A startup aiming to transform how construction firms find and win projects has raised $3.5 million in seed funding. Cascade, founded in 2025 by Hannia Zia and Joana Ferreira, has secured investment from Andreessen Horowitz Speedrun, Ada Ventures, and Snowball VC. The platform uses artificial intelligence to help architecture, engineering, and construction companies identify opportunities that match their expertise.
The founders say the current process of finding construction work is fragmented, with firms forced to check multiple portals across different states, cities, and federal agencies in the US. Cascade consolidates this data, using AI to analyse past tender results and predict which developers are likely to win upcoming contracts. The system also tracks signals such as new grant announcements to alert customers about potential leads.
Ferreira explained that when a customer wins a bid, the platform learns from the outcome, improving its predictions over time. “Every time a customer wins a bid, they give feedback, so the system keeps getting smarter,” she said. The startup has already signed contracts with firms involved in major projects including JFK and La Guardia airports, as well as Four Seasons hotels.
For UK readers, the technology raises interesting possibilities. While Cascade currently focuses on the US market, similar inefficiencies exist in the UK construction sector, where small and medium-sized firms often struggle to access a centralised view of public and private contracts. The UK’s Information Commissioner’s Office (ICO) has yet to issue specific guidance on AI-driven procurement tools, but the EU AI Act could influence future regulation if the platform expands to Europe. UK businesses may benefit from such platforms as they increasingly adopt AI to streamline operations and reduce costs.
Experts note that AI-native tools like Cascade could help level the playing field for smaller construction firms, which often lack the resources to track opportunities manually. However, concerns remain about data accuracy, bias in predictive models, and the potential for over-reliance on automated recommendations. The startup plans to use the fresh capital to go to market, host industry events, and hire more engineers, with a long-term goal of building a comprehensive map of the construction industry that its AI can navigate to find the best leads for each customer.