Implementing AI & Scalable Data Pipelines for a Leading UK Property Data Platform.

AI Development

We partnered with the in‑house data science team at one of the UK’s most widely used data platforms for estate agents—built around a large proprietary national property database—to accelerate innovation with modern ML/AI. Following a deep audit of models, data flows, and product features, we engineered numerous production data pipelines, improved development workflows, and integrated updated ML models to enrich data and unlock new prospecting and client‑nurturing capabilities for agents.

What was the main challenge in this project?

In a highly competitive estate‑agent SaaS market, the client needed to differentiate by turning its extensive but complex property datasets into actionable, AI‑driven insights—despite documentation gaps, large/heterogeneous data, confidentiality constraints (on‑site servers), and several slow, inefficient legacy ML models.

What was your solution or approach?

1. Discovery & Audit: Reviewed infrastructure, data requirements, and all existing ML models & feature dependencies. 2. Build & Integrate: Developed dozens of data pipelines powering new features; sequenced ML launches to monitor interdependencies and performance. 3. Test, Fix, Support: Post‑launch debugging, user‑driven enhancements, and ongoing collaboration in agile sprints with the client’s data science group.

What was the outcome or impact for the client?

Several dozen production data pipelines enabled expansion of the feature set (e.g., on‑market property tracking, market‑trend analytics, prospect identification). Updated ML models streamlined processing and improved the usefulness of data‑driven insights surfaced to estate agents. Enhanced workflows now support faster rollout of new AI‑powered functionality