Building AI Powered Customer Relationship Management (CRM) System

Project title : 180-Day AI-Powered Customer Relationship Management (CRM) System from Scratch

This course guides you through constructing an AI-enhanced CRM platform, starting from a minimal viable product (MVP) that runs on your local machine and evolving it into a hyperscale system capable of managing millions of customer interactions daily. You'll code every day, layering in features like lead tracking, sales pipelines, automated insights via machine learning, and real-time collaboration. By the end, your CRM will handle complex tasks such as predictive lead scoring, personalized recommendations, and seamless scaling across distributed environments, all while emphasizing practical coding over abstract theory.

Course Details

Role : Full Stack Developer
Format : everyday code
Course difficulty : Intermediate
course_duration : 180 Lessons
Target Audience : Fresh computer Science & Engineering Grads, Software Engineers/Developers, Software designers, software architects, product managers, UI/UX Designers, Quality Assurance (QA) Engineers, SRE, DevOps Engineers, Data Engineers, Project Managers, Engineering Managers and Technical Writers, IT service industry consultant & engineers

Why This Course?

Building a CRM isn't just about managing contacts—it's about creating a system that anticipates customer needs using AI, scales effortlessly as your user base grows, and integrates smoothly into business workflows. In today's world, where companies like Salesforce process billions of queries daily, understanding how to architect such a platform gives you an edge in designing tools that drive real revenue. This course equips you with the hands-on skills to turn raw code into a production powerhouse, focusing on the subtle decisions that make systems resilient and efficient.

What You'll Build

You'll construct "InsightCRM," an AI-powered platform that starts as a simple local app for tracking leads and evolves into a distributed system handling 100 million requests per second. Features include drag-and-drop sales pipelines, AI-driven lead scoring (using models like random forests for predicting conversion rates), real-time analytics dashboards, and automated email campaigns. By Day 180, it'll support multi-tenant architectures, container orchestration, and edge computing for low-latency AI inferences, mirroring how HubSpot uses ML to personalize user experiences.

Who Should Take This Course?

If you're a fresh grad eager to bridge theory with code, a developer looking to level up to architect-level designs, a product manager wanting insight into scalability trade-offs, or a DevOps engineer aiming to automate AI deployments, this is for you. It's tailored for those who code daily but want structured guidance to tackle enterprise challenges.

What Makes This Course Different?

Unlike typical courses that skim theory or dump code snippets, this one enforces daily coding to build muscle memory for implementation details—like tuning database shards for query speed or optimizing ML models for inference latency. We draw from rare insights, such as how LinkedIn's CRM internals use graph databases for relationship mapping, providing practical tweaks you won't find in standard tutorials. Plus, it's local-first, using free tools, so you focus on building without setup hurdles.

Key Topics Covered

• Full-stack development with PERN stack

• AI/ML integration for predictive features

• Distributed systems principles for scaling

• Real-time data handling and security

• DevOps for automated deployments

• Performance optimization and monitoring

Prerequisites

Basic JavaScript knowledge, familiarity with Git, and a computer with Docker installed. No prior AI experience needed—we build it step by step.

Course Structure

The course progresses through eight major sections, each spanning multiple weeks:

1. Foundations and MVP Setup (Days 1-20): Establish core structure.

2. Frontend Interactivity (Days 21-40): Build user-facing features.

3. Backend Logic and Data Modeling (Days 41-60): Handle business rules.

4. AI/ML Fundamentals Integration (Days 61-90): Add intelligent capabilities.

5. Real-Time and Collaboration Features (Days 91-110): Enable live updates.

6. Scalability and Distributed Systems (Days 111-140): Prepare for high load.

7. DevOps and Deployment (Days 141-160): Automate operations.

8. Optimization, Security, and Capstone (Days 161-180): Polish for production.

Each section includes daily coding lessons that incrementally enhance the CRM, with success criteria like "Run a local query under 50ms" to mark progress.

Course Structure

The structure flows from single-machine basics to hyperscale distributed setups. We start with a monolithic MVP to grasp end-to-end flow, then refactor into microservices for modularity. AI elements are introduced midway, building on data foundations, before scaling them across nodes. DevOps comes later to automate what you've manually built, culminating in a system that could theoretically support enterprise loads like those in Zoho CRM's global operations.

2. Detailed Curriculum

Section 1: Foundations and MVP Setup (Days 1-20)

• Fundamental distributed systems principles: Consistency in local data stores, eventual consistency basics via simple queues.

• Advanced system design insights: Modular monoliths for rapid iteration, inspired by how Basecamp's CRM started simple before scaling.

• Implementation mechanics: Setting up PERN stack with Docker Compose for reproducibility.

• Production tuning: Basic indexing to speed up initial queries.

• List of all the lessons:
  Day 1: Install Docker and set up project repos; code a basic "Hello CRM" Express server. Success: Server runs on localhost:3000.
  Day 2: Initialize React frontend with Vite; connect to backend via a simple API call. Success: Display static welcome message.
  Day 3: Set up PostgreSQL container; create users table. Success: Insert and query a test user.
  Day 4: Implement JWT auth signup endpoint. Success: Generate token on registration.
  Day 5: Add login functionality; protect a route. Success: Access protected endpoint with token.
  Day 6: Design contacts schema; add CRUD endpoints. Success: Create and list contacts.
  Day 7: Frontend form for adding contacts; integrate with API. Success: Submit and see new contact.
  Day 8: Add leads table; link to contacts. Success: Associate lead with contact.
  Day 9: Basic search endpoint for contacts. Success: Filter by name.
  Day 10: Error handling middleware in Express. Success: Graceful 400 on invalid input.
  Day 11: Tailwind CSS integration for basic UI styling. Success: Styled contact list.
  Day 12: Zustand for client state management (e.g., sidebar toggle). Success: Toggle UI element.
  Day 13: TanStack Query for fetching contacts. Success: Cache and refetch data.
  Day 14: Docker Compose yml for full stack. Success: Up/down entire app with one command.
  Day 15: Environment variables for secrets. Success: Load JWT secret from .env.
  Day 16: Basic logging with Winston. Success: Log API requests to console.
  Day 17: Unit test for auth endpoint using Jest. Success: Pass signup test.
  Day 18: Add notes table; attach to leads. Success: Create note for lead.
  Day 19: Frontend display for notes. Success: Show notes under lead.
  Day 20: Review MVP; fix one bug. Success: App runs without errors.

Section 2: Frontend Interactivity (Days 21-40)

• Fundamental distributed systems principles: Client-side caching to reduce server load.

• Advanced system design insights: Optimistic updates for perceived speed, as in Gmail's compose flow.

• Implementation mechanics: Drag-and-drop with dnd-kit for pipelines.

• Production tuning: Virtualization for large lists to maintain 60fps.

• List of all the lessons:
  Day 21: Create sales pipeline columns (e.g., Prospect, Qualified). Success: Render empty board.
  Day 22: Add deal cards to pipeline. Success: Display deals in columns.
  Day 23: Integrate dnd-kit for dragging deals. Success: Move card between columns.
  Day 24: Persist drag changes via API. Success: Save new position.
  Day 25: Implement fractional indexing utility. Success: Insert deal without reindexing all.
  Day 26: Optimistic UI with TanStack onMutate. Success: Instant drag, rollback on fail.
  Day 27: Add modal for deal details. Success: Edit deal in popup.
  Day 28: Conditional rendering based on user role. Success: Hide edit if not owner.
  Day 29: Accessibility tweaks for dnd-kit (ARIA labels). Success: Keyboard navigation works.
  Day 30: Infinite scroll for contact list. Success: Load more on scroll.
  Day 31: Search bar with debounced input. Success: Filter contacts live.
  Day 32: Theme toggle with Tailwind dark mode. Success: Switch light/dark.
  Day 33: Form validation with React Hook Form. Success: Prevent invalid submit.
  Day 34: Error boundaries in React. Success: Catch and display UI error.
  Day 35: Lazy load components with Suspense. Success: Faster initial load.
  Day 36: Add charts with Recharts for deal stats. Success: Render bar chart.
  Day 37: Export contacts to CSV. Success: Download button works.
  Day 38: Import from CSV endpoint. Success: Upload and parse file.
  Day 39: Frontend progress indicators (spinners). Success: Show during API calls.
  Day 40: Refactor one component; add comments. Success: Cleaner code.

Section 3: Backend Logic and Data Modeling (Days 41-60)

• Fundamental distributed systems principles: ACID transactions for data integrity.

• Advanced system design insights: Polymorphic associations for flexible attachments, like in Notion's databases.

• Implementation mechanics: Recursive queries for hierarchies.

• Production tuning: Query optimization with EXPLAIN.

• List of all the lessons:
  Day 41: Expand schema for deals and pipelines. Success: Join tables query.
  Day 42: Implement deep clone for duplicating leads. Success: Copy with notes.
  Day 43: Transaction wrapper for clone. Success: Rollback on partial fail.
  Day 44: JSONB for custom fields on contacts. Success: Store arbitrary data.
  Day 45: Full-text search index. Success: Search across fields.
  Day 46: Rate limiting middleware. Success: Block excessive requests.
  Day 47: Background jobs with BullMQ. Success: Queue email send.
  Day 48: File upload endpoint with Multer. Success: Store attachment.
  Day 49: MinIO container for local S3 mock. Success: Upload to bucket.
  Day 50: RBAC permissions table. Success: Check delete permission.
  Day 51: Middleware for RBAC checks. Success: 403 on unauthorized.
  Day 52: Audit logs table. Success: Log user actions.
  Day 53: Pagination in API responses. Success: Limit/offset works.
  Day 54: Caching with Redis container. Success: Cache contact fetch.
  Day 55: Invalidate cache on update. Success: Fresh data after change.
  Day 56: Webhook endpoint for integrations. Success: Receive test payload.
  Day 57: Input sanitization with Joi. Success: Validate schema.
  Day 58: Error reporting to console. Success: Log stack traces.
  Day 59: Integration test for clone API. Success: End-to-end pass.
  Day 60: Schema migration with Knex. Success: Add new column.

Section 4: AI/ML Fundamentals Integration (Days 61-90)

• Fundamental distributed systems principles: Data pipelines for ML training.

• Advanced system design insights: Feature engineering for lead scoring, akin to how Marketo predicts engagement.

• Implementation mechanics: Using scikit-learn for models.

• Production tuning: Model serialization for fast loads.

• List of all the lessons:
  Day 61: Set up Python container for ML; install scikit-learn. Success: Run hello script.
  Day 62: Export CRM data to CSV for training. Success: Generate sample dataset.
  Day 63: Clean data with Pandas. Success: Handle missing values.
  Day 64: Feature selection for lead conversion. Success: Correlation matrix.
  Day 65: Train random forest classifier. Success: Fit model on data.
  Day 66: Evaluate model with cross-validation. Success: Accuracy > 70%.
  Day 67: Serialize model with joblib. Success: Save/load file.
  Day 68: Node.js endpoint to call Python script. Success: Predict via child process.
  Day 69: Integrate lead score in deal card. Success: Display score.
  Day 70: Add NLP for sentiment in notes (VADER). Success: Analyze text.
  Day 71: Recommendation engine basics (content-based). Success: Suggest similar contacts.
  Day 72: Embed contacts with Sentence Transformers. Success: Generate vectors.
  Day 73: Faiss for vector search. Success: Find nearest neighbors.
  Day 74: Periodic retraining job. Success: Schedule via cron.
  Day 75: Handle imbalanced data (SMOTE). Success: Oversample minority class.
  Day 76: Hyperparameter tuning with GridSearch. Success: Optimize params.
  Day 77: Explainable AI with SHAP. Success: Feature importance plot.
  Day 78: Add chatbot interface (simple rule-based). Success: Respond to queries.
  Day 79: Integrate Dialogflow mock for intent. Success: Parse user input.
  Day 80: ML monitoring endpoint. Success: Log prediction latency.
  Day 81: A/B testing for model versions. Success: Route to variant.
  Day 82: Data privacy: Anonymize training data. Success: Hash PII.
  Day 83: Clustering contacts with KMeans. Success: Group by behavior.
  Day 84: Visualize clusters with Matplotlib. Success: Render plot.
  Day 85: Time-series forecasting for sales (Prophet). Success: Predict trends.
  Day 86: Integrate forecast in dashboard. Success: Show graph.
  Day 87: Handle large datasets with Dask. Success: Parallel compute.
  Day 88: Model deployment as microservice. Success: Separate container.
  Day 89: API for inference. Success: Post data, get prediction.
  Day 90: Review AI features; tune one hyperparam. Success: Improved accuracy.

Section 5: Real-Time and Collaboration Features (Days 91-110)

• Fundamental distributed systems principles: Pub/sub for event broadcasting.

• Advanced system design insights: Conflict resolution in collaborative edits, like Google Docs' operational transforms.

• Implementation mechanics: SSE for updates.

• Production tuning: Connection pooling for efficiency.

• List of all the lessons:
  Day 91: Set up Redis for pub/sub. Success: Publish/subscribe test.
  Day 92: SSE endpoint for pipeline changes. Success: Push update.
  Day 93: Frontend listener for SSE. Success: Auto-refresh board.
  Day 94: Multi-user invites via email mock (MailHog). Success: Send invite.
  Day 95: Accept invite; add to team. Success: Shared pipeline access.
  Day 96: Conflict detection on concurrent drags. Success: Merge changes.
  Day 97: WebSocket fallback for bidirectional. Success: Implement chat.
  Day 98: Presence indicators (online users). Success: Show dot on avatar.
  Day 99: Notifications table; push alerts. Success: Bell icon updates.
  Day 100: Mobile responsiveness with Tailwind. Success: View on small screen.
  Day 101: Offline support with Service Worker. Success: Cache assets.
  Day 102: Sync offline changes on reconnect. Success: Replay actions.
  Day 103: Audit real-time events. Success: Log collaborations.
  Day 104: Rate limit SSE connections. Success: Prevent abuse.
  Day 105: Test concurrent users with scripts. Success: Simulate 10 clients.
  Day 106: Add comments to deals. Success: Post and display.
  Day 107: Mention users in comments (@). Success: Notify mentioned.
  Day 108: Emoji reactions. Success: Add to comment.
  Day 109: Version history for deals. Success: View changes.
  Day 110: Refactor real-time code. Success: Modular handlers.

Section 6: Scalability and Distributed Systems (Days 111-140)

• Fundamental distributed systems principles: Sharding and replication.

• Advanced system design insights: Leaderless replication for high availability, as in Cassandra-inspired CRM data stores.

• Implementation mechanics: Partition data by tenant.

• Production tuning: Load testing to find bottlenecks.

• List of all the lessons:
  Day 111: Introduce multi-tenant schema. Success: Isolate data per org.
  Day 112: Shard contacts by hash. Success: Route query to shard.
  Day 113: Set up Postgres replication. Success: Master-slave sync.
  Day 114: Read from replicas. Success: Balance load.
  Day 115: eventual consistency for non-critical updates. Success: Async propagate.
  Day 116: Circuit breaker pattern in API calls. Success: Fallback on fail.
  Day 117: Distributed tracing with Jaeger container. Success: Trace request.
  Day 118: Scale ML inference with multiple workers. Success: Queue predictions.
  Day 119: Caching layers with Redis clusters. Success: Cluster mode.
  Day 120: Horizontal scaling: Add backend instances. Success: Load balance.
  Day 121: NGINX as reverse proxy. Success: Route to instances.
  Day 122: Session affinity for stateful parts. Success: Sticky sessions.
  Day 123: Database connection pooling with PgBouncer. Success: Reuse connections.
  Day 124: Index optimization for shards. Success: Query <10ms.
  Day 125: Data migration between shards. Success: Move tenant data.
  Day 126: Fault injection testing. Success: Simulate node fail.
  Day 127: Auto-scaling script mock. Success: Add instance on load.
  Day 128: Distributed locks with Redis. Success: Prevent race.
  Day 129: GraphQL for flexible queries. Success: Replace REST endpoint.
  Day 130: Federation for microservices. Success: Stitch schemas.
  Day 131: Edge caching with Varnish mock. Success: Cache responses.
  Day 132: Handle 100M req/sec sim (Locust). Success: Test throughput.
  Day 133: Partition AI models by region. Success: Low-latency inference.
  Day 134: Consensus with Raft mock. Success: Elect leader.
  Day 135: Backup and restore strategy. Success: Dump/restore DB.
  Day 136: Zero-downtime deployments. Success: Blue-green switch.
  Day 137: Monitor with Prometheus. Success: Scrape metrics.
  Day 138: Alert on high latency. Success: Threshold trigger.
  Day 139: Optimize AI batch processing. Success: Group inferences.
  Day 140: Review scalability; fix one bottleneck. Success: Improved perf.

Section 7: DevOps and Deployment (Days 141-160)

• Fundamental distributed systems principles: Immutable infrastructure.

• Advanced system design insights: GitOps for declarative deploys, like in GitLab's CRM pipelines.

• Implementation mechanics: Kubernetes for orchestration.

• Production tuning: Resource limits to prevent OOM.

• List of all the lessons:
  Day 141: Set up Minikube for local K8s. Success: Cluster running.
  Day 142: Containerize backend. Success: Dockerfile build.
  Day 143: Deploy pod for app. Success: Access via kubectl.
  Day 144: Service for exposure. Success: Port forward works.
  Day 145: Helm chart for CRM. Success: Install app.
  Day 146: CI with GitHub Actions. Success: Build on push.
  Day 147: CD to local cluster. Success: Deploy on merge.
  Day 148: Secrets management with K8s. Success: Mount env.
  Day 149: Persistent volumes for DB. Success: Data survives restart.
  Day 150: Horizontal pod autoscaler. Success: Scale on CPU.
  Day 151: Ingress for routing. Success: Domain mapping.
  Day 152: Cert-manager for SSL. Success: HTTPS access.
  Day 153: Logging with ELK stack mock. Success: Aggregate logs.
  Day 154: Monitoring dashboard with Grafana. Success: View metrics.
  Day 155: Chaos engineering with Chaos Mesh. Success: Inject pod fail.
  Day 156: Backup cronjob. Success: Schedule dump.
  Day 157: Rollout strategies. Success: Canary deploy.
  Day 158: Integrate ML container. Success: Deploy model service.
  Day 159: Service mesh with Istio mock. Success: Traffic shift.
  Day 160: Full pipeline test. Success: End-to-end deploy.

Section 8: Optimization, Security, and Capstone (Days 161-180)

• Fundamental distributed systems principles: Secure enclaves for sensitive data.

• Advanced system design insights: Zero-trust networking, as in Okta's CRM access controls.

• Implementation mechanics: Encryption at rest/transit.

• Production tuning: Audit for compliance.

• List of all the lessons:
  Day 161: Encrypt DB columns. Success: Store encrypted PII.
  Day 162: HTTPS enforcement. Success: Redirect HTTP.
  Day 163: OAuth for third-party logins. Success: Google auth.
  Day 164: Vulnerability scan with Trivy. Success: Check images.
  Day 165: WAF rules mock. Success: Block SQL injection.
  Day 166: Compliance audit (GDPR mock). Success: Data export.
  Day 167: Performance profile with Clinic.js. Success: Flame graph.
  Day 168: Optimize queries with indexes. Success: Reduce time.
  Day 169: Bundle analysis for frontend. Success: Trim size.
  Day 170: Lighthouse audit. Success: Score >90.
  Day 171: Accessibility full check. Success: Fix issues.
  Day 172: Internationalization (i18n). Success: Translate strings.
  Day 173: Add custom theme support. Success: User prefs.
  Day 174: Integrate analytics (mock GA). Success: Track events.
  Day 175: Documentation generation. Success: API docs with Swagger.
  Day 176: Capstone: Add personal feature (e.g., voice notes). Success: Implement end-to-end.
  Day 177: Load test full system. Success: Handle 1000 req/sec local.
  Day 178: Security review; patch one vuln. Success: Secure code.
  Day 179: Refactor tech debt. Success: Cleaner modules.
  Day 180: Final demo; export project. Success: Running hyperscale CRM.

3. Learning Objectives

Section 1: Foundations and MVP Setup

• Construct a basic CRM MVP that authenticates users and manages core entities like contacts and leads.

• Configure a reproducible local environment using Docker, ensuring the app starts consistently.

• Implement essential API patterns with error handling, achieving reliable CRUD operations.

Section 2: Frontend Interactivity

• Develop interactive UI components, including drag-and-drop pipelines, with smooth user experiences.

• Apply state management and caching to minimize latency in data-heavy views.

• Ensure UI responsiveness and accessibility, meeting basic web standards for diverse users.

Section 3: Backend Logic and Data Modeling

• Model complex relational data with transactions and custom fields for flexibility.

• Enforce security through RBAC and input validation, preventing common exploits.

• Optimize backend performance with caching and jobs, handling growing data volumes.

Section 4: AI/ML Fundamentals Integration

• Build and integrate ML models for predictive features like lead scoring and recommendations.

• Manage data pipelines for training and inference, evaluating model effectiveness.

• Deploy AI components as services, monitoring for accuracy and speed in real use.

Section 5: Real-Time and Collaboration Features

• Implement event-driven updates using SSE and pub/sub for live collaboration.

• Handle conflicts and offline scenarios, ensuring data sync across users.

• Add notification systems that scale with team size, enhancing workflow efficiency.

Section 6: Scalability and Distributed Systems

• Partition and replicate data for horizontal scaling, managing high-throughput queries.

• Apply resilience patterns like circuit breakers to maintain availability under load.

• Simulate and tune for massive scale, understanding trade-offs in distributed designs.

Section 7: DevOps and Deployment

• Orchestrate containers with Kubernetes for automated, reliable deployments.

• Set up CI/CD pipelines that integrate testing and scaling.

• Monitor and log systems to detect issues proactively, ensuring operational smoothness.

Section 8: Optimization, Security, and Capstone

• Secure the platform end-to-end with encryption and access controls.

• Optimize for performance and compliance, auditing for production readiness.

• Synthesize all skills into a polished, extensible CRM, ready for real-world adaptation.