AI recruiting and retention platform for real estate brokerages
Courted targets a high-pain segment of the residential real estate industry: talent management at brokerages. The tech stack reveals a data-forward architecture—Databricks, Spark, PostgreSQL, Python—built to handle predictive analytics and generative AI features across agent recruiting and retention workflows. Pain-point data shows the core problems are scaling ingestion pipelines and integration across fragmented toolsets, which aligns with their active work to ingest new datasets and correlate disparate data sources. The balanced hiring mix across engineering, data, design, product, and sales suggests they're in early-stage growth mode, building both product depth and GTM motion simultaneously.
Courted is an AI-powered platform that helps residential real estate brokerages improve agent recruiting and retention through predictive analytics and generative AI. Founded in 2021 and based in New York, the company targets mid-market and enterprise brokerages with a software product that integrates into existing workflows. The platform delivers actionable intelligence on agent hiring and churn decisions, with reported ROI realized in 2–4 weeks. Current product development spans agent search, market intelligence, broker back-office operations, commission management, and notifications. The team of 11–50 employees is actively hiring across engineering, data, product, design, and sales in the United States and Canada.
Courted's core stack includes Python, PySpark, SQL, Apache Spark, and Databricks for data processing; PostgreSQL for storage; Django for backend; Docker and CircleCI for deployment; and Figma and Jira for design and project management.
Current projects include agent search, market intelligence, broker back-office tools, agent onboarding, commission management, notifications, and new data ingestion pipelines to expand predictive capabilities.
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