Improving Features of AI Tagging System for Database of Construction Technologies

Project Description

Project Description

Hometeam Ventures’ mission is to close the housing gap by investing in early-stage founders bringing breakthrough technology to one of the world’s largest but least innovative industries: construction & housing. To support this, Hometeam Ventures (HTV) actively scouts potential investments, focusing on solutions that address key challenges in affordable housing. These solutions are part of a constantly growing database of ~3600 companies which allows HTV to decide on possible investments, keep track of industry trends and share potential innovations with industry partners. HTV have established an internal system to categorize and organize these solutions based on their scope of work. Assigning industry tags and attributes manually requires significant time and effort. On average, around 50 new companies are categorized weekly, a process that is vital for HTV but one that could be greatly streamlined.

Thanks to a previous collaboration with E4C, a first version of an AI-powered tool to automate this tagging process has been developed. While this initial prototype has laid a solid foundation, further development will allow it to fully meet HTV’s functional needs. The Fellow’s support will build on this progress, refining the tool’s interface, improving the logic of its categorization system, and integrating it more seamlessly with our internal workflows.

We would like to develop an AI-automated tool capable of automatically categorizing and tagging these ventures based on their scope of work and technological focus. By implementing this tool, the aim is to achieve three primary goals:

1. Increase Efficiency: Automate the manual tagging of new ventures, allowing our team to allocate more time to high-value activities such as identifying and supporting new companies in earlier growth stages.
2. Improve Accuracy: By using an AI model trained on our current database of 3,600 ventures, the tool will ensure that company tags and attributes are consistently and accurately applied.
3. Enhance Strategic Impact: Facilitating early support for emerging companies aligns with our broader goals of expanding investment and promoting visibility for innovation in the construction industry.

Impact

The impact of this project is multiple, with a core focus on elevating the visibility of early-stage startups developing transformative construction technologies. These startups often face significant challenges at the early stages, where support and awareness are crucial for their growth. By increasing visibility for these emerging technologies, we enhance the likelihood of success for early-stage founders, helping them gain traction, funding, and valuable industry connections.

The successful development of these solutions can directly boost productivity in the construction industry, a sector traditionally in need of innovative efficiencies. Additionally, as these startups grow and evolve into established companies, they contribute indirectly by creating new employment opportunities and fostering economic growth within the sector. A further impact of this project is its potential to attract greater attention to innovations in construction technology, spotlighting the value and potential of the sector and encouraging investment and support for new, impactful solutions.

Overall, this project not only supports the growth and success of startups but also aligns with Hometeam Ventures’ mission to close the housing gap and stimulate positive change across the industry.

Eligibility Criteria

Skills / Experience

• Required:
• Data Analysis + Statistics
• Programming
• Technical skills in AI – building, implementing and testing AI models
• Attention to detail, especially in developing tools that generate consistent and accurate outputs
• Ability to work independently and communicate technical progress clearly to non-technical stakeholders
• Willingness to test, iterate, and make changes based on feedback
• Desired:
• Data management and analysis
• Understanding of construction and housing technologies may be helpful

Discipline:

• Software engineering / Computer science

Preferred fellow local experience (all projects can be completed remotely):

• N/A

Time zone compatibility (when the Fellow should be available for meetings):

• CET (UTC+1)