Technologies employed included
Our client provides a suite of prospecting, customer management, and nurturing tools to estate agents. Its platform is built around an extensive, proprietary database of UK properties and is trusted by many well-known brands, including Knight Frank and Carter Jonas. We partnered with our client’s in-house data science team to build new data pipelines, enhance development workflows, and implement machine learning (ML) models. The result was an expanded, powerful new feature set for end users.
Developed dozens of data pipelines, streamlining data processing and allowing our client to expand and update its feature set.
Integrated advanced machine learning (ML) and AI functionality to enhance the depth and utility of data insights for end users.
Audited and enhanced existing ML models that were slow and inefficient and established long-term workflows for faster feature rollouts in the future.
A Leading Data Platform for Estate Agents
Medium enterprise (more than 250 employees)
United Kingdom
Our client provides one of the UK’s most powerful and innovative data platforms for estate agents. Platform functionality falls into two main areas: prospecting and client nurturing.
A vast property database sits at the heart of the company’s value proposition. Among other applications, agents use this database, available through the “Pro” plan, to find properties about to come onto the market, track existing properties available for sale or rent, and enrich information about properties already on their books.
Our client is driven by a clear, inclusive mission: “Everything we do here is about trying to galvanise and improve the property industry; to open it up and reveal the market’s true potential, for everyone.”
Our client’s platform is one of the most powerful on the market. Its competitors simply can’t match the depth and quality of its data — much of it drawn from proprietary sources — and wide feature set.
However, the estate agent SaaS sector is competitive. Differentiation isn’t merely optional, and continuous innovation is a must. As such, our client wanted to implement advanced, up-to-date machine learning (ML) technology to enrich data, enhance insights, and power new customer features.
One data engineer and one data scientist were assigned to the project. They supported and guided the in-house data science team, particularly in improving and implementing existing ML models.
Because certain datasets were confidential, we used our client’s on-site servers rather than cloud solutions.
Research and analysis of requirements: Sigli conducted a thorough review of current infrastructure, including an audit of existing ML models, output and input data requirements, data pipelines, and platform features.
Data pipeline development and ML integrations: We developed multiple data pipelines, which would act as the engines behind new product features. We also launched and integrated ML models in specific sequences so as to monitor for interdependencies that could affect performance.
Bug fixing and support: After launch, we conducted thorough testing and debugging and responded to user requests.
The majority of communication was via regular virtual meetings, with text and email as “always on” channels between Sigli and our client.
Project management centred on standard routines like daily meetings, sprint planning and retrospectives. These ensured clear communication between Sigli engineers and the data science team.
In partnership with the internal data science team, Sigli delivered an array of infrastructure upgrades. These included new data pipelines, advanced ML functionality, and improved workflows for quickly releasing new features.
Developed several dozen unique data pipelines which allowed our client to expand its feature set, including on-market property tracking, market trend analysis, which identifies prospects thinking about switching to a new estate agent.
Worked with the data science team to implement up-to-date ML models that streamlined data pipelines and expanded the platform feature set.
Audited and assisted with the development of existing ML models that operated slowly and hindered existing workflows.
Overcame multiple challenges, including a lack of documentation, large and complex data sets, and the usual difficulties of transitioning to a new tech stack.
Sigli was instrumental in upgrading our cutting-edge solution and future-proofing it with the very latest machine learning and data analysis tech.
Are you eager to see the benefits of AI and ML in your own company? Get in touch today to learn how Sigli’s developers and AI specialists can enhance and upgrade your tech infrastructure.
Managing Director, Benelux
Chief Business Development Officer
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