How did you handle multi-tenancy for data isolation?

We used a database-per-tenant model with MongoDB, where each tenant has their own namespace. This gives true data isolation without the operational complexity of separate database clusters at early scale.

What was the biggest performance bottleneck?

N+1 queries in the original build. A single dashboard page was firing 47 separate database queries. We restructured the data access layer with aggregation pipelines and added Redis caching, reducing that to 3 queries.

How long was the MVP phase before scaling?

We delivered a production-ready MVP in 6 weeks — covering core dashboard, authentication, and unit management. The remaining 6 months were structured 2-week sprints adding analytics, billing, integrations, and scaling work.

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Case Study · Web Development

MVP to 1,200 Users in 8 Months.

How we designed, built, and scaled a B2B SaaS property management platform from a napkin sketch to a product serving 1,200 active users.

Service

Web Development

Location

USA & India

Duration

8 months

Year

2025

Industry

B2B SaaS / PropTech

Project Overview

A PropTech startup came to us with a validated concept: a unified SaaS dashboard for property managers to track income, expenses, occupancy, and unit performance across their entire portfolio. They had a basic spreadsheet-based prototype and 12 committed early customers. They needed a production-grade platform that could launch in weeks and scale to thousands of users without a costly rebuild.

Key Insight: Optimize DB Queries First

Identifying N+1 query bottlenecks and applying Redis cache solved 90% of scaling lag. Never throw server resources at what can be fixed with index optimization and queries optimization.

The Challenge

The Problem

The client had already burned six months with a previous agency that delivered a poorly architected prototype — slow, unscalable, and full of technical debt. The real challenge wasn't just building a product; it was building one that could grow from 12 to 1,200+ users without architectural intervention.

Previous build had N+1 database queries causing 8-second load times at just 50 concurrent users

No multi-tenancy: each customer's data was not properly isolated

No CI/CD pipeline — deployments took 4 hours and frequently broke production

Zero automated testing: every release was a manual QA nightmare

No analytics — the founding team couldn't see which features users actually used

Our Solution

Our Approach

We conducted a one-week technical discovery sprint, assessed what to salvage (the core database schema), and rebuilt the application layer from scratch on a scalable, multi-tenant architecture.

Multi-Tenant Architecture

Built a database-per-tenant model using MongoDB namespacing, with shared infrastructure and isolated data boundaries — enabling both data security and operational simplicity.

Performance-First Frontend

Rebuilt the React frontend with code-splitting, lazy loading, React Query for server-state management, and Recharts for interactive analytics. Load time dropped from 8s to under 1.6s.

Scalable Backend & API

Designed a RESTful Node.js/Express API with Redis caching for hot data paths, rate limiting, and database indexing strategy — handling concurrent traffic without degradation.

CI/CD & Observability

Implemented GitHub Actions for automated testing and zero-downtime deployments to AWS. Added Datadog for APM, error tracking, and usage analytics — giving the team full production visibility.

Execution Timeline

Our Step-by-Step Process

Month 1

Tech Audit & Refactoring

Profiling N+1 database queries, isolating database namespacing, and setting up clean CI/CD.

Months 2-3

Core Frontend Rebuild

Writing React elements with lazy loading, dynamic imports, and React Query.

Months 4-5

Multi-Tenancy & Backend

MongoDB Atlas shard key design, Redis cache layer, and Stripe API integration.

Months 6-8

CI/CD & Final Scale Testing

Deploying AWS pipeline, configuring Datadog, and verifying load capacities.

Tech Stack

React 18
Vite
Node.js
Express
MongoDB
Redis
AWS EC2 & S3
GitHub Actions
Datadog
Stripe
React Query
Recharts

Results

Measurable impact — verified outcomes

1,200+

Active users at 8 months

Up from 12 at project start

1.6s

Average page load time

Down from 8 seconds on the old build

48%

Faster reporting workflows

Measured via user session data

99.9%

Uptime since launch

Zero unplanned production outages

Retrospective

Lessons Learned & Refinements

Database Namespacing

Schema isolation per tenant kept multi-tenancy robust and secure without multiple expensive database clusters.

Client-Side State

React Query dramatically cut duplicate fetch calls, optimizing dashboard loading for heavy data-views.

"The previous agency gave us technical debt we couldn't recover from. Futurise rebuilt everything properly and we went from 12 to 1,200 active users without touching the architecture. That's what we needed."

Alex K.

Co-Founder & CTO, HOMIES PropTech

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