Salla Merchant Analytics Command Center

**Requirement ID**: REQ-OVR-004 **Status**: REMOVED --- ### **Enhancement Justification** * **Rationale**: To resolve a critical contradiction between the specified deployment platforms (AWS/EKS in the original REQ-OVR-004 vs. Vercel in REQ-OVR-001) and to streamline the SRS by removing redundant information. * **Description**: The requirement REQ-OVR-004 was removed from the specification. Its contents regarding deployment environments and client-side requirements were previously consolidated into REQ-OVR-001, making this requirement redundant and contradictory. This action solidifies the Vercel-first serverless architecture as the single source of truth. --- ### **Original Text (for historical reference)** The system shall be deployed on Amazon Web Services (AWS) using Amazon EKS (Elastic Kubernetes Service). The project shall maintain three separate environments: Development, Staging, and Production. The system shall not have specific end-user hardware requirements beyond a modern web browser. The client-side application shall require a modern, evergreen web browser (e.g., Chrome, Firefox, Safari, Edge). --- ### **Formalized Text** N/A (Requirement has been removed).

is_enhanced: true enhancement_justification: This section was added to address GAP-001. It defines the critical first steps a user must take to use the application, which were previously missing. This ensures a clear and functional entry point into the system. FR-101: User Registration The system shall allow merchants to register for an account using an email and password. The system shall create a new user account given a valid, unique email and a strong password. The system shall display an error message if a user provides an email that already exists. The system shall display an error message detailing password complexity requirements if a user provides a password that does not meet them. The system shall enforce that email addresses are in a valid format (BR-001). The system shall enforce that passwords meet minimum complexity requirements (BR-002). FR-102: User Authentication The system shall allow registered merchants to log in and out of the system securely. The system shall provide a 'Forgot Password' workflow. The system shall successfully log in a registered user and redirect them to their dashboard given correct credentials. The system shall display an error message given incorrect credentials. The system shall send an email with a time-sensitive reset link to a user's registered email address upon a password reset request. A logged-in user shall have an option to log out, which invalidates their session. FR-103: Salla Store Connection The system shall guide a merchant through an OAuth 2.0 flow to securely connect their Salla store upon first login. The system shall present a 'Connect Salla Store' prompt to a new user upon login. The system shall redirect the user to the Salla authentication page when the user initiates the connection. The system shall establish the connection and redirect the user back to the platform after successful authorization on Salla. The system shall redirect the user back with an appropriate error message if authorization fails or is denied. FR-104: Initial Data Synchronization The system shall initiate a one-time bulk import of historical data from the Salla store after successful store connection. The user interface shall show the progress of this initial sync. A background job shall be triggered to import historical data upon successful Salla store connection. The UI shall display a clear message indicating that the initial data sync is in progress. The user shall be able to navigate parts of the application that do not depend on historical data while the sync is running. The system shall notify the user upon completion of the sync. All dashboards shall be fully populated upon completion of the sync.

is_enhanced: true enhancement_justification: This section was added to address GAP-002. It acknowledges that merchant businesses often consist of teams, providing essential security and access control features required for professional use. FR-201: Role Definitions The system shall support at least four distinct user roles: Owner, Admin, Analyst, and Marketer. The user who initially connects the store shall be automatically assigned the 'Owner' role. Permissions for each role shall be strictly enforced across the entire application at both the UI and API levels. FR-202: Team Member Invitation The 'Owner' role shall be able to invite team members via email to access the command center. A user with the 'Owner' role shall be able to access a user management interface. The Owner shall be able to enter an email address and select a role to send an invitation. The invited user shall receive an email with a unique link to sign up and join the merchant's team. FR-203: Role Assignment The 'Owner' role shall be able to assign and change the roles of their team members. The Owner shall be able to see a list of all team members in the user management interface. The Owner shall be able to modify the role of any team member except their own. The Owner shall be able to remove a team member from the account, which revokes their access.

is_enhanced: true enhancement_justification: This section was expanded to address GAP-003. The vague term 'Deep Analytics' was replaced with specific, measurable, and testable requirements for dashboards, reports, filters, and functionalities. This provides a clear scope for development. FR-301: Central Dashboard The system shall provide a main overview dashboard displaying key performance indicators (KPIs). The dashboard shall display the following KPIs for a selected time period: Total Sales, Number of Orders, Average Order Value (AOV), Conversion Rate, and Abandoned Cart Rate. The user shall be able to select the time period for the dashboard (e.g., today, last 7 days, last 30 days). FR-302: Sales Trend Analysis The system shall provide detailed reports allowing merchants to analyze sales trends over time. The user shall be able to filter all sales reports by a date range, including presets and a custom range (FR-302.1). The user shall be able to select a second time period to compare performance against the primary period (FR-302.2). The user shall be able to group the sales data visualization by hour, day, week, or month (FR-302.3). FR-303: Customer Segmentation The system shall provide reports that segment customers based on behavior. The system shall provide reports segmenting customers as 'New' vs. 'Returning' (FR-303.1). The system shall allow segmentation of customers by total spending or AOV to identify high-value customers (FR-303.2). The system shall provide reports segmenting customers by city and country (FR-303.3). FR-304: Product Performance Reporting The system shall provide reports to identify best and worst-selling products. Product reports shall include metrics for Units Sold, Revenue Generated, and Conversion Rate per product (FR-304.1). The user shall be able to sort the product list by any key metric (FR-304.2). The user shall be able to filter the list by product category (FR-304.2). FR-305: Sales Forecasting The system shall provide a module that provides basic, algorithm-driven sales forecasts for the upcoming month based on historical data. The system shall display a projected sales figure and a trend line for the next 30 days. The forecast shall be clearly labeled as an estimate based on historical performance. FR-306: Data Export All reports and dashboard views shall have an option to be exported in CSV format. Every report and dashboard shall contain an 'Export to CSV' button. Clicking the export button shall download a CSV file containing the data currently displayed in the view, respecting any applied filters.

is_enhanced: true enhancement_justification: This section was detailed to address GAP-004. It clarifies the specific capabilities of the AI Assistant, moving from a generic concept to a defined set of features (NLQ on specific data, proactive alerts, rule-based suggestions), which makes the requirement actionable. FR-401: Natural Language Queries (NLQ) Merchants shall be able to ask natural language questions about their business data. The UI shall provide an input field for users to type questions. The system shall correctly parse questions related to the metrics defined in the Deep Analytics section. The system shall return a clear, concise answer to the user's query. The system shall respond with a helpful message if a query cannot be understood. FR-402: Proactive Insights & Alerts The AI assistant shall proactively generate and display insight cards on the dashboard. The dashboard shall display a dedicated section for AI-generated insights. The system shall generate alerts for significant positive or negative deviations from historical norms. The system shall generate alerts for anomalies. FR-403: Optimization Suggestions The assistant shall provide suggestions based on predefined rules and patterns. The system shall analyze patterns in the data to provide actionable suggestions. Suggestions shall be based on predefined rules (e.g., if a product has a high 'add to cart' rate but a low purchase rate, suggest reviewing price/shipping). Suggestions shall be displayed clearly in the UI, separate from alerts.

is_enhanced: true enhancement_justification: This section was expanded to address GAP-005. It adds critical configuration and management capabilities (customizable sequences, template management, performance tracking) that are essential for merchants to effectively use the cart recovery feature. FR-501: Abandoned Cart Tracking The system shall identify and list all abandoned carts. The list shall include customer contact information (if available) and the items in the cart. A dedicated section in the UI shall list all abandoned carts within a selectable time frame. Each entry shall show the customer's name and email (if known), the time of abandonment, the total value of the cart, and the items in the cart. FR-502: Automated Follow-up Sequences Merchants shall be able to create multi-step recovery campaigns. The user shall be able to create a sequence of follow-ups and define the delay for each step (FR-502.1). The system shall send follow-ups via email (FR-502.2). The system shall automatically stop a sequence for a customer if they complete their purchase. FR-503: Email Template Management Merchants shall be able to create, edit, and manage multiple email templates for their recovery campaigns. The UI shall provide a rich text editor for creating and editing email templates. Templates shall support dynamic variables (e.g., `{{customer_name}}`, `{{cart_items}}`, `{{checkout_link}}`). Users shall be able to save, name, and delete multiple templates. FR-504: Performance Analytics The module shall provide analytics on the effectiveness of the recovery campaigns. The Cart Recovery dashboard shall display metrics including the number of emails sent, open rate, click-through rate, number of carts recovered, and total value of recovered sales. The data shall be filterable by date range and by specific campaign.

This Software Requirement Specification (SRS) document shall serve as the single source of truth for all stakeholders. The development team shall use this document to implement the system. The QA team shall use this document to create test plans and test cases. Project managers shall use this document to manage the project scope. The document shall define the functional, non-functional, and technical requirements of the system.

IN-SCOPE: The system shall be a web-based Progressive Web App (PWA). The system shall provide analytics and business management tools for Salla e-commerce merchants. The system shall provide secure user onboarding and integration with the Salla platform via OAuth 2.0. The system shall include a deep analytics module with dashboards for sales, products, and customers. The system shall include an AI-powered assistant for natural language queries and proactive insights. The system shall include an automated cart recovery module with customizable email sequences. The system shall provide Role-Based Access Control (RBAC) for merchant teams. The system shall perform data synchronization via Salla APIs and Webhooks. OUT-OF-SCOPE: The system shall not provide direct management of Salla store inventory, products, or orders. The system shall not process payments or handle any payment gateway integrations. The system shall not include native mobile applications for iOS or Android. The system shall not support e-commerce platforms other than Salla. The system shall not include customer-facing features.

The following definitions, acronyms, and abbreviations shall be used throughout the project documentation: AOV: Average Order Value. API: Application Programming Interface. CQRS: Command Query Responsibility Segregation. ETL/ELT: Extract, Transform, Load / Extract, Load, Transform. JWT: JSON Web Token. KPI: Key Performance Indicator. LCP: Largest Contentful Paint. NLQ: Natural Language Query. OAuth 2.0: An open standard for access delegation. OLAP: Online Analytical Processing. OLTP: Online Transactional Processing. PWA: Progressive Web App. RBAC: Role-Based Access Control. SRS: Software Requirement Specification. UI/UX: User Interface / User Experience. Webhook: An automated message sent from an app when something happens.

The project shall adhere to and reference the following documents: - Original User Requirements Document - Gaps and Contradiction Analysis Report - Technical Architecture & Solution Design Document - Salla API Documentation

The user interface shall be constructed using the shadcn/ui component library. The application shall be fully responsive and provide a seamless experience on desktop, tablet, and mobile devices. The UI shall adhere to Web Content Accessibility Guidelines (WCAG) 2.1 Level AA standards. The application shall be primarily controlled via mouse/trackpad clicks and keyboard input on desktop. The application shall be primarily controlled via touch gestures (tap, swipe) on mobile devices. All interactive elements shall have clear focus states for keyboard navigation. The application shall feature a clean, data-focused design with a light and dark mode option.

The system shall use Salla's REST APIs for the initial bulk data import and for periodic data reconciliation. The system shall consume Salla Webhooks for real-time updates on key events. An anti-corruption layer shall be implemented to translate Salla data models into the system's internal domain models. The system shall integrate with a third-party email service (e.g., AWS SES, SendGrid) to send transactional and cart recovery emails. The AI Assistant shall integrate with an external NLP service (e.g., AWS Lex, Google Dialogflow) to parse natural language queries.

All communication between the client and the backend servers shall occur over HTTPS using TLS 1.2 or higher. All communication between backend services and external APIs shall occur over HTTPS using TLS 1.2 or higher. Data exchanged between the client and the backend API shall be in JSON format. Data exported from reports shall be in CSV format. Client-server communication shall be secured using JWT Bearer token authentication.

Standard user interface interactions and transactional API calls shall have a p95 response time below 200ms under normal operating conditions (NFR-101). Complex, data-intensive operations, such as generating large analytical reports, shall be processed asynchronously (NFR-102). The user shall be notified when an asynchronous operation is initiated and again when the report is ready (NFR-102). The application's key pages shall achieve a Largest Contentful Paint (LCP) of less than 2.5 seconds (NFR-103). Real-time updates from Salla Webhooks shall be reflected in the UI within 30 seconds of the event occurring in the Salla store (NFR-104).

The primary OLTP database (PostgreSQL) shall be configured with automated daily snapshots and point-in-time recovery capabilities provided by the managed database service. The OLAP database (ClickHouse) shall have a scheduled backup process to a cloud object store (e.g., Amazon S3). A disaster recovery plan shall be in place to restore service from backups using a multi-region or multi-cloud data persistence strategy. The system shall be designed for high availability by leveraging Vercel's global edge network for compute and multi-region capabilities of its managed data stores. The system shall use message queues (e.g., Vercel Q, Upstash QStash) for inter-service communication to prevent data loss in case of a temporary service failure. Requirement updated for consistency with the Vercel-based architecture defined in REQ-OVR-001. References to AWS-specific high availability and disaster recovery mechanisms (Availability Zones, regions) have been replaced with strategies applicable to a Vercel and managed services ecosystem.

User authentication shall be handled via JWTs. The system shall issue short-lived access tokens (15 minutes) and long-lived refresh tokens (30 days). Refresh tokens shall be stored securely in an `HttpOnly` cookie. A robust Role-Based Access Control (RBAC) model shall be implemented. API endpoints shall be protected by middleware that validates the user's role and permissions for every request. All data shall be encrypted in transit using HTTPS/TLS 1.2+. All data shall be encrypted at rest in all data stores, using the managed service's native encryption capabilities. All sensitive credentials, including database passwords and API keys, shall be stored securely using Vercel Environment Variables (for build-time and runtime secrets). Applications shall access secrets securely as provided by the Vercel platform. All database queries at the application layer shall be strictly scoped by `merchant_id` to ensure tenant data isolation. Requirement updated to align with the new Vercel-based technology stack. References to AWS-specific security services (KMS, Secrets Manager, IAM) have been replaced with their Vercel-ecosystem equivalents or platform-agnostic alternatives to ensure the security posture remains robust in the new architecture.

Availability: The system shall maintain an uptime of 99.9%, excluding planned maintenance. Planned maintenance windows shall be scheduled during off-peak hours. Planned maintenance shall be communicated to users at least 48 hours in advance. Scalability: All backend serverless functions shall be stateless and designed to scale automatically. The execution of serverless functions shall be automatically scaled by the Vercel platform based on incoming traffic. The system shall be designed to support thousands of concurrent users per merchant. The architecture shall accommodate significant data growth without performance degradation. Maintainability: The system shall be built using a serverless architecture to promote maintainability. A minimum of 80% unit test code coverage shall be required for all backend services. Code shall adhere to defined linting and style standards. Infrastructure for external managed services (e.g., databases) shall be defined and managed using Terraform. Requirement updated to align with the Vercel deployment model. The scalability section was modified to replace Kubernetes-specific terminology ('containers', 'Horizontal Pod Autoscaler') with Vercel's serverless scaling paradigm.

RISK-001 Mitigation: The Data Ingestion service shall implement a rate limiter that respects Salla's API limits. RISK-001 Mitigation: The system shall use an exponential backoff strategy for retries on rate-limited requests. RISK-001 Mitigation: The system shall prioritize real-time webhooks over historical polling. RISK-002 Mitigation: The system shall implement strict data retention policies. RISK-002 Mitigation: The data warehouse shall use partitioning and clustering keys effectively. RISK-002 Mitigation: The system shall be designed to start with a smaller instance size and scale up as needed.

The system shall be a standalone, cloud-hosted SaaS platform deployed on Vercel. The system shall integrate with the Salla e-commerce ecosystem. The system shall function as a supplementary data analysis and business intelligence tool. The system shall pull data from a merchant's Salla store via official APIs and Webhooks. The system shall not modify the Salla store's core data. The system shall provide a sophisticated layer of analytics and automation on top of Salla data. The backend infrastructure shall run on Vercel, utilizing PostgreSQL, Redis, and Prisma. The project shall maintain three separate environments: Development, Staging, and Production. The system shall not have specific end-user hardware requirements beyond a modern web browser. The client-side application shall require a modern, evergreen web browser (e.g., Chrome, Firefox, Safari, Edge). User requested modification to specify the deployment platform and technology stack. The requirement was enhanced to incorporate Vercel as the deployment target and specify the use of PostgreSQL, Redis, and Prisma for the backend. Details regarding development environments and client-side browser requirements were also added as requested. The original statement about microservices was removed to avoid direct contradiction within this single requirement.

The system shall provide seamless user registration and secure connection to a Salla store. The system shall provide multi-user access with predefined roles for team collaboration. The system shall provide a comprehensive suite of dashboards and reports for sales, customer, and product analysis. The system shall provide an intelligent assistant for natural language data queries, proactive alerts, and optimization suggestions. The system shall provide an automated system to track and recover abandoned shopping carts via email campaigns.

The system shall support the 'Store Owner / Admin' user persona. The system shall support the 'Data Analyst' user persona. The system shall support the 'Marketer' user persona. The 'Owner' role shall have full access to all features, billing, and user management. The 'Admin' role shall have full access to all features except billing and user management. The 'Analyst' role shall have view-only access to all analytics dashboards and reports. The 'Analyst' role shall not have access to Cart Recovery or AI suggestion configurations. The 'Marketer' role shall have access to the Cart Recovery module and related analytics. The 'Marketer' role shall have view-only access to other dashboards.

The frontend UI shall be built using the shadcn/ui component library. The frontend shall be built using React and Tailwind CSS. The system shall integrate exclusively with the Salla e-commerce platform using its official APIs and Webhooks. The application shall be developed as a Progressive Web App (PWA). The system shall be designed as a multi-tenant application. The system shall enforce strict data isolation between merchants. The system shall use a database-level multi-tenancy model where every merchant-specific record is scoped by a `merchant_id`.

The system shall be designed with the assumption that users will accept longer processing times for complex, non-real-time analytical reports. The system's functionality shall be critically dependent on the availability, performance, and versioning of the Salla Platform's APIs and Webhooks. The system shall depend on a third-party service for email delivery. The system shall depend on a third-party service for Natural Language Processing.

Vercel's integrated observability tools shall be used for monitoring performance and application metrics. A centralized logging solution shall be implemented by integrating Vercel with a third-party service (e.g., Logtail, Datadog). All serverless functions shall output structured logs (JSON format) to stdout/stderr for collection. OpenTelemetry shall be integrated into all services to enable distributed tracing of requests. The integrated Vercel dashboard and/or a third-party service shall be the primary tool for creating monitoring dashboards and defining alert rules. Critical alerts shall be sent to an on-call notification system (e.g., PagerDuty, Slack). Dashboards shall be created to monitor System Health (function invocations, duration, errors). Dashboards shall be created to monitor API Performance (latency, error rates, request volume). Dashboards shall be created to monitor Business Metrics (new sign-ups, active merchants, data ingestion volume). Requirement updated to reflect the observability stack suitable for a Vercel deployment. Kubernetes-centric tools like Prometheus have been replaced with Vercel's built-in observability features and integrations with third-party log management services.

The system shall be built on a serverless-first architecture leveraging Vercel Functions. The system shall employ a Command Query Responsibility Segregation (CQRS) pattern, with commands and queries handled by distinct serverless functions. The system shall use a managed PostgreSQL service as the OLTP database for transactions. The system shall use a managed ClickHouse service as the OLAP data warehouse for analytics. Requirement was updated to align with the strategic shift to a Vercel-based deployment model (see REQ-OVR-001). The architecture is redefined as 'serverless-first' to accurately reflect Vercel's paradigm, replacing the previous 'containerized microservices' approach. This resolves the conflict with the deprecated EKS-based strategy.

Frontend Framework shall be React v18+ with Next.js v14+. Frontend State Management shall be Zustand. Frontend UI Components shall be shadcn/ui. Frontend Styling shall be Tailwind CSS. Frontend Build & Hosting shall be on Vercel. Backend Language & Framework shall be TypeScript with NestJS v10+. Backend ORM shall be Prisma. Backend API Design shall be RESTful with an OpenAPI (Swagger) specification. Backend Authentication shall use JWT (JSON Web Tokens). Primary Database (OLTP) shall be a managed PostgreSQL v15+ service (e.g., Vercel Postgres, Neon). Analytical Database (OLAP) shall be a managed ClickHouse service. Caching layer shall be a managed Redis service (e.g., Vercel KV, Upstash). File Storage shall be a cloud object store (e.g., Amazon S3, Cloudflare R2). Cloud Provider shall be Vercel for compute, supplemented by managed data services. Container Orchestration shall be Not Applicable (superseded by serverless architecture). Serverless Computing shall use Vercel Functions. CI/CD Pipeline shall be GitHub Actions. Infrastructure as Code shall be managed with Terraform for non-Vercel resources. The technology stack has been comprehensively updated to resolve major contradictions introduced by the shift to a Vercel deployment platform (REQ-OVR-001). AWS-specific services like EKS, Lambda, and RDS have been replaced with Vercel-native or platform-agnostic equivalents (Vercel Functions, Managed PostgreSQL). Prisma has been officially added as the ORM, aligning the stack with the new architectural direction.

### **Project Summary: Salla Merchant Analytics Command Center** This document outlines the requirements for an advanced analytics and business management platform designed specifically for Salla e-commerce merchants. The system's core purpose is to serve as a centralized "command center," offering merchants comprehensive visibility and control over their sales and business operations. #### **Core Functional Requirements** The platform will be built around three key features: * **Deep Analytics**: The system will offer robust analytical tools. This includes, but is not limited to, sales trend analysis, customer segmentation, product performance reporting, and sales forecasting to empower merchants with actionable data. * **AI Assistant**: An integrated AI-powered assistant will provide intelligent insights. Merchants will be able to ask natural language questions about their business data and receive suggestions for optimizing their store's performance. * **Cart Recovery**: A dedicated module will be implemented to track and recover abandoned shopping carts. This feature will use automated follow-ups like emails and notifications to help merchants reclaim lost sales. #### **Target Audience** * The exclusive users of this platform are **e-commerce merchants** operating on the Salla platform. #### **Technical and Design Specifications** The system must adhere to the following non-functional and design requirements: * **Scalable Architecture**: The underlying infrastructure must be designed to scale efficiently, accommodating growth in data and user traffic without performance degradation. * **High Performance**: The application must be fast and responsive. A key performance indicator (KPI) is to maintain API and user interaction response times below 200ms under normal operating conditions. * **Progressive Web App (PWA)**: The application will be developed as a PWA, ensuring a seamless, installable, and app-like experience on both desktop and mobile devices. * **UI/UX Design**: The user interface will be constructed using the **shadcn/ui** component library to ensure a modern, consistent, and high-quality user experience.

# Software Requirements Specification: Salla Merchant Analytics Command Center ## 1.0 Project Goal The primary goal is to build an analytics system for Salla e-commerce <<$Change>>merchants<<$Change>>. This system will serve as a <<$Change>>command center<<$Change>>, providing them with full <<$Change>>visibility<<$Change>> for <<$Change>>managing their<<$Change>> sales and ongoing business operations. #### Enhancement Justification * **Reasoning**: Corrected several spelling and grammatical errors ("merchnets", "comand", "visability", "manageing", "there") to improve the clarity and professionalism of the core project statement. The functional goal remains identical. ## 2.0 Key Features ### 2.1 Deep Analytics The system must provide deep analytics capabilities. <<$Addition>>(Examples include, but are not limited to: sales trend analysis, customer segmentation, conversion rate optimization funnels, product performance reports, and sales forecasting.)<<$Addition>> #### Enhancement Justification * **Reasoning**: The original term "deep analytics" is ambiguous. Adding concrete examples provides a clear, actionable scope for development, ensures key functionalities are included, and prevents scope creep. ### 2.2 AI Assistant The system will include an AI <<$Change>>Assistant<<$Change>>. <<$Addition>>(This assistant should leverage technologies like Large Language Models (LLMs) to provide data-driven insights, answer natural language queries about sales data, and suggest automated actions to improve store performance.)<<$Addition>> #### Enhancement Justification * **Reasoning**: Corrected a spelling error ("assitant"). The addition clarifies the technical approach and expected capabilities of the AI feature, transforming a vague concept into a well-defined and testable requirement. ### 2.3 Cart Recovery The system must feature a cart <<$Change>>recovery<<$Change>> module. <<$Addition>>(This includes functionality to track abandoned shopping carts and trigger automated follow-up actions, such as customized emails or push notifications, to encourage customers to complete their purchase.)<<$Addition>> #### Enhancement Justification * **Reasoning**: Corrected a spelling error ("recovary"). The added text defines the core functionality of the cart recovery system, making the requirement specific and actionable for the development team. ## 3.0 Target Users The primary target users for this system are e-commerce <<$Change>>merchants<<$Change>> using the Salla platform. #### Enhancement Justification * **Reasoning**: Corrected a spelling error ("merchents") for clarity. ## 4.0 Non-Functional Requirements (Technology Preferences) ### 4.1 Architecture The system architecture must be <<$Change>>scalable<<$Change>> to handle growing data volumes and user loads. #### Enhancement Justification * **Reasoning**: Corrected a spelling error ("scalble"). ### 4.2 Performance The application must be <<$Change>>fast<<$Change>>. <<$Addition>>(As a target, key user interactions and API response times should be under 200ms under typical load conditions.)<<$Addition>> #### Enhancement Justification * **Reasoning**: The term "fast" is subjective. Adding a quantifiable performance metric (e.g., <200ms) transforms this into a specific, measurable, and testable non-functional requirement. ### 4.3 Platform Support The application must be delivered as a Progressive Web App (PWA) to ensure accessibility across devices and provide an app-like experience. ## 5.0 Design Requirements ### 5.1 UI Component Library The user interface is to be built using the <<$Change>>shadcn/ui<<$Change>> component library. #### Enhancement Justification * **Reasoning**: Corrected a typo ("chadin ui") to specify the correct and intended UI component library. This is a critical detail for the front-end development team.