Artificial intelligence applied to capturing business opportunities is reshaping sectors that until now seemed impervious to digital transformation. The latest example comes from New York: Cascade, a startup founded by two entrepreneurs with backgrounds at Google and Amazon, has closed a $3.5 million seed round led by Andreessen Horowitz's Speedrun program (a16z). Cascade's proposition is as simple as it is ambitious: detect construction projects before they become public by analyzing administrative documents, bond filings, land swaps, city council minutes, and other bureaucratic traces that precede a formal tender. In a market where U.S. construction spending has plateaued at around $2.1 trillion annually, the ability to get ahead of the competition becomes the only real lever for growth.
Cascade's approach resembles that of quantitative hedge funds: treating construction demand as a phenomenon legible in dispersed data before it becomes consensus. The platform, called Pursuit Hub, performs three distinct tasks. First, detection: continuously ingesting information from public and semi-public sources such as permit applications, corporate earnings reports, municipal budgets, and meeting notes. Second, prediction and fit scoring: assigning a score to each signal to estimate which firm is best positioned to win the project, using a model that learns from historical outcomes. Third, the relationship graph: mining contacts stored in email and other company tools to surface warm paths to project decision-makers. This last layer is perhaps the most valuable in a sector where 50% to 74% of revenue comes from existing relationships, according to QorusDocs data.
The macroeconomic context reinforces the investment thesis. U.S. construction has been stuck in a plateau between $2.1 and $2.2 trillion for over two years (per the Census Bureau's latest C30 release), with 2025 output projected at $2,164.4 billion, down 1.4% from 2024. In a flat market, growth can only come from taking market share. Globally, Oxford Economics forecasts construction volume rising from $9.7 trillion in 2022 to $13.9 trillion by 2037, driven by the U.S., China, and India. Whoever sees those projects first will have a decisive advantage.
Cascade faces considerable challenges, however. Prediction precision must hold as it scales: business development teams forgive a false negative before they lose a week chasing a phantom project. Moreover, the data moat must go beyond mere ingestion of public records, because that data is available to anyone. The sustainable advantage lies in outcome data: which firm won each project, which signals were decisive, which partner combinations worked. This creates a network effect where each new use case improves the model for everyone.
Confidentiality is another delicate hurdle. Firms that feed their market intelligence into the system are simultaneously sharing a platform that their competitors may use. Cascade must prove it can isolate each client's data while extracting aggregated patterns that benefit the whole. The $3.5 million round, though adequate for a seed, forces prioritization: the next raise will depend on net retention metrics and real improvements in win rates.
The founders' background is a differentiator. Hannia Zia and Joana Ferreira met at UnlikelyAI, a neuro-symbolic AI startup backed by Amadeus Capital Partners and Octopus Ventures, where they built knowledge graphs and trained LLM agents to navigate real-world data. That experience is directly applicable to parsing bureaucratic fragments. In addition, both come from construction families: Ferreira grew up in a Portuguese town where carpentry and masonry were economic pillars; Zia witnessed the failure of a family construction business firsthand. This biographical proximity reduces the typical risk of tech founders who underestimate the importance of personal relationships in the AEC (Architecture, Engineering, and Construction) sector.
The opportunity Cascade addresses is not exclusive to construction. In any industry where decision cycles are long and public information is fragmented, predictive analysis of early signals can make a difference. This is where companies like Q2BSTUDIO, specializing in custom software development, offer complementary solutions. For example, artificial intelligence capabilities and autonomous agents can be integrated into CRM or ERP systems to alert on latent opportunities. The same logic of ingesting public data can be applied to detecting regulatory changes, new public tenders, or intellectual property movements that foreshadow investments. Combining cloud computing (AWS, Azure) with Business Intelligence and Power BI enables dashboards that monitor those signals in real time. And all of this requires a solid cybersecurity foundation to protect the proprietary data each company introduces into the system.
a16z's Speedrun program, which began as a gaming accelerator and now accepts less than 1% of applications, bets that vertical AI in construction is a speed game: the first platform to accumulate proprietary pursuit-outcome data will have an advantage latecomers cannot buy. The co-investors in the round reinforce that thesis: Ada Ventures backs team quality, Blitzscaling Ventures focuses on network effects, and Indico Capital points to transatlantic expansion leveraging Ferreira's Portuguese roots.
What needs to happen for Cascade to succeed? First, prediction precision must survive scaling. Second, the proprietary data moat must outrun the replicability of open sources. Third, the network flywheel must activate without breaching client confidentiality. Fourth, the startup needs to show within 18 months (by early 2028) a measurable improvement in win rates for a cohort of clients, and at least one reference project won from a signal detected more than six months before the RFP. If it hits those milestones, Cascade will become a pricing-power tool in the least digitized corner of a $2 trillion market.
Meanwhile, the lesson for other industries is clear: the data that precedes a formal decision is out there, scattered in minutes, registries, and communications. Knowing how to read it in time is not magic, but well-executed data engineering. And in that field, the ecosystem of technology companies, from startups to established firms like Q2BSTUDIO, is building the foundations of a new competitive advantage based on early information.
