From fa2ed0751056645b8dbefcccba8aab847dc10646 Mon Sep 17 00:00:00 2001 From: pikelito Date: Tue, 29 Oct 2024 23:24:17 -0500 Subject: [PATCH 1/2] LTI_MAMB --- LTI_MAMB/ATS_MAMB.md | 0 LTI_MAMB/LTI_MAMB.md | 0 LTI_MAMB/casos de uso fases.png | Bin 0 -> 172973 bytes LTI_MAMB/database/fase1.png | Bin 0 -> 251187 bytes LTI_MAMB/database/fase2.png | Bin 0 -> 291936 bytes LTI_MAMB/database/fase3.png | Bin 0 -> 332981 bytes LTI_MAMB/database/fase4.png | Bin 0 -> 465515 bytes LTI_MAMB/database/mvp.png | Bin 0 -> 529393 bytes LTI_MAMB/diagrama c4.png | Bin 0 -> 220352 bytes LTI_MAMB/diagramac4.md | 61 ++ .../dise\303\261o sistema alto nivel.png" | Bin 0 -> 81571 bytes LTI_MAMB/prompts.md | 948 ++++++++++++++++++ 12 files changed, 1009 insertions(+) create mode 100644 LTI_MAMB/ATS_MAMB.md create mode 100644 LTI_MAMB/LTI_MAMB.md create mode 100644 LTI_MAMB/casos de uso fases.png create mode 100644 LTI_MAMB/database/fase1.png create mode 100644 LTI_MAMB/database/fase2.png create mode 100644 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selección"| ATS +Candidate -->|"Aplica a vacantes"| ATS +HRManager -->|"Supervisa y configura el sistema"| ATS +ATS -->|"Publica ofertas"| JobBoards +ATS -->|"Distribuye contenido en redes"| SocialMedia + +%% Nivel 2: Diagrama de Contenedores +subgraph SistemaATS ["Sistema ATS [Software System]"] + WebApp["Aplicación Web\n[Contenedor: React/TypeScript]\nInterfaz principal"] + MobileApp["Aplicación Móvil\n[Contenedor: React Native]\nInterfaz móvil"] + + APIGateway["API Gateway\n[Contenedor: Node.js]\nManejo de solicitudes y autenticación"] + + CoreServices["Servicios Core\n[Contenedor: Node.js/Express]\nLógica de negocio principal"] + + AIEngine["Motor de IA\n[Contenedor: Python]\nAnálisis avanzado y procesamiento"] + + Database["Base de Datos\n[Contenedor: PostgreSQL]\nAlmacenamiento de datos"] + + ChatbotService["Servicio de Chatbot\n[Contenedor: Python]\nAtención automatizada"] +end + +WebApp -->|"Solicitudes API"| APIGateway +MobileApp -->|"Solicitudes API"| APIGateway +APIGateway -->|"Enrutamiento"| CoreServices +CoreServices -->|"Consultas y actualizaciones"| Database +CoreServices -->|"Solicita análisis de IA"| AIEngine +CoreServices -->|"Envía mensajes"| ChatbotService + +%% Nivel 3: Diagrama de Componentes (MVP) +subgraph CoreServices ["Servicios Core [Contenedor]"] + JobManager["Gestor de Ofertas\n[Componente]\nManejo de publicaciones de empleo"] + CandidateManager["Gestor de Candidatos\n[Componente]\nAdministración de aplicantes"] + RecruitmentFlow["Flujo de Reclutamiento\n[Componente]\nControl del proceso de selección"] + Analytics["Análisis y Reportes\n[Componente]\nGeneración de métricas y KPIs"] + + AIIntegration["Integración con IA\n[Componente]\nConexión a servicios de IA"] + CommunicationManager["Gestor de Comunicaciones\n[Componente]\nNotificaciones y mensajería"] +end + +JobManager -->|"Acceso a datos de ofertas"| Database +CandidateManager -->|"Acceso a perfiles y estados"| Database +RecruitmentFlow -->|"Control de estados del proceso"| Database 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Su principal función es **facilitar la administración de candidatos** que aplican a una posición en una empresa. Este sistema **automatiza y centraliza** todas las actividades relacionadas con la selección de talento, desde la publicación de ofertas de empleo hasta la organización y análisis de los perfiles de los postulantes. + +### Principales funciones de un sistema ATS: + +1. **Recopilación y Organización de Candidatos**: Los ATS permiten recoger aplicaciones de múltiples fuentes (portales de empleo, redes sociales, sitios web corporativos) y organizarlas en una sola base de datos. Esto facilita el acceso a los perfiles de los candidatos. + +2. **Filtrado de Candidatos**: Los ATS pueden realizar filtrados automáticos, descartando aquellos candidatos que no cumplan con ciertos requisitos básicos del puesto (como experiencia o habilidades específicas). Esto se hace mediante palabras clave y criterios predefinidos, ahorrando tiempo a los reclutadores. + +3. **Automatización de Comunicaciones**: Permiten enviar correos automáticos a los candidatos en diferentes etapas del proceso, lo cual agiliza la comunicación y mantiene informados a los aplicantes sobre el estado de su aplicación. + +4. **Programación de Entrevistas**: Muchos sistemas ATS tienen integraciones de calendario, lo cual facilita coordinar entrevistas con los candidatos y los entrevistadores, reduciendo el tiempo de gestión manual. + +5. **Reportes y Análisis**: Los ATS pueden generar reportes sobre el rendimiento del proceso de selección, el tiempo que tarda cada etapa, el número de candidatos descartados, y otras métricas que ayudan a identificar mejoras en el proceso. + +### Beneficios de un sistema ATS + +- **Eficiencia**: Reduce significativamente el tiempo necesario para gestionar grandes volúmenes de aplicaciones. +- **Mejora en la Calidad de Contratación**: Al centralizar y organizar la información, permite una mejor evaluación de los candidatos. +- **Cumplimiento Normativo**: Facilita el cumplimiento de regulaciones relacionadas con el almacenamiento de datos y la equidad en los procesos de selección. +- **Experiencia del Candidato**: La automatización de comunicaciones y seguimiento puede mejorar la percepción de los candidatos sobre la empresa. + +En resumen, un sistema ATS es una herramienta fundamental para optimizar el proceso de reclutamiento y selección, ahorrando tiempo, facilitando el análisis de perfiles, y mejorando tanto la eficiencia interna del equipo de Recursos Humanos como la experiencia de los candidatos. + +# PROMPT + +mi objetivo es crear un sistema ATS desde cero que pueda incorporar nuevas funcionalidades y mejorar las funcionalidades que ofrecen todos los sistemas ATS apoyandome en la IA, con esto busco sobresalir sobre la competencia y obtener una buena cantidad de clientes. +Como un business analist experto que eres me darás una lluvia de ideas para ir consolidando la idea de negocio para el sistema, deberás explicarme de forma breve el porqué de cada idea. + +# CHATGPT +¡Excelente! Crear un ATS desde cero con capacidades avanzadas apoyadas en IA es una gran idea y puede ser un diferenciador fuerte en el mercado. Aquí tienes una serie de ideas para consolidar este sistema ATS innovador: + +### 1. **Análisis predictivo para la selección de candidatos** + - **Descripción**: Utiliza IA para predecir el ajuste de un candidato en la empresa y en el rol específico basándose en datos históricos de otros empleados y el perfil cultural de la empresa. + - **Beneficio**: Permite tomar decisiones de contratación más informadas y reduce la rotación, atrayendo clientes interesados en mejorar su retención de talento. + +### 2. **Procesamiento de lenguaje natural (NLP) para entender CVs y cartas de presentación** + - **Descripción**: Implementa NLP para leer y analizar CVs y cartas de presentación de manera inteligente, extrayendo información clave de diferentes formatos y lenguajes. + - **Beneficio**: Elimina la dependencia de formatos específicos de CV y permite un análisis más profundo de las competencias y experiencias. + +### 3. **Matching avanzado basado en habilidades y experiencia** + - **Descripción**: Utiliza algoritmos de matching que relacionen las habilidades y experiencias de los candidatos con los requisitos del puesto en lugar de solo palabras clave. + - **Beneficio**: Aumenta la precisión en la selección y reduce falsos positivos, evitando que candidatos no calificados avancen en el proceso. + +### 4. **Chatbot de IA para la interacción con candidatos** + - **Descripción**: Desarrolla un chatbot que responda preguntas de los candidatos y los guíe en el proceso de aplicación de manera personalizada y en tiempo real. + - **Beneficio**: Mejora la experiencia del candidato y reduce la carga de comunicación en el equipo de RRHH. + +### 5. **Sistema de recomendaciones de candidatos internos** + - **Descripción**: Permite que los empleados actuales puedan ser considerados automáticamente para vacantes abiertas, basándose en sus habilidades, desempeño y aspiraciones. + - **Beneficio**: Fomenta la movilidad interna y retiene talento clave, mejorando la percepción del ATS para empresas centradas en el desarrollo de su personal. + +### 6. **Evaluación automatizada de habilidades técnicas y pruebas psicológicas** + - **Descripción**: Integra pruebas técnicas y psicológicas automáticas en el ATS, con análisis automatizados que detecten habilidades clave y el ajuste cultural del candidato. + - **Beneficio**: Reduce la necesidad de evaluaciones externas y proporciona información objetiva para la toma de decisiones, atrayendo clientes interesados en evaluaciones rápidas y precisas. + +### 7. **Seguimiento del mercado laboral y análisis de tendencias** + - **Descripción**: Añade un módulo que recopile datos del mercado laboral para informar a los reclutadores sobre tendencias salariales, demanda de habilidades y comparación de beneficios. + - **Beneficio**: Ofrece valor añadido para empresas en busca de insights del mercado, ayudándoles a ajustar sus ofertas para ser más competitivas. + +### 8. **Dashboard personalizado y KPI de contratación** + - **Descripción**: Proporciona un dashboard interactivo con KPIs clave personalizables para cada usuario (ejemplo: tiempo para contratar, calidad de los candidatos, fuentes más efectivas). + - **Beneficio**: Permite a los gerentes de RRHH tener una visión clara del rendimiento de su proceso de selección y ajustar en función de los datos. + +### 9. **Capacidades multilingües y sensibilidad cultural** + - **Descripción**: Desarrolla un sistema que analice y evalúe candidatos de distintas regiones e idiomas, y tenga en cuenta diferencias culturales en la interpretación de los CVs. + - **Beneficio**: Atrae empresas globales o en expansión internacional al ofrecer una solución ATS que respete y entienda las diversidades culturales. + +### 10. **Funcionalidad de onboarding y seguimiento post-contratación** + - **Descripción**: Integra un módulo de onboarding que guíe a los nuevos empleados durante sus primeros días, y un sistema de seguimiento de satisfacción y ajuste. + - **Beneficio**: Brinda valor agregado a las empresas que buscan mejorar la experiencia de sus nuevos empleados, lo cual puede ser un factor clave de decisión en la elección del ATS. + +### 11. **Integración con plataformas de aprendizaje y desarrollo (L&D)** + - **Descripción**: Facilita la conexión con sistemas de capacitación para que los reclutadores puedan recomendar cursos o programas de desarrollo a los candidatos internos y externos. + - **Beneficio**: Ofrece una funcionalidad extra para empresas que valoran el crecimiento profesional, integrando el ATS en un ecosistema de desarrollo continuo. + +### 12. **Análisis de emociones y comportamiento durante entrevistas** + - **Descripción**: Implementa un sistema que analice el tono, el lenguaje corporal y las emociones de los candidatos durante entrevistas virtuales. + - **Beneficio**: Ofrece un nivel de análisis profundo que ayuda a detectar rasgos de personalidad y ajuste cultural que podrían no aparecer en el CV. + +### 13. **Sistema de feedback colaborativo** + - **Descripción**: Facilita que los diferentes miembros del equipo de reclutamiento y los gerentes puedan dejar feedback colaborativo en tiempo real sobre los candidatos. + - **Beneficio**: Mejora la comunicación interna y hace que la toma de decisiones sea más transparente y rápida. + +### 14. **Análisis de diversidad e inclusión en el proceso de selección** + - **Descripción**: Genera informes sobre el nivel de diversidad en cada etapa del proceso de selección y ofrece recomendaciones para mejorar la inclusión. + - **Beneficio**: Atrae a empresas con políticas de diversidad e inclusión que buscan soluciones para medir y mejorar su proceso de selección en estas áreas. + +### 15. **Mobile-first y adaptabilidad a redes sociales** + - **Descripción**: Crea un sistema ATS móvil y adaptable para integrarse fácilmente con redes sociales y aplicaciones de mensajería. + - **Beneficio**: Facilita el uso del ATS en cualquier dispositivo y atrae talento joven o que se encuentra en plataformas como LinkedIn o Instagram. + +Cada una de estas ideas tiene el potencial de darle a tu ATS una ventaja competitiva en el mercado, integrando funcionalidades avanzadas y diferenciadoras que van más allá de las capacidades tradicionales de los sistemas ATS actuales. + +# PROMPT +Me gustan todas las ideas que me acabas de dar. +## Objetivo: Crear un documento en formato markdown el cuál contenga la siguiente información separada por indices: +- la visión y la idea central del negocio +- Beneficios y diferencial con la competencia +- Evalúar la propuesta de valor y establecer la viabilidad del producto. + +Si tienes dudas debes preguntarme y resolverlas antes de dar una respuesta + +# CHATGPT +Gracias por los detalles. Antes de crear el documento en formato Markdown, quisiera aclarar algunos puntos para asegurar que el contenido refleje con precisión la visión del proyecto y se ajuste a tus expectativas: + +1. **Visión y idea central del negocio**: + - ¿Cómo defines la visión a largo plazo? Por ejemplo, ¿sería algo como "convertirse en el sistema ATS líder que redefine la experiencia de selección mediante IA"? + - ¿Quieres que la "idea central del negocio" incluya una descripción de los objetivos estratégicos a mediano y largo plazo (como expansión geográfica o integraciones con otras tecnologías)? + +2. **Beneficios y diferencial con la competencia**: + - ¿Hay competidores clave a los que quieres que compare el ATS en términos de funcionalidades o características específicas? + - ¿Deseas un enfoque general sobre los beneficios o prefieres que se desglosen en categorías (por ejemplo, eficiencia, experiencia del usuario, análisis avanzado)? + +3. **Evaluación de la propuesta de valor y viabilidad del producto**: + - ¿Tienes alguna métrica o indicador clave de éxito que quieras incluir en la evaluación de viabilidad, como retención de clientes, crecimiento proyectado o integración de IA? + - ¿Hay algún aspecto específico de la propuesta de valor (como la personalización o escalabilidad) en el que quieras un enfoque más detallado? + +Con estas respuestas podré construir un documento más ajustado a lo que estás visualizando. + +# PROMPT +1. Si convertirme en un sistema ATS líder mendiante la IA. Si incluir la descripción a mediano y largo plazo +2. usa supuestos de las mejores ATS del mercado colombiano, un enfoque general +3. retención de cliente, crecimiento proyectado e integración de la IA. hacer enfoque en la escalabilidad + +Si tienes dudas debes preguntarme y resolverlas antes de dar una respuesta + +# CHATGPT +GENERA MERMAID + +Si deseas ajustar alguna sección o añadir detalles específicos, ¡házmelo saber! + +# PROMPT + +**Quien eres**: un Product Manager Senior +**Obetivo**: Generar en un documento formato mardown con el MVP y las fases del sistema, debe ser pensado en que se pueda escalar la solución haciendo productivo el MVP y cada fase en tiempos preferiblemente cortos. + +# CHAGPT +GENERA MERMAID + +Este plan permitirá que el sistema ATS tenga entregables en tiempos cortos y sea funcional desde el MVP, maximizando el valor para los primeros usuarios y permitiendo una mejora continua en cada fase. + +# PROMPT +**Quien eres**: un arquitecto de software experimentado +**Objetivo**: Crear la arquitectura con la cuál se diseñara el sistema, teniendo un claro enfoque en la escalabilidad y mantenibilidad del sistema. +**Caracteristicas**: - Frontend con Nuxt3 +- Usar BFF quién será el encargado de gestionar las peticiones del frontend a los microservicios +- Usar microservicios +- Usar los mejores servicios de aws +- Base de datos para cada microservicio +Debes considerar las mejores caracteristicas y mejores prácticas para que sea un sistema escalable, cualquier duda que tengas debes resolverlas antes de dar una respuesta + +# CHATGPT +Gracias por el contexto detallado. Antes de pasar a crear la arquitectura, quisiera aclarar algunos puntos clave para garantizar que el diseño se alinee con tus necesidades: + +1. **Escalabilidad de Microservicios**: + - ¿Existen servicios o módulos específicos que deseas priorizar en cuanto a escalabilidad? Por ejemplo, funcionalidades como el procesamiento de aplicaciones, filtrado de candidatos con IA o los módulos de chatbots, que suelen requerir más capacidad. + +2. **Manejo de Datos en Microservicios**: + - ¿Tienes alguna preferencia respecto al modelo de base de datos por microservicio (relacional vs. no relacional)? Esto puede influir en la elección para servicios intensivos en datos o que dependan de consultas complejas. + +3. **Requerimientos de IA**: + - Para la integración de IA, ¿planeas implementar tus propios modelos o prefieres usar servicios de AWS como Amazon SageMaker? Esto influirá en cómo orquestar los recursos de IA y optimizar la escalabilidad. + +4. **Entorno Multiusuario y Seguridad**: + - ¿Quieres integrar un sistema de autenticación y autorización con alta granularidad (e.g., Amazon Cognito con permisos en varios niveles de usuarios)? Esto puede condicionar la arquitectura del BFF y las interacciones de microservicios. + +Con esta información, podré diseñar una arquitectura detallada y alineada con tus objetivos. + +# PROMPT +1. las que consideres priorizar con tu experiencia +2. relacional +3. sevicios de aws +4. usar amazon cognito + +# CHATGPT +Con esta información en mente, aquí tienes un diseño arquitectónico detallado para tu sistema ATS con un enfoque en escalabilidad y mantenibilidad. La arquitectura se compone de microservicios desacoplados y aprovecha las mejores prácticas de AWS para lograr una infraestructura flexible y segura. + +GENERA MERMAID + +# PROMPT +**Quien eres**: Eres un analista de software experto +**Contexto**: puedes acceder a todo el contexto del proyecto en el documento adjunto +**Objetivo**: crear un diagrama de flujo con los casos de uso del mvp y cada fase del proyecto organizándolo por actores principales y sus interacciones. +**Output**: DiagramFormato mermaid + +# CLOUD +Te ayudaré a crear diagramas de casos de uso separados para el MVP y cada fase del desarrollo, basándome en el roadmap del documento. + +```mermaid +graph TB + +%% MVP - Casos de Uso Básicos +subgraph MVP[MVP - Funcionalidades Base] + direction TB + + subgraph ActoresMVP[Actores MVP] + RecruiterMVP((Reclutador)) + CandidateMVP((Candidato)) + BasicAI((IA Básica)) + end + + subgraph FuncionesMVP[Funciones Básicas] + PublishJob[Publicar Ofertas] + ViewDashboard[Ver Dashboard] + FilterCandidates[Filtrar Candidatos] + BasicChat[Chatbot Básico] + BasicMatch[Matching Básico] + ApplyJob[Aplicar Trabajo] + + RecruiterMVP --> PublishJob + RecruiterMVP --> ViewDashboard + BasicAI --> FilterCandidates + BasicAI --> BasicMatch + CandidateMVP --> ApplyJob + CandidateMVP --> BasicChat + end +end + +%% Fase 1 - IA Avanzada +subgraph Phase1[Fase 1 - IA Avanzada] + direction TB + + subgraph ActoresF1[Actores Fase 1] + RecruiterF1((Reclutador)) + AdvancedAI((IA Avanzada)) + CandidateF1((Candidato)) + end + + subgraph FuncionesF1[Funciones Avanzadas] + AutoSkillTest[Evaluación Automática] + EmotionAnalysis[Análisis Emocional] + AdvancedMatch[Matching Avanzado] + TakeTests[Realizar Pruebas] + + RecruiterF1 --> AutoSkillTest + AdvancedAI --> EmotionAnalysis + AdvancedAI --> AdvancedMatch + CandidateF1 --> TakeTests + end +end + +%% Fase 2 - Experiencia y Escalabilidad +subgraph Phase2[Fase 2 - Experiencia y Escalabilidad] + direction TB + + subgraph ActoresF2[Actores Fase 2] + RecruiterF2((Reclutador)) + HRManager((Gerente RRHH)) + NewEmployee((Nuevo Empleado)) + end + + subgraph FuncionesF2[Funciones de Experiencia] + CustomDashboard[Dashboard Personalizado] + Onboarding[Gestión Onboarding] + SatisfactionTrack[Seguimiento Satisfacción] + ViewMetrics[Ver Métricas] + + RecruiterF2 --> CustomDashboard + HRManager --> ViewMetrics + RecruiterF2 --> Onboarding + NewEmployee --> SatisfactionTrack + end +end + +%% Fase 3 - Multilingüe y Diversidad +subgraph Phase3[Fase 3 - Diversidad e Inclusión] + direction TB + + subgraph ActoresF3[Actores Fase 3] + GlobalRecruiter((Reclutador Global)) + MultiAI((IA Multilingüe)) + IntCandidate((Candidato Internacional)) + end + + subgraph FuncionesF3[Funciones Globales] + MultilingualCV[Procesar CV Multilingüe] + DiversityAnalytics[Análisis de Diversidad] + InternalRecommend[Recomendación Interna] + + GlobalRecruiter --> DiversityAnalytics + MultiAI --> MultilingualCV + IntCandidate --> MultilingualCV + GlobalRecruiter --> InternalRecommend + end +end + +%% Fase 4 - Integraciones +subgraph Phase4[Fase 4 - Integraciones Avanzadas] + direction TB + + subgraph ActoresF4[Actores Fase 4] + SystemAdmin((Administrador)) + AIEngineer((Ingeniero IA)) + end + + subgraph FuncionesF4[Funciones de Integración] + LearningIntegration[Integrar Plataforma L&D] + OptimizeAI[Optimizar Algoritmos] + IntegrateVR[Integrar VR] + + SystemAdmin --> LearningIntegration + AIEngineer --> OptimizeAI + SystemAdmin --> IntegrateVR + end +end +``` +He creado un diagrama que muestra los casos de uso divididos por fases. Permíteme detallar cada fase: + +# Detalles de Casos de Uso por Fase + +## MVP - Funcionalidades Base +**Actores Principales:** +- Reclutador +- Candidato +- IA Básica + +**Casos de Uso:** +1. **Gestión de Ofertas** + - Publicar ofertas de trabajo + - Distribuir en plataformas + - Ver dashboard básico + +2. **Interacción Candidato** + - Aplicar a trabajos + - Interactuar con chatbot básico + - Recibir comunicaciones automáticas + +3. **Filtrado Básico** + - Filtrado automático de CVs + - Matching básico de perfiles + - Seguimiento de estados + +## Fase 1 - IA Avanzada +**Actores Principales:** +- Reclutador +- IA Avanzada +- Candidato + +**Casos de Uso:** +1. **Evaluación Avanzada** + - Realizar tests automáticos + - Analizar resultados con IA + - Generar reportes de competencias + +2. **Análisis Comportamental** + - Analizar emociones en entrevistas + - Evaluar comportamiento + - Generar insights + +## Fase 2 - Experiencia y Escalabilidad +**Actores Principales:** +- Reclutador +- Gerente RRHH +- Nuevo Empleado + +**Casos de Uso:** +1. **Gestión Personalizada** + - Personalizar dashboard + - Configurar KPIs + - Gestionar reportes + +2. **Onboarding** + - Gestionar proceso de onboarding + - Seguimiento de satisfacción + - Automatizar documentación + +## Fase 3 - Diversidad e Inclusión +**Actores Principales:** +- Reclutador Global +- IA Multilingüe +- Candidato Internacional + +**Casos de Uso:** +1. **Gestión Multilingüe** + - Procesar CVs en múltiples idiomas + - Adaptar evaluaciones culturalmente + - Gestionar comunicaciones multilingües + +2. **Análisis de Diversidad** + - Monitorear métricas de diversidad + - Generar recomendaciones + - Seguimiento de objetivos + +## Fase 4 - Integraciones Avanzadas +**Actores Principales:** +- Administrador de Sistema +- Ingeniero IA + +**Casos de Uso:** +1. **Gestión de Integraciones** + - Integrar plataformas de aprendizaje + - Configurar evaluaciones VR + - Optimizar algoritmos IA + +2. **Mejora Continua** + - Actualizar modelos de IA + - Implementar nuevas tecnologías + - Mantener integraciones + + +# PROMPT + +**Quien eres**: un experto en base de datos +**Contexto**: lo encuentras en el archivo que adjunte +**Objetivo**: tienes la responsabilidad de diseár la base de datos en formato mermaid +**Caracteristicas**: cubrir entidades, atributos (nombre y tipo) y relaciones, debes alinearte con la arquitectura planeada en el contexto y entregarla por las fases correspondientes +Antes de dar una respuesta deberás resolver todas tus dudas + +# CLOUD +Como experto en bases de datos, analizaré el documento para diseñar una base de datos que soporte el sistema ATS descrito. Antes de proceder, tengo algunas preguntas importantes para asegurar un diseño óptimo: + +1. ¿Debería incluir tablas para manejar la configuración multinivel mencionada en la Fase 2 (diferentes configuraciones para PYMES vs grandes corporaciones)? + +2. En relación al chatbot mencionado en el MVP, ¿necesitamos almacenar el historial de conversaciones para entrenamiento futuro del modelo de IA? + +3. Para el análisis de emociones en entrevistas virtuales (Fase 1), ¿se requiere almacenar los datos crudos del análisis facial/voz o solo los resultados procesados? + +4. En cuanto al sistema de evaluación automática de habilidades (Fase 1), ¿se necesita almacenar las preguntas/respuestas de los tests o solo los resultados finales? + +5. Para el módulo de diversidad e inclusión (Fase 3), ¿qué métricas específicas necesitan ser almacenadas para el seguimiento de objetivos? + +6. Respecto al onboarding (Fase 2), ¿se requiere almacenar documentos (contratos, guías, etc.) en la base de datos o solo referencias a documentos en un sistema externo? + +7. Para las integraciones con plataformas de aprendizaje (Fase 4), ¿necesitamos almacenar el progreso/resultados de los cursos o solo las referencias a cursos externos? + +Estas respuestas me ayudarán a diseñar un esquema de base de datos más preciso y alineado con los requerimientos del sistema. ¿Podrías proporcionar clarificación sobre estos puntos? + +# PROMPT +Antes de dar una respuesta deberás resolver todas tus dudas +1. si +2. si +3. resultados procesados +4. preguntas y respuestas +5. las básicas que consideres +6. referencia a un sistema externo +7. solo referencias a cursos externos + +# CLOUD +Gracias por las aclaraciones. Tengo algunas preguntas adicionales para asegurar un diseño completo y preciso: + +1. Respecto a la publicación de ofertas en múltiples plataformas (MVP): + - ¿Necesitamos guardar el estado de publicación en cada plataforma? + - ¿Se requiere almacenar estadísticas de efectividad por plataforma? + +2. Sobre el análisis predictivo de ajuste (MVP): + - ¿Los criterios de evaluación son configurables por empresa? + - ¿Se debe mantener un histórico de las predicciones realizadas? + +3. Para el sistema de matching avanzado (Fase 1): + - ¿Los pesos de los criterios de matching son configurables por empresa? + - ¿Se requiere almacenar el histórico de matches exitosos para mejorar el algoritmo? + +4. En cuanto al dashboard personalizable (Fase 2): + - ¿Cada usuario puede tener su propia configuración de dashboard? + - ¿Se necesita mantener un histórico de las métricas para análisis temporal? + +5. Sobre el procesamiento multilingüe (Fase 3): + - ¿Cada empresa puede configurar sus idiomas soportados? + - ¿Se necesita almacenar las traducciones de las ofertas de trabajo? + +Estas respuestas me ayudarán a definir mejor las estructuras de datos necesarias para cada fase. ¿Podrías aclarar estos puntos? + +# PROMT + +Antes de dar una respuesta deberás resolver todas tus dudas +1. no / no +2. si / no +3. si / si +4. no / si +5. si / si + +# CLOUD +Gracias por estas aclaraciones adicionales. Tengo algunas últimas preguntas para asegurar un diseño robusto: + +1. Respecto a los permisos y roles: + - ¿Se necesitan roles personalizados por empresa además de los básicos (reclutador, gerente RRHH, administrador)? + - ¿Los permisos deben ser configurables a nivel de función específica? + +2. Sobre el seguimiento de candidatos: + - ¿Se debe mantener un historial completo de todos los estados por los que pasa un candidato? + - ¿Cada empresa puede definir sus propias etapas del proceso de selección? + +3. Para la gestión de evaluaciones: + - ¿Una evaluación puede ser reutilizada para diferentes posiciones? + - ¿Las empresas pueden crear sus propias plantillas de evaluación? + +4. Respecto al sistema de recomendación de candidatos internos (Fase 3): + - ¿Se debe mantener un histórico de todas las recomendaciones realizadas? + - ¿Las habilidades y competencias deben tener niveles o solo ser binarias (tiene/no tiene)? + +5. En cuanto a la configuración multinivel: + - ¿Cada nivel de suscripción tiene acceso a diferentes funcionalidades? + - ¿Se debe mantener un histórico de cambios de nivel por empresa? + +¿Podrías aclarar estos últimos puntos para proceder con el diseño detallado? + +# PROMT + +Antes de dar una respuesta deberás resolver todas tus dudas +1. no / si +2. si / no +3. si / no +4. si / binarias +5. si / no + +# CLOUD + +Con todas las dudas resueltas, procederé a diseñar el modelo de datos por fases. Empezaré con el MVP y luego iré agregando las estructuras necesarias para cada fase subsecuente. + + + +```mermaid +erDiagram + Company { + int id PK + string name + string industry + datetime created_at + boolean active + } + + User { + int id PK + int company_id FK + string email + string name + string password_hash + datetime created_at + boolean active + } + + Permission { + int id PK + string name + string description + string resource + string action + } + + UserPermission { + int user_id FK + int permission_id FK + } + + JobPosting { + int id PK + int company_id FK + string title + text description + string requirements + datetime created_at + datetime expiry_date + string status + } + + Candidate { + int id PK + string email + string name + text resume_url + datetime created_at + boolean active + } + + Application { + int id PK + int job_posting_id FK + int candidate_id FK + datetime applied_at + string status + float matching_score + } + + ChatConversation { + int id PK + int candidate_id FK + int job_posting_id FK + datetime started_at + datetime ended_at + } + + ChatMessage { + int id PK + int conversation_id FK + string sender_type + text message + datetime sent_at + } + + EvaluationCriteria { + int id PK + int company_id FK + string name + string description + float weight + } + + Company ||--o{ User : has + Company ||--o{ JobPosting : creates + User ||--o{ UserPermission : has + Permission ||--o{ UserPermission : assigned_to + JobPosting ||--o{ Application : receives + Candidate ||--o{ Application : makes + Candidate ||--o{ ChatConversation : participates_in + ChatConversation ||--o{ ChatMessage : contains + Company ||--o{ EvaluationCriteria : defines + +``` + +```mermaid +erDiagram + %% Incluye todas las entidades del MVP y agrega: + + Skill { + int id PK + string name + string description + string category + } + + Test { + int id PK + string name + string description + string type + boolean active + } + + Question { + int id PK + int test_id FK + string question_text + string type + json options + } + + TestResult { + int id PK + int application_id FK + int test_id FK + datetime taken_at + json answers + float score + } + + InterviewEmotion { + int id PK + int application_id FK + datetime recorded_at + json emotion_data + float confidence_score + } + + MatchingHistory { + int id PK + int application_id FK + float score + json criteria_scores + datetime matched_at + } + + Application ||--o{ TestResult : has + Application ||--o{ InterviewEmotion : analyzed_in + Application ||--o{ MatchingHistory : evaluated_with + Test ||--o{ Question : contains + Application }o--|| JobPosting : belongs_to + +``` + +```mermaid +erDiagram + %% Incluye todas las entidades anteriores y agrega: + + SubscriptionTier { + int id PK + string name + string description + json features + } + + CompanySubscription { + int id PK + int company_id FK + int tier_id FK + datetime start_date + datetime end_date + } + + Metric { + int id PK + string name + string description + string calculation_method + } + + MetricHistory { + int id PK + int company_id FK + int metric_id FK + float value + datetime recorded_at + } + + OnboardingReference { + int id PK + int application_id FK + string document_type + string external_url + datetime created_at + } + + Company ||--o{ CompanySubscription : has + SubscriptionTier ||--o{ CompanySubscription : defines + Company ||--o{ MetricHistory : tracks + Metric ||--o{ MetricHistory : measured_in + Application ||--o{ OnboardingReference : includes + +``` + +```mermaid +erDiagram + %% Incluye todas las entidades anteriores y agrega: + + Language { + int id PK + string code + string name + } + + CompanyLanguage { + int id PK + int company_id FK + int language_id FK + boolean is_primary + } + + JobPostingTranslation { + int id PK + int job_posting_id FK + int language_id FK + string title + text description + string requirements + } + + DiversityMetric { + int id PK + string name + string description + } + + DiversityReport { + int id PK + int company_id FK + datetime generated_at + json metrics_data + } + + InternalRecommendation { + int id PK + int job_posting_id FK + int candidate_id FK + datetime recommended_at + string status + } + + Company ||--o{ CompanyLanguage : supports + Language ||--o{ CompanyLanguage : used_by + JobPosting ||--o{ JobPostingTranslation : translated_to + Language ||--o{ JobPostingTranslation : contains + Company ||--o{ DiversityReport : generates + +``` + +```mermaid +erDiagram + %% Incluye todas las entidades anteriores y agrega: + + LearningPlatform { + int id PK + string name + string api_endpoint + json credentials + } + + CourseReference { + int id PK + int platform_id FK + string external_id + string name + string description + } + + CandidateCourse { + int id PK + int candidate_id FK + int course_ref_id FK + datetime recommended_at + string status + } + + AIModelVersion { + int id PK + string model_name + string version + datetime deployed_at + json parameters + } + + ModelPerformance { + int id PK + int model_id FK + datetime recorded_at + float accuracy + json metrics + } + + LearningPlatform ||--o{ CourseReference : provides + CourseReference ||--o{ CandidateCourse : recommended_to + Candidate ||--o{ CandidateCourse : receives + AIModelVersion ||--o{ ModelPerformance : measured_by + +``` + +Este diseño de base de datos: + +1. Está organizado por fases según el roadmap +2. Incluye todas las funcionalidades requeridas +3. Soporta las configuraciones multinivel +4. Permite el almacenamiento de conversaciones del chatbot +5. Maneja evaluaciones y sus resultados +6. Soporta múltiples idiomas +7. Incluye métricas de diversidad +8. Mantiene referencias a sistemas externos +9. Permite seguimiento de recomendaciones y matches + +¿Necesitas que explique algún aspecto específico del diseño o que realice algún ajuste? + +# PROMT +ahora eres un experto creando diagramas C4 y tendras como objetivo crear un Diagrama C4 que llegue en profundidad al MVP, el contexto lo obtendras en el archivo adjunto. + + +# CLOUD +Analizaré el documento y crearé los diagramas C4 para el MVP del sistema ATS, comenzando desde el nivel de contexto hasta llegar al detalle de los componentes principales. + + + +```mermaid +%% Nivel 1: Diagrama de Contexto +graph TB + subgraph Sistema ATS + ATS["Sistema ATS\n[Software System]\nSistema de gestión de reclutamiento\nbasado en IA"] + end + + Recruiter["Reclutador\n[Person]\nUsuario principal del sistema"] + Candidate["Candidato\n[Person]\nAplica a posiciones"] + HRManager["Gerente RRHH\n[Person]\nGestiona procesos de selección"] + + JobBoards["Portales de Empleo\n[Software System]\nPlataformas externas de empleo"] + SocialMedia["Redes Sociales\n[Software System]\nPlataformas de difusión"] + + Recruiter -->|"Gestiona procesos\nde selección"| ATS + Candidate -->|"Aplica y interactúa"| ATS + HRManager -->|"Supervisa y configura"| ATS + ATS -->|"Publica ofertas"| JobBoards + ATS -->|"Distribuye contenido"| SocialMedia + +%% Nivel 2: Diagrama de Contenedores +graph TB + subgraph "Sistema ATS [Software System]" + WebApp["Aplicación Web\n[Container: React/TypeScript]\nInterfaz principal del sistema"] + MobileApp["Aplicación Móvil\n[Container: React Native]\nAcceso móvil al sistema"] + + APIGateway["API Gateway\n[Container: Node.js]\nGestión de APIs y autenticación"] + + CoreServices["Servicios Core\n[Container: Node.js/Express]\nLógica de negocio principal"] + + AIEngine["Motor IA\n[Container: Python]\nAnálisis y procesamiento IA"] + + Database["Base de Datos\n[Container: PostgreSQL]\nAlmacenamiento principal"] + + ChatbotService["Servicio Chatbot\n[Container: Python]\nInteracción automatizada"] + end + + WebApp -->|"API calls"| APIGateway + MobileApp -->|"API calls"| APIGateway + APIGateway -->|"Routes"| CoreServices + CoreServices -->|"Queries/Updates"| Database + CoreServices -->|"Solicita análisis"| AIEngine + CoreServices -->|"Gestiona conversaciones"| ChatbotService + +%% Nivel 3: Diagrama de Componentes (MVP) +graph TB + subgraph "Servicios Core [Container]" + JobManager["Gestor de Ofertas\n[Component]\nGestión de publicaciones"] + CandidateManager["Gestor de Candidatos\n[Component]\nGestión de aplicantes"] + RecruitmentFlow["Flujo de Reclutamiento\n[Component]\nGestión del proceso"] + Analytics["Análisis y Reportes\n[Component]\nMétricas y KPIs"] + + AIIntegration["Integración IA\n[Component]\nConexión con servicios IA"] + CommunicationManager["Gestor de Comunicaciones\n[Component]\nNotificaciones y mensajes"] + end + + JobManager -->|"Datos de ofertas"| Database + CandidateManager -->|"Perfiles y estados"| Database + RecruitmentFlow -->|"Estados del proceso"| Database + Analytics -->|"Consultas"| Database + + AIIntegration -->|"Solicitudes"| AIEngine + CommunicationManager -->|"Mensajes"| ChatbotService + +``` + +He creado un diagrama C4 completo que muestra los tres primeros niveles de la arquitectura del sistema ATS, enfocándome en el MVP. Expliquemos cada nivel: + +1. **Nivel 1 - Diagrama de Contexto:** + - Muestra los actores principales (Reclutador, Candidato, Gerente RRHH) + - Sistemas externos (Portales de Empleo, Redes Sociales) + - El sistema ATS como núcleo + +2. **Nivel 2 - Diagrama de Contenedores:** + - Aplicación Web y Móvil para acceso al sistema + - API Gateway para gestión de servicios + - Servicios Core para la lógica de negocio + - Motor IA para análisis y procesamiento + - Base de datos PostgreSQL + - Servicio de Chatbot + +3. **Nivel 3 - Diagrama de Componentes (MVP):** + - Gestor de Ofertas + - Gestor de Candidatos + - Flujo de Reclutamiento + - Análisis y Reportes + - Integración IA + - Gestor de Comunicaciones + +Los componentes mostrados corresponden específicamente a las funcionalidades del MVP mencionadas en el documento: +- Dashboard centralizado +- Filtrado automático +- Publicación de ofertas +- Chatbot inicial +- Análisis predictivo básico + +¿Te gustaría que profundice en algún nivel específico o que agregue más detalles sobre algún componente en particular? \ No newline at end of file From 05fd1e2ca14e6fa57a9a835a28704de449f829b1 Mon Sep 17 00:00:00 2001 From: pikelito Date: Tue, 29 Oct 2024 23:31:31 -0500 Subject: [PATCH 2/2] add documentation --- LTI_MAMB/ATS_MAMB.md | 0 LTI_MAMB/LTI_MAMB.md | 718 +++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 718 insertions(+) delete mode 100644 LTI_MAMB/ATS_MAMB.md diff --git a/LTI_MAMB/ATS_MAMB.md b/LTI_MAMB/ATS_MAMB.md deleted file mode 100644 index e69de29..0000000 diff --git a/LTI_MAMB/LTI_MAMB.md b/LTI_MAMB/LTI_MAMB.md index e69de29..2530051 100644 --- a/LTI_MAMB/LTI_MAMB.md +++ b/LTI_MAMB/LTI_MAMB.md @@ -0,0 +1,718 @@ +# Documento de Proyecto: Sistema ATS con Inteligencia Artificial + +## 1. Visión y Idea Central del Negocio + +### Visión +Convertirnos en el sistema ATS líder en el mercado colombiano, redefiniendo el proceso de selección de personal mediante el uso de inteligencia artificial avanzada. Nuestro objetivo es facilitar una gestión de talento más precisa, inclusiva y automatizada, mejorando la experiencia tanto de candidatos como de reclutadores. + +### Idea Central del Negocio +El sistema ATS aprovechará la IA para optimizar cada etapa del proceso de selección de personal, desde la identificación y atracción de talento hasta la evaluación, selección y el onboarding. A mediano plazo, buscamos ampliar nuestra presencia en el mercado, integrando nuestra plataforma con las mejores herramientas de Recursos Humanos y expandiéndonos a nivel regional, escalando tanto en funcionalidades como en capacidad de respuesta. + +A largo plazo, nos posicionaremos como una solución integral y adaptable que evoluciona constantemente con las necesidades del mercado laboral, integrando las últimas tecnologías de análisis predictivo, procesamiento de lenguaje natural y automatización de la experiencia del usuario. + +## 2. Beneficios y Diferencial con la Competencia + +Nuestro sistema ATS se destaca por ofrecer funcionalidades y beneficios avanzados, diseñados para posicionarse por encima de los sistemas ATS tradicionales en el mercado colombiano. A continuación, se describen los beneficios clave y los diferenciales respecto a la competencia: + +- **Automatización Inteligente**: A diferencia de los ATS tradicionales, nuestro sistema cuenta con IA para realizar análisis predictivo del ajuste candidato-empresa, mejorando la calidad de las contrataciones. + +- **Experiencia del Candidato Mejorada**: Con un chatbot de IA que asiste y responde consultas en tiempo real, ofrecemos una experiencia de usuario única que reduce la carga de trabajo del equipo de reclutamiento y aumenta el compromiso de los candidatos. + +- **Evaluación y Análisis de Emociones**: A través de tecnologías avanzadas de reconocimiento facial y procesamiento de lenguaje, el sistema evalúa emociones y comportamientos durante entrevistas virtuales, proporcionando insights profundos sobre la personalidad de los candidatos. + +- **Integración Multicultural y Multilingüe**: Nuestro sistema permite la evaluación de candidatos de diversas culturas y en diferentes idiomas, haciendo del ATS una herramienta ideal para empresas con alcance global o en expansión. + +- **Onboarding y Seguimiento Post-Contratación**: Incluimos un módulo que facilita el proceso de integración de nuevos empleados, mejorando su experiencia inicial y el ajuste a la cultura empresarial, lo cual no se encuentra comúnmente en los ATS tradicionales. + +## 3. Evaluación de la Propuesta de Valor y Viabilidad del Producto + +### Propuesta de Valor +La propuesta de valor de nuestro sistema ATS se centra en: + +- **Escalabilidad y Adaptabilidad**: Nuestra plataforma ha sido diseñada para escalar sin dificultades, ofreciendo funcionalidades avanzadas que pueden ampliarse en función de la demanda de las empresas. Esto asegura que tanto pequeñas empresas en crecimiento como grandes corporaciones encuentren valor en el sistema. + +- **Integración de IA**: La integración de la inteligencia artificial en análisis predictivo, procesamiento de lenguaje y chatbots de interacción en tiempo real eleva nuestro ATS más allá de las capacidades de la mayoría de los sistemas ATS en el mercado, que aún dependen en gran medida de procesos manuales y evaluaciones de palabras clave. + +- **Retención de Clientes**: Nuestro enfoque en la experiencia del candidato, la precisión en la selección y el soporte post-contratación aumentan las probabilidades de retención de clientes. Al ofrecer un sistema que permite una contratación eficiente y con un onboarding bien estructurado, incrementamos el valor de cada cliente a largo plazo. + +### Viabilidad del Producto + +Para determinar la viabilidad, tomamos en cuenta los siguientes factores: + +- **Retención de Clientes**: Nuestro objetivo es lograr una alta retención de clientes mediante la oferta de funcionalidades diferenciadas y un alto nivel de personalización. Esto permite que las empresas puedan ajustar el ATS según sus necesidades específicas, lo cual incrementa la satisfacción y la lealtad de los clientes. + +- **Crecimiento Proyectado**: Dada la creciente demanda de herramientas de IA en el proceso de selección, proyectamos un crecimiento anual significativo en la adopción del sistema, en especial entre medianas y grandes empresas del mercado colombiano y potencialmente en otros mercados de habla hispana en América Latina. + +- **Escalabilidad del Producto**: La plataforma se ha desarrollado con una infraestructura escalable que permite integrar nuevas funcionalidades sin afectar el rendimiento. Este enfoque garantiza que el sistema pueda evolucionar a medida que se desarrollen nuevas tecnologías y aumente el volumen de clientes. + +--- + +# Roadmap de Desarrollo: Sistema ATS con Inteligencia Artificial + +## Objetivo General +Construir un sistema ATS que utilice IA para optimizar el proceso de selección de talento, ofreciendo funcionalidades diferenciadas y escalables, que permitan a las empresas automatizar y mejorar sus procesos de contratación. El desarrollo se organizará en un **MVP** y varias fases, cada una con entregables específicos, diseñadas para ser escalables y operativas de forma independiente. + +--- + +## 1. MVP (Producto Mínimo Viable) + +**Objetivo del MVP**: Lanzar un sistema ATS funcional y básico, centrado en la automatización del flujo de reclutamiento inicial y con integración de IA para el análisis de perfiles y filtrado de candidatos. Este MVP debe ser productivo y escalable, permitiendo que las empresas puedan empezar a utilizarlo con las funcionalidades esenciales mientras se desarrollan fases adicionales. + +**Características Principales del MVP**: + +1. **Dashboard Centralizado de Candidatos**: + - Permite la visualización de candidatos en diferentes etapas del proceso de selección. + - Incluye filtros básicos para gestionar candidatos según etapa de proceso, habilidades y experiencia. + +2. **Filtrado Automático de Candidatos**: + - Sistema básico de IA que utiliza palabras clave y criterios específicos para descartar o recomendar candidatos según los requisitos del puesto. + +3. **Funcionalidad de Publicación de Ofertas**: + - Permite la publicación en múltiples plataformas y redes sociales mediante integraciones básicas. + - Incluye seguimiento de la fuente de cada candidato para mejorar el análisis futuro de efectividad. + +4. **Chatbot Inicial para Interacción de Candidatos**: + - Chatbot sencillo basado en IA para guiar a los candidatos en el proceso de aplicación y responder preguntas comunes. + - Mejora la experiencia del candidato y reduce el tiempo invertido en comunicación por el equipo de RRHH. + +5. **Análisis Predictivo de Ajuste Básico**: + - Algoritmo que predice el ajuste de los candidatos al perfil de la empresa según experiencia y habilidades. + - Permite que los reclutadores tengan una estimación inicial de los candidatos con mayor potencial. + +**Duración Estimada**: 3-4 meses + +--- + +## 2. Fase 1: Funcionalidades Avanzadas de IA y Evaluaciones + +**Objetivo**: Añadir funcionalidades avanzadas de IA para una evaluación más profunda de los candidatos y mejorar el análisis predictivo. La IA en esta fase permitirá análisis complejos en el comportamiento y las emociones de los candidatos, incrementando el valor agregado. + +**Características Clave**: + +1. **Evaluación Automática de Habilidades**: + - Integración de evaluaciones técnicas y tests psicológicos automatizados. + - IA para análisis automático de resultados con generación de reportes sobre habilidades y ajuste cultural. + +2. **Análisis de Emociones en Entrevistas Virtuales**: + - Tecnología de reconocimiento facial y análisis de tono de voz para detectar emociones y comportamientos en tiempo real durante entrevistas en línea. + +3. **Sistema de Matching Avanzado**: + - Algoritmo de matching que relaciona habilidades y experiencia con los requisitos del puesto de forma dinámica, mejorando el filtro inicial de candidatos. + +**Duración Estimada**: 3 meses + +--- + +## 3. Fase 2: Expansión de la Experiencia del Candidato y de la Escalabilidad + +**Objetivo**: Mejorar la experiencia del candidato a lo largo de todo el proceso y desarrollar funcionalidades que hagan el sistema más adaptable a empresas de diversos tamaños y sectores, apuntando a la escalabilidad. + +**Características Clave**: + +1. **Dashboard Personalizable**: + - Tablero de métricas personalizable que permite visualizar KPIs específicos del proceso de selección. + - Ofrece reportes de tiempo de contratación, fuentes de talento efectivas, y calidad de los candidatos. + +2. **Onboarding y Seguimiento Post-Contratación**: + - Sistema de onboarding automatizado para nuevos empleados, con integración de recursos y guías para su primer día. + - Módulo de seguimiento de satisfacción en las primeras semanas de los nuevos empleados. + +3. **Escalabilidad Multinivel**: + - Infraestructura para manejar múltiples niveles de clientes: desde pymes hasta grandes corporaciones. + - Capacidades de personalización de acuerdo con la industria o sector. + +**Duración Estimada**: 4 meses + +--- + +## 4. Fase 3: Integración Multilingüe y Diversidad e Inclusión + +**Objetivo**: Adaptar el sistema para que funcione en contextos multiculturales y diversos, permitiendo que las empresas gestionen el talento a nivel internacional y promuevan la inclusión en su proceso de contratación. + +**Características Clave**: + +1. **Capacidades Multilingües y Sensibilidad Cultural**: + - Sistema de procesamiento de CVs y cartas de presentación en varios idiomas, adaptado a contextos culturales específicos. + - Ajustes automáticos en los criterios de evaluación según la región de origen del candidato. + +2. **Análisis de Diversidad e Inclusión**: + - Herramientas de análisis que ofrecen datos sobre la diversidad de candidatos y recomendaciones para mejorar la equidad en el proceso de selección. + +3. **Funcionalidades de Recomendación de Candidatos Internos**: + - Sistema de recomendación que permite considerar a empleados actuales para vacantes abiertas según sus habilidades y desempeño. + +**Duración Estimada**: 4 meses + +--- + +## 5. Fase 4: Expansión de Integraciones y Adaptación a Nuevas Tecnologías + +**Objetivo**: Expandir la funcionalidad y la flexibilidad del sistema mediante integraciones avanzadas y adaptaciones a nuevas tecnologías que puedan beneficiar el sistema ATS, asegurando que la plataforma esté siempre actualizada. + +**Características Clave**: + +1. **Integración con Plataformas de Aprendizaje y Desarrollo**: + - Conexión con sistemas de capacitación para recomendar cursos a los candidatos o empleados, apoyando la movilidad interna y el desarrollo profesional. + +2. **Actualizaciones de Algoritmos y Optimización de IA**: + - Implementación de algoritmos mejorados para optimizar la precisión en análisis predictivo, filtrado y matching de candidatos. + +3. **Soporte para Nuevas Tecnologías de Evaluación**: + - Capacidad de adaptación para integrar tecnologías emergentes en evaluación de candidatos, como evaluaciones por realidad virtual o análisis biométricos. + +**Duración Estimada**: 3-4 meses + +--- + +## Resumen del Roadmap + +| Fase | Características Clave | Duración Estimada | +|-------------|--------------------------------------------------------------|--------------------| +| **MVP** | Dashboard, filtrado automático, chatbot básico, análisis predictivo inicial | 3-4 meses | +| **Fase 1** | Evaluaciones avanzadas de IA, análisis de emociones, matching avanzado | 3 meses | +| **Fase 2** | Dashboard personalizable, onboarding y seguimiento, escalabilidad multinivel | 4 meses | +| **Fase 3** | Multilingüe, análisis de diversidad e inclusión, recomendación de candidatos internos | 4 meses | +| **Fase 4** | Integración con L&D, optimización de IA, soporte para nuevas tecnologías | 3-4 meses | + +--- + +# Roadmap de Desarrollo: Sistema ATS con Inteligencia Artificial + +## Objetivo General +Construir un sistema ATS que utilice IA para optimizar el proceso de selección de talento, ofreciendo funcionalidades diferenciadas y escalables, que permitan a las empresas automatizar y mejorar sus procesos de contratación. El desarrollo se organizará en un **MVP** y varias fases, cada una con entregables específicos, diseñadas para ser escalables y operativas de forma independiente. + +--- + +## 1. MVP (Producto Mínimo Viable) + +**Objetivo del MVP**: Lanzar un sistema ATS funcional y básico, centrado en la automatización del flujo de reclutamiento inicial y con integración de IA para el análisis de perfiles y filtrado de candidatos. Este MVP debe ser productivo y escalable, permitiendo que las empresas puedan empezar a utilizarlo con las funcionalidades esenciales mientras se desarrollan fases adicionales. + +**Características Principales del MVP**: + +1. **Dashboard Centralizado de Candidatos**: + - Permite la visualización de candidatos en diferentes etapas del proceso de selección. + - Incluye filtros básicos para gestionar candidatos según etapa de proceso, habilidades y experiencia. + +2. **Filtrado Automático de Candidatos**: + - Sistema básico de IA que utiliza palabras clave y criterios específicos para descartar o recomendar candidatos según los requisitos del puesto. + +3. **Funcionalidad de Publicación de Ofertas**: + - Permite la publicación en múltiples plataformas y redes sociales mediante integraciones básicas. + - Incluye seguimiento de la fuente de cada candidato para mejorar el análisis futuro de efectividad. + +4. **Chatbot Inicial para Interacción de Candidatos**: + - Chatbot sencillo basado en IA para guiar a los candidatos en el proceso de aplicación y responder preguntas comunes. + - Mejora la experiencia del candidato y reduce el tiempo invertido en comunicación por el equipo de RRHH. + +5. **Análisis Predictivo de Ajuste Básico**: + - Algoritmo que predice el ajuste de los candidatos al perfil de la empresa según experiencia y habilidades. + - Permite que los reclutadores tengan una estimación inicial de los candidatos con mayor potencial. + +**Duración Estimada**: 3-4 meses + +--- + +## 2. Fase 1: Funcionalidades Avanzadas de IA y Evaluaciones + +**Objetivo**: Añadir funcionalidades avanzadas de IA para una evaluación más profunda de los candidatos y mejorar el análisis predictivo. La IA en esta fase permitirá análisis complejos en el comportamiento y las emociones de los candidatos, incrementando el valor agregado. + +**Características Clave**: + +1. **Evaluación Automática de Habilidades**: + - Integración de evaluaciones técnicas y tests psicológicos automatizados. + - IA para análisis automático de resultados con generación de reportes sobre habilidades y ajuste cultural. + +2. **Análisis de Emociones en Entrevistas Virtuales**: + - Tecnología de reconocimiento facial y análisis de tono de voz para detectar emociones y comportamientos en tiempo real durante entrevistas en línea. + +3. **Sistema de Matching Avanzado**: + - Algoritmo de matching que relaciona habilidades y experiencia con los requisitos del puesto de forma dinámica, mejorando el filtro inicial de candidatos. + +**Duración Estimada**: 3 meses + +--- + +## 3. Fase 2: Expansión de la Experiencia del Candidato y de la Escalabilidad + +**Objetivo**: Mejorar la experiencia del candidato a lo largo de todo el proceso y desarrollar funcionalidades que hagan el sistema más adaptable a empresas de diversos tamaños y sectores, apuntando a la escalabilidad. + +**Características Clave**: + +1. **Dashboard Personalizable**: + - Tablero de métricas personalizable que permite visualizar KPIs específicos del proceso de selección. + - Ofrece reportes de tiempo de contratación, fuentes de talento efectivas, y calidad de los candidatos. + +2. **Onboarding y Seguimiento Post-Contratación**: + - Sistema de onboarding automatizado para nuevos empleados, con integración de recursos y guías para su primer día. + - Módulo de seguimiento de satisfacción en las primeras semanas de los nuevos empleados. + +3. **Escalabilidad Multinivel**: + - Infraestructura para manejar múltiples niveles de clientes: desde pymes hasta grandes corporaciones. + - Capacidades de personalización de acuerdo con la industria o sector. + +**Duración Estimada**: 4 meses + +--- + +## 4. Fase 3: Integración Multilingüe y Diversidad e Inclusión + +**Objetivo**: Adaptar el sistema para que funcione en contextos multiculturales y diversos, permitiendo que las empresas gestionen el talento a nivel internacional y promuevan la inclusión en su proceso de contratación. + +**Características Clave**: + +1. **Capacidades Multilingües y Sensibilidad Cultural**: + - Sistema de procesamiento de CVs y cartas de presentación en varios idiomas, adaptado a contextos culturales específicos. + - Ajustes automáticos en los criterios de evaluación según la región de origen del candidato. + +2. **Análisis de Diversidad e Inclusión**: + - Herramientas de análisis que ofrecen datos sobre la diversidad de candidatos y recomendaciones para mejorar la equidad en el proceso de selección. + +3. **Funcionalidades de Recomendación de Candidatos Internos**: + - Sistema de recomendación que permite considerar a empleados actuales para vacantes abiertas según sus habilidades y desempeño. + +**Duración Estimada**: 4 meses + +--- + +## 5. Fase 4: Expansión de Integraciones y Adaptación a Nuevas Tecnologías + +**Objetivo**: Expandir la funcionalidad y la flexibilidad del sistema mediante integraciones avanzadas y adaptaciones a nuevas tecnologías que puedan beneficiar el sistema ATS, asegurando que la plataforma esté siempre actualizada. + +**Características Clave**: + +1. **Integración con Plataformas de Aprendizaje y Desarrollo**: + - Conexión con sistemas de capacitación para recomendar cursos a los candidatos o empleados, apoyando la movilidad interna y el desarrollo profesional. + +2. **Actualizaciones de Algoritmos y Optimización de IA**: + - Implementación de algoritmos mejorados para optimizar la precisión en análisis predictivo, filtrado y matching de candidatos. + +3. **Soporte para Nuevas Tecnologías de Evaluación**: + - Capacidad de adaptación para integrar tecnologías emergentes en evaluación de candidatos, como evaluaciones por realidad virtual o análisis biométricos. + +**Duración Estimada**: 3-4 meses + +--- + +## Resumen del Roadmap + +| Fase | Características Clave | Duración Estimada | +|-------------|--------------------------------------------------------------|--------------------| +| **MVP** | Dashboard, filtrado automático, chatbot básico, análisis predictivo inicial | 3-4 meses | +| **Fase 1** | Evaluaciones avanzadas de IA, análisis de emociones, matching avanzado | 3 meses | +| **Fase 2** | Dashboard personalizable, onboarding y seguimiento, escalabilidad multinivel | 4 meses | +| **Fase 3** | Multilingüe, análisis de diversidad e inclusión, recomendación de candidatos internos | 4 meses | +| **Fase 4** | Integración con L&D, optimización de IA, soporte para nuevas tecnologías | 3-4 meses | + +--- + +# Detalles de Casos de Uso por Fase + +## MVP - Funcionalidades Base +**Actores Principales:** +- Reclutador +- Candidato +- IA Básica + +**Casos de Uso:** +1. **Gestión de Ofertas** + - Publicar ofertas de trabajo + - Distribuir en plataformas + - Ver dashboard básico + +2. **Interacción Candidato** + - Aplicar a trabajos + - Interactuar con chatbot básico + - Recibir comunicaciones automáticas + +3. **Filtrado Básico** + - Filtrado automático de CVs + - Matching básico de perfiles + - Seguimiento de estados + +## Fase 1 - IA Avanzada +**Actores Principales:** +- Reclutador +- IA Avanzada +- Candidato + +**Casos de Uso:** +1. **Evaluación Avanzada** + - Realizar tests automáticos + - Analizar resultados con IA + - Generar reportes de competencias + +2. **Análisis Comportamental** + - Analizar emociones en entrevistas + - Evaluar comportamiento + - Generar insights + +## Fase 2 - Experiencia y Escalabilidad +**Actores Principales:** +- Reclutador +- Gerente RRHH +- Nuevo Empleado + +**Casos de Uso:** +1. **Gestión Personalizada** + - Personalizar dashboard + - Configurar KPIs + - Gestionar reportes + +2. **Onboarding** + - Gestionar proceso de onboarding + - Seguimiento de satisfacción + - Automatizar documentación + +## Fase 3 - Diversidad e Inclusión +**Actores Principales:** +- Reclutador Global +- IA Multilingüe +- Candidato Internacional + +**Casos de Uso:** +1. **Gestión Multilingüe** + - Procesar CVs en múltiples idiomas + - Adaptar evaluaciones culturalmente + - Gestionar comunicaciones multilingües + +2. **Análisis de Diversidad** + - Monitorear métricas de diversidad + - Generar recomendaciones + - Seguimiento de objetivos + +## Fase 4 - Integraciones Avanzadas +**Actores Principales:** +- Administrador de Sistema +- Ingeniero IA + +**Casos de Uso:** +1. **Gestión de Integraciones** + - Integrar plataformas de aprendizaje + - Configurar evaluaciones VR + - Optimizar algoritmos IA + +2. **Mejora Continua** + - Actualizar modelos de IA + - Implementar nuevas tecnologías + - Mantener integraciones + +--- + +# Diseño de base de datos en formato mermaid por fases + +Este diseño de base de datos: +1. Está organizado por fases según el roadmap +2. Incluye todas las funcionalidades requeridas +3. Soporta las configuraciones multinivel +4. Permite el almacenamiento de conversaciones del chatbot +5. Maneja evaluaciones y sus resultados +6. Soporta múltiples idiomas +7. Incluye métricas de diversidad +8. Mantiene referencias a sistemas externos +9. Permite seguimiento de recomendaciones y matches + +**MVP** +erDiagram + Company { + int id PK + string name + string industry + datetime created_at + boolean active + } + + User { + int id PK + int company_id FK + string email + string name + string password_hash + datetime created_at + boolean active + } + + Permission { + int id PK + string name + string description + string resource + string action + } + + UserPermission { + int user_id FK + int permission_id FK + } + + JobPosting { + int id PK + int company_id FK + string title + text description + string requirements + datetime created_at + datetime expiry_date + string status + } + + Candidate { + int id PK + string email + string name + text resume_url + datetime created_at + boolean active + } + + Application { + int id PK + int job_posting_id FK + int candidate_id FK + datetime applied_at + string status + float matching_score + } + + ChatConversation { + int id PK + int candidate_id FK + int job_posting_id FK + datetime started_at + datetime ended_at + } + + ChatMessage { + int id PK + int conversation_id FK + string sender_type + text message + datetime sent_at + } + + EvaluationCriteria { + int id PK + int company_id FK + string name + string description + float weight + } + + Company ||--o{ User : has + Company ||--o{ JobPosting : creates + User ||--o{ UserPermission : has + Permission ||--o{ UserPermission : assigned_to + JobPosting ||--o{ Application : receives + Candidate ||--o{ Application : makes + Candidate ||--o{ ChatConversation : participates_in + ChatConversation ||--o{ ChatMessage : contains + Company ||--o{ EvaluationCriteria : defines + +**fase 1** +erDiagram + %% Incluye todas las entidades del MVP y agrega: + + Skill { + int id PK + string name + string description + string category + } + + Test { + int id PK + string name + string description + string type + boolean active + } + + Question { + int id PK + int test_id FK + string question_text + string type + json options + } + + TestResult { + int id PK + int application_id FK + int test_id FK + datetime taken_at + json answers + float score + } + + InterviewEmotion { + int id PK + int application_id FK + datetime recorded_at + json emotion_data + float confidence_score + } + + MatchingHistory { + int id PK + int application_id FK + float score + json criteria_scores + datetime matched_at + } + + Application ||--o{ TestResult : has + Application ||--o{ InterviewEmotion : analyzed_in + Application ||--o{ MatchingHistory : evaluated_with + Test ||--o{ Question : contains + Application }o--|| JobPosting : belongs_to + +**fase 2** + +erDiagram + %% Incluye todas las entidades anteriores y agrega: + + SubscriptionTier { + int id PK + string name + string description + json features + } + + CompanySubscription { + int id PK + int company_id FK + int tier_id FK + datetime start_date + datetime end_date + } + + Metric { + int id PK + string name + string description + string calculation_method + } + + MetricHistory { + int id PK + int company_id FK + int metric_id FK + float value + datetime recorded_at + } + + OnboardingReference { + int id PK + int application_id FK + string document_type + string external_url + datetime created_at + } + + Company ||--o{ CompanySubscription : has + SubscriptionTier ||--o{ CompanySubscription : defines + Company ||--o{ MetricHistory : tracks + Metric ||--o{ MetricHistory : measured_in + Application ||--o{ OnboardingReference : includes + +**fase 3** + +erDiagram + %% Incluye todas las entidades anteriores y agrega: + + Language { + int id PK + string code + string name + } + + CompanyLanguage { + int id PK + int company_id FK + int language_id FK + boolean is_primary + } + + JobPostingTranslation { + int id PK + int job_posting_id FK + int language_id FK + string title + text description + string requirements + } + + DiversityMetric { + int id PK + string name + string description + } + + DiversityReport { + int id PK + int company_id FK + datetime generated_at + json metrics_data + } + + InternalRecommendation { + int id PK + int job_posting_id FK + int candidate_id FK + datetime recommended_at + string status + } + + Company ||--o{ CompanyLanguage : supports + Language ||--o{ CompanyLanguage : used_by + JobPosting ||--o{ JobPostingTranslation : translated_to + Language ||--o{ JobPostingTranslation : contains + Company ||--o{ DiversityReport : generates + +**fase 4** + +erDiagram + %% Incluye todas las entidades anteriores y agrega: + + LearningPlatform { + int id PK + string name + string api_endpoint + json credentials + } + + CourseReference { + int id PK + int platform_id FK + string external_id + string name + string description + } + + CandidateCourse { + int id PK + int candidate_id FK + int course_ref_id FK + datetime recommended_at + string status + } + + AIModelVersion { + int id PK + string model_name + string version + datetime deployed_at + json parameters + } + + ModelPerformance { + int id PK + int model_id FK + datetime recorded_at + float accuracy + json metrics + } + + LearningPlatform ||--o{ CourseReference : provides + CourseReference ||--o{ CandidateCourse : recommended_to + Candidate ||--o{ CandidateCourse : receives + AIModelVersion ||--o{ ModelPerformance : measured_by \ No newline at end of file

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