Northstar CRM: Full-Stack Customer Relationship and Sales Management Platform

I designed and developed Northstar CRM to transform customer relationship management into a focused, visually intuitive, and connected workspace. Instead of overwhelming sales teams with dense tables and fragmented tools, I structured the platform around clear customer insights, actionable sales data, and streamlined daily workflows.

The visual identity combines a soft atmospheric background with glass-inspired surfaces, dimensional cards, and vibrant blue, teal, yellow, and charcoal accents. Carefully layered shadows, subtle gradients, and elevated components create a refined 3D appearance while maintaining clarity and professional usability. The entire interface follows a consistent 4:3 presentation ratio, making every screen suitable for portfolio displays and high-quality screenshots.

The overview dashboard gives users an immediate picture of business performance. Key metrics—including total revenue, pipeline value, completed tasks, priority deals, recent activities, and upcoming meetings—are presented through scannable cards, progress indicators, compact charts, and contextual status colours. This allows teams to identify important changes and required actions without navigating through multiple reports.

The customer management screen provides a searchable directory with company information, recent interactions, account value, relationship status, and assigned ownership. Selecting a customer dynamically updates the detailed profile panel, where users can review contact information, relationship health, next actions, and communication options such as messaging, calling, or scheduling a meeting.

The deal pipeline organizes opportunities into clear stages, including Discovery, Proposal, Negotiation, and Closing. Each opportunity card displays its customer, estimated value, expected closing date, owner, and current stage. The Kanban-style structure gives sales teams a visual understanding of pipeline movement while supporting faster prioritization and opportunity management.

The calendar and reporting screens connect operational planning with performance analysis. The calendar provides monthly scheduling, customer meetings, deadlines, focus time, and team commitments. The reporting workspace combines revenue trends, conversion rates, customer retention, pipeline sources, account health, and period-based performance comparisons through interactive charts and summary cards.

On the backend, I implemented a modular Node.js and Express REST API connected to a PostgreSQL database. The data model supports users, customers, companies, contacts, deals, pipeline stages, activities, tasks, meetings, notes, and reporting records. Structured relationships and indexed queries ensure that customer histories, pipeline summaries, and dashboard analytics remain fast and consistent as the dataset grows.

Authentication is handled through secure token-based sessions, password hashing, protected API routes, and role-based access control. Different permissions can be assigned to administrators, sales managers, and sales representatives, ensuring users only access or modify information relevant to their responsibilities. Input validation, centralized error handling, audit logging, and rate limiting strengthen reliability and application security.

Real-time updates allow changes to deals, tasks, customer activity, and meeting schedules to appear across connected screens without requiring a manual refresh. Background processes handle reminders, notifications, scheduled follow-ups, and reporting calculations, while caching improves the performance of frequently requested dashboard metrics.

Across the complete full-stack platform, I prioritized reusable frontend components, responsive layouts, secure API design, reliable data relationships, and consistent interaction patterns. The result is a modern CRM experience that feels premium and approachable while providing the technical foundation required for scalable customer management, sales execution, and business intelligence.

The backend model is the interesting part here. Once customer history, pipeline stages, tasks, and meetings share relationships, it’s easy for dashboard numbers to drift from the underlying records. Did you calculate those summaries on demand, or keep derived reporting data updated through the background jobs you mentioned?

If you have ever been handed a dataset and wanted to know the integrity of the content, and any key information sitting inside it, you needed a descriptive analysis.

This is a descriptive analysis of Canada Structures, the federal building-footprint product. A file like this is assembled from several sources and file types, and that mix can leave artifacts. The work was looking for those artifacts so the file can be trusted before anyone treats stored height or type as national fields.

National count is 13,760,754, matching the spec. Every Nunavut footprint stores height as zero (11,068). 66.5% of footprints have a positive stored height. 82.4% carry no building type. Shape, size, and province together score 0.42 at guessing source class, against 0.32 from province alone.

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