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Design the system before a line is written.

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Build it so it actually works — in production, under load, with real users.

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AI-native platforms built by Datawise — designed to orchestrate intelligence and unlock institutional knowledge at scale.

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From $250M to $2B: Driving Transformation with AI-Powered Revenue Optimization for a global real estate company
Case Studies
Real Estate, Shared Workspaces, B2B SaaS Pricing & Revenue Optimization

From $250M to $2B: Driving Transformation with AI-Powered Revenue Optimization for a global real estate company

How our full-house engineering agency partnered with a leading global real estate company to unlock exponential growth by combining data science, machine learning, and commercial strategy.

Overview

Key Highlights

Location Recommendation Engine

Built a location recommendation engine guiding global expansion into high-yield cities and micro-markets.

Workspace Design Optimization

Optimized workspace design through predictive analytics to maximize utilization and member satisfaction.

Enterprise Pivot

Pivoted pricing model from SMB/consumer focus to enterprise clients, which grew to 40% of total revenue.

Dynamic Pricing & Discounting

Rolled out dynamic pricing & discounting engine using behavioral signals, contract duration, and demand patterns.

Account Growth Scoring

Designed an account growth scoring model to segment customers, predict demand, and calculate lifetime value.

Project Overview

Details & Results

Domain

Real Estate, Shared Workspaces, B2B SaaS Pricing & Revenue Optimization

Tech Stack

  • Backend: Java, Spring Boot, Spring Security
  • Auth & Identity: Keycloak, MyID, biometric face verification APIs
  • Data: PostgreSQL, AWS Redshift, Redis, MongoDB
  • Messaging/Eventing: SQS, RabbitMQ
  • DevOps: AWS EC2, Kubernetes, Docker,
  • Frontend: ReactJS
  • Other: SageMaker, Airflow

Problem Statement

  • Company was scaling quickly but faced structural challenges:
  • Expansion Risk: Location selection was often opportunistic, with inconsistent ROI.
  • Revenue Model Strain: Heavy reliance on freelancers and small businesses created churn and limited stability.
  • Static Pricing: Flat desk pricing ignored demand signals and contract length.
  • Customer Blind Spots: No systematic framework to identify expansion-ready accounts or project lifetime value.

Solution

  • Expansion & Space Optimization: Location Recommendation Engine: ML models on geospatial, demographic, and competitive datasets. Space Optimization: IoT sensor + booking data → predictive models for desk mix (hot desk vs. private office) → design changes tied to demand lift.
  • Pricing Transformation: Enterprise Pivot: Data dashboards showed enterprise clients had higher lifetime value and lower churn → partnered with GTM team to reframe offering. Dynamic Pricing Engine: Deployed ML models adjusting desk pricing by demand, contract duration, and behavior patterns.
  • Customer Intelligence & Retention: Account Growth Scoring Model: Tracked funding rounds, hiring signals, and utilization to predict expansion likelihood. CLV Framework: Guided retention strategy and upsell campaigns.

Outcomes

  • 8x Revenue Growth: From $250M to $2B in two years, with ML-driven revenue optimization central to scaling.
  • Expansion ROI: +30% improvement on new site performance.
  • Enterprise Success: Enterprise clients contributed 40% of total revenue by 2019.
  • Yield Optimization: Dynamic pricing added 18% lift in revenue per desk.
  • Customer Intelligence: CLV analytics drove 25% higher upsell conversions.