Question 1

What is AI Tool and what are its core features?

Accepted Answer

AI Tool is a collaborative task and project management platform that combines real-time team communication with AI-driven productivity suggestions. Core features include task and project CRUD, real-time team chat and notifications, AI-powered task prioritization and daily productivity scoring, automated project summaries, smart reminders tuned to user work rhythms, subscription and billing management, and analytics for engagement and performance. The system is built as a microservice architecture (React/Next frontend, Node.js/Go backends, Python AI service) so it supports multi-user workspaces, role-based access, and scalable deployments.

Question 2

How do AI suggestions work and how is my data handled?

Accepted Answer

AI suggestions are produced by the AI Assistant Service (Python + FastAPI) which ingests task and behavioral metadata (task attributes, timestamps, activity patterns) to infer priorities, detect potential burnout, and produce summaries and tips. Models can be invoked via OpenAI or Gemini APIs. Sensitive raw content is not sent to external services unless you enable cloud AI integrations; for enterprise or self-hosted deployments you can configure on-prem models or private endpoints. Authentication, access control and data partitioning are enforced at the User Service and API Gateway layers. You can opt out of AI processing at the workspace or user level if you prefer no AI analysis of your data.

Question 3

How do integrations, authentication, and real-time features work?

Accepted Answer

The platform exposes REST endpoints routed through the API gateway (e.g., /api/users/* → User Service, /api/tasks/* → Task Service, /api/chat/* → Chat Service). Authentication can be handled with Firebase Auth or a JWT-based custom auth; user service issues tokens at signup/login. Real-time chat and notifications use WebSockets/Socket.IO with Redis for pub/sub and session state. Payments integrate via Stripe for subscriptions and invoicing; webhooks are supported to notify your systems of billing events. Developers can extend functionality by adding microservices, consuming APIs, or subscribing to analytics webhooks.

Question 4

What are the pricing options and deployment/scaling considerations?

Accepted Answer

Pricing follows a freemium model (limited projects and members), a Pro plan (₹499 or $9.99/month for unlimited projects and AI insights), and an Enterprise tier with custom pricing, dedicated analytics, SLAs and support. The tech stack is containerized (Docker) and orchestrated with Kubernetes, and can be deployed to AWS, GCP, or Azure. For scaling, use the message queue (RabbitMQ or Kafka) for async workloads, Redis for caching and session scaling, and separate databases for operational (MongoDB for users/tasks) and transactional data (Postgres for payments/analytics). Enterprise customers can request dedicated clusters, on-prem deployment, or private AI endpoints to meet compliance and performance needs.

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