Heimstaden Uses OpenAI Tools for Letting and Property Management

Nov 3, 2026
Heimstaden is using OpenAI tools for property listings, environmental risk analysis, procurement and internal workflows.

Heimstaden is using tools from OpenAI across property letting, environmental analysis and procurement, as detailed in an OpenAI blog post. The company selected OpenAI in June 2025 and launched ChatGPT Enterprise that September.

AI now supports rental pricing, virtual staging, apartment descriptions, listing reviews and market feedback. In the Netherlands, Heimstaden says the cost of producing a virtually staged property image fell from about €50 to less than €0.10, with estimated annual savings of €130,000. Employees remain responsible for reviewing content and approving decisions.

An environmental sustainability analyst also used Codex to create data processing workflows for assessing 20,112 assets. The project generated more than six million rows of usable data and reduced the estimated staffing requirement from five people working for a year to two people working for two months.

Half of Heimstaden employees were using ChatGPT Enterprise within two to three months of its launch. The company is also rolling out a procurement platform that uses OpenAI models across Outlook, SharePoint and Teams for contract analysis, negotiation preparation and contract management.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like Enterprise AI Brief or Daily AI Brief.

Also, consider following us on social media:

Enterprise AI AI Brief AI Brief (X)
Free newsletter

Enterprise AI Brief

Weekly report on AI business applications, enterprise software releases, automation tools, and industry implementations.

Guide
9 Voice to Text Apps

9 Voice to Text Apps

The best AI dictation apps for turning speech into clean text anywhere you type.

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more

You may also like

Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.