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Innowise is an international full-cycle software development company founded in 2007. We are a team of 1600+ IT professionals developing software for other professionals worldwide.
About us
Innowise is an international full-cycle software development company founded in 2007. We are a team of 1600+ IT professionals developing software for other professionals worldwide.

Real estate online platform: x14 faster data loading and processing

Innowise’s developers have revamped the digital real estate platform to enhance its efficiency, user-friendliness, and overall performance.


Real estate
The United Kingdom
Client since

Our customer is a leading company offering innovative software solutions to the real estate industry. With a deep understanding of the evolving needs and challenges real estate professionals face, they provide a comprehensive suite of technology-driven tools designed to enhance efficiency, productivity, and overall success in the dynamic world of real estate.

Detailed information about the client cannot be disclosed under the provisions of the NDA.


Elevating the prowess of the digital real estate platform

Our company has been approached by a customer who owns a comprehensive database of UK properties. Due to outdated libraries and frameworks, the current platform suffered from severe speed issues. In addition, the system’s reliance on on-premise servers made it inefficient from a cost perspective. Furthermore, the client wanted to integrate AI-based software to streamline processes, save time, and reduce costs by automating manual tasks.


Revamping real estate platform for faster client-property matching

Innowise has redesigned a real estate online platform that assists real estate agencies in finding properties to clients based on their specific requirements, ensuring the process is completed quickly.

Slow load performance improvement

When the client approached us, the platform operated on outdated PHP code. It was evident that this older technology was becoming a bottleneck, hindering the platform’s potential growth and efficiency. To address this, and after a comprehensive assessment, we decided to migrate the entire system to Python.

The transition from PHP to Python has brought a noticeable boost in the platform’s speed and overall performance.  As a result, users can now enjoy faster property searches, quicker data retrieval, and smoother interactions with the platform.  

Our team of experts meticulously tested and verified every platform aspect throughout the migration process to ensure its stability and reliability. We understand the importance of a seamless transition and have taken every measure to minimize disruptions.

Data pipelines

We have successfully developed cutting-edge data pipelines designed to handle vast volumes of data. The backbone of this platform is an interconnected suite of AWS services that facilitate the efficient flow and processing of real estate data.

Core pipeline components:

  • AWS Airflow (MWAA): as the orchestrator, MWAA ensures that real estate listings, transaction records, and market data are systematically ingested, processed, and made available, creating a smooth pipeline from source to application.
  • Amazon S3: the foundational storage layer, S3 houses vast repositories of property images, transaction histories, and relevant real estate documents. Its durability ensures data integrity and availability.
  • Amazon Cloud Watch: in a market where every second counts, Cloud Watch monitors the health and performance of our pipelines. It ensures that data flows without interruption, making real-time property insights consistently accessible.
  • AWS Lambda: a game-changer in event-driven processing. Whether it’s updating a property listing or reflecting a recent sale, Lambda processes such inputs immediately, ensuring the platform remains current.
  • Amazon API Gateway: an essential bridge to external systems. Through this gateway, third-party real estate data sources can seamlessly integrate with the platform, enhancing the depth and breadth of property insights.
  • AWS Glue & AWS Glue Catalog: after raw real estate data is ingested into S3, AWS Glue takes over the transformation process, turning raw data into actionable insights. The Glue Catalog organizes this processed data, making it easily queryable and enhancing its utility across the platform.
  • Amazon Athena: real estate professionals often need to make quick, data-driven decisions. Athena provides them the ability to make ad-hoc SQL queries directly on S3 data, delivering immediate insights.
  • Amazon DocumentDB: the complexity of the real estate market is mirrored in its data. Document DB provides a space for unstructured data like client feedback, tenant histories, and more, adding a richer dimension to property insights.
  • Amazon QuickSight: data is only as good as its presentation. QuickSight transforms intricate real estate data analytics into visually compelling dashboards and reports, aiding stakeholders in making informed decisions.
  • Amazon Redshift: as the data powerhouse, Redshift conducts deep dives into property trends, buyer behaviors, and market forecasts. Integrated with the Glue Catalog, it offers unparalleled analytics, shaping the strategic direction of real estate businesses.

By seamlessly blending these AWS components, Innowise has not just created a data pipeline; we’ve laid the foundation for the future of online real estate. With data-driven insights and analytics at its core, this platform promises to revolutionize the way real estate businesses operate online.

AI integration

Innowise has integrated AI into the platform aiding users in accurate property valuations. With AI’s advanced capabilities, the platform can process and analyze vast amounts of real estate data, ranging from historical property prices to demographic information.

Furthermore, AI-powered predictive models enable users to anticipate property market trends and fluctuations. Armed with this knowledge, individuals can make well-informed choices regarding  investments and pricing strategies.

Another significant advantage of AI integration is its ability to analyze user feedback and sentiments. This helps real estate professionals understand customer preferences and areas that require improvement, ultimately leading to better service offerings and improved customer satisfaction.

Moreover, sellers benefit from AI’s prowess as it suggests optimal pricing strategies based on property characteristics, location, and market demand. It increases the likelihood of a successful and lucrative sale.

Migrating the platform from server to AWS

Our company successfully migrated a real estate platform from an on-premise server to the cloud-based system AWS which resulted in cost efficiency. The pay-as-you-go approach of AWS means businesses only pay for what they use. This eliminates hefty initial infrastructure costs, allowing companies to tap into extensive computational power and storage without buying or maintaining costly hardware.

Previously, the client often overprovisioned the resources to accommodate peak loads, leading to underutilization during off-peak periods, while AWS Auto Scaling automatically adjusts the number of instances running based on specified conditions. This means that during periods of high user traffic, the number of server instances can automatically increase to handle the load. Conversely, instances can be scaled down during low-traffic periods, optimizing costs and resource utilization.







Python, Scala

Cloud Platforms


Data engineering

Apache Airflow, Apache Spark, Airbyte, DBT

Machine Learning/Data Science

Apache Spark MLLib, Scikit-learn, PySpark, Numpy, Pandas, Scipy


Docker, Terraform, Jenkins

Version control systems



Innowise began by pinpointing the pain points and bottlenecks in the current platform, documenting the insights in Notion. We then set distinct objectives for the optimization process, aiming at reducing loading times, enhancing search functionality, and amplifying user-friendliness.

Using the Kanban methodology, and tracking tasks in Jira, we held weekly meetings to review the backlog, reorder tasks, and strategize for the week ahead. Throughout the development phase, our team conducted daily standup meetings on Slack, providing updates on completed tasks, forthcoming activities, and any potential hindrances. This ensured team alignment and enabled prompt issue resolution.



Project Manager




Data Engineers


 Data Scientists


Boosted efficiency: 3x cost savings, 14x faster data loading for superior user experience

We’ve overhauled the client’s real estate platform, enhancing speed and user experience. By moving from PHP to Python, we’ve achieved faster response times and a more user-friendly interface. A key upgrade is new data pipelines, built specifically to manage large data volumes efficiently. 

AI integration now delivers tailored property suggestions to users and automates several standard tasks, making the user journey smoother. Users also benefit from instant property alerts, keeping them updated on the latest market changes. 

Leveraging Amazon Web Services reduces operational costs and ensures platform robustness during high-traffic periods.

These combined improvements have elevated the real estate online platform to new heights, making it a more efficient, user-friendly, and cutting-edge solution for all our valued users.

Project duration
  • January 2022 - Ongoing


more cost-efficient


faster data loading and processing

Related cases

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