail it as much as you can \ No newline at end of file diff --git a/LTI-ADFR/prompts.md b/LTI-ADFR/prompts.md new file mode 100644 index 0000000..53db145 --- /dev/null +++ b/LTI-ADFR/prompts.md @@ -0,0 +1,12 @@ +# First prompt + +You are an expert Business Analyst. I want you to help me in the definition of the best ATS (Applicant Tracking System). This ATS must add improvements such as: +- increase the HR departments productivity. +- Improve the real time collaboration between recruiters and managers (that are the owners of the job positions). +- Automations in the process. +- AI assistance in specific tasks. + +I would like you to focus in these 3 goals: +1. A description of the Applican Tacking System and the main competitor tools or existing ATSs. +2. Added value of the ATS. +3. Competitive advantages of our ATS proposal. \ No newline at end of file