Case Study
Rentana
Full-stack platform engineering for AI-powered revenue management
Rentana needed a team that could work across their entire RevOps platform: revamping the sync engine that pushes optimized prices to property management systems, improving alert data pipelines, building competitor scraping infrastructure, and enhancing both internal and user-facing UIs.
Engagement
Dedicated
Engineering-focused engagement
Stack
React, Node.js, SQL, GraphQL
Plus AWS Lambda, Playwright, Yardi, Entrata, and Resman
The Challenge
A RevOps platform with growing infrastructure demands
Rentana provides AI-powered revenue intelligence for multifamily real estate, helping property managers optimize rent pricing, manage renewals, and track competitor rates across their portfolios. The platform interconnects with several property management systems and relies on data pipelines, scraping infrastructure, and alert systems to deliver actionable insights.
As the customer base grew, the platform needed work across multiple layers: the sync engine that pushes prices to PMS targets had become fragile, the alert pipeline needed improvements, competitor scraping tools required ongoing maintenance, database systems needed to be redesigned for scale, and both internal and user-facing UIs needed enhancement. Rentana needed a full-stack team that could move across all of these areas.
Key Requirements
What they needed
- 01
Reliable price syncing across Yardi, Entrata, and Resman
- 02
Improved alert generation pipeline and notification system
- 03
Database systems redesigned for scale with existing SQL and GraphQL API
- 04
Maintained and enhanced competitor price scraping tools
What We Built
Platform infrastructure across the full stack
PMS Sync Engine
Revamped the core sync engine that pushes AI-optimized rental prices to property management systems including Yardi, Entrata, and Resman. Built a clean adapter layer per PMS with structured retry policies, rate limit handling, and full traceability for every sync operation.
Alert Data Pipeline
Improved the alert generation pipeline that surfaces pricing anomalies and market signals to property managers. Enhanced email notification design and functionality so alerts are actionable and reach users with the right context at the right time.
Competitor Price Scraping
Maintained and improved web scraping tools built with Playwright that collect competitor pricing data across rental markets. Built better UI for displaying scraped data and added data validation layers to ensure accuracy across sources.
Database Design and GraphQL API
Redesigned database systems within the existing SQL database and GraphQL API to perform at scale. Optimized queries, restructured schemas, and improved data access patterns to support the growing volume of properties, pricing records, and sync operations across the platform.
How We Worked
Engagement structure
Full-stack embedded engineering
Our team worked across backend services, AWS Lambda functions, React front-ends, and scraping infrastructure. We operated as an extension of Rentana's engineering team, picking up work across the entire stack as priorities shifted.
Parallel workstreams
Sync engine improvements, alert pipeline work, scraping maintenance, and UI enhancements moved forward simultaneously. We balanced infrastructure reliability with user-facing feature delivery so progress was visible across the platform.
Data accuracy focus
Across scraping, sync operations, and alert generation, we prioritized data validation and accuracy. Every pipeline was instrumented to catch inconsistencies before they reached users or downstream systems.
Long-term partnership
The engagement was structured for sustained collaboration with growth potential. We built systems and patterns that the Rentana team could extend independently while continuing to contribute across the platform.
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