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Cascade raises $3.5M seed backed by a16z to surface construction project opportunities before they reach formal tender

A London startup building an AI platform that aggregates pre-bid signals — bond filings, building permits, property transactions, capital plan line items — to help architecture, engineering, and construction firms identify project opportunities before formal RFPs are issued.

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Cascade (London) has raised $3.5 million (approximately €3.24 million) in a seed round led by Andreessen Horowitz Speedrun (a16z's accelerator programme). Ada Ventures, Blitzscaling Ventures, Indico Capital, shuckerVC, G2C Ventures, and Snowball VC participate in the syndicate. The round was announced 21 July 2026.

The company was founded by Hannia Zia (CEO, former Google) and Joana Ferreira (CTO, former Amazon). Cascade builds an AI platform for the architecture, engineering, and construction (AEC) sector that aggregates pre-bid intelligence: bond filings, building permit applications, property transactions, and public capital plan line items. The platform surfaces these signals — typically weeks or months before a formal request for proposals (RFP) is issued — allowing AEC firms to position their business development before the market knows a project exists.

Early clients reportedly include AEC firms working on projects at JFK Airport, LaGuardia Airport, and on nuclear facility and data centre programmes — a client profile that suggests the platform is being stress-tested in high-value, complex procurement environments rather than commodity construction.

The intelligence gap in construction business development

Construction business development has a structural timing problem. Winning major work is relationship- and positioning-dependent, but even the best-maintained relationships do not surface a project that the market has not yet publicly announced. Most AEC firms encounter formal project information at the RFP stage — by which point the scope is set, the leading relationships are established, and late entrants face an uphill contest.

Cascade's argument is that public record sources contain reliable leading indicators of construction demand that current BD teams either miss or process too slowly. Bond filings at state and county level record capital allocation decisions. Building permit applications reveal project scope before contractor engagement. Property transactions signal development pipeline. The platform aggregates these sources, scores them for relevance to a given firm's capability set, and surfaces prioritised leads.

The practical advantage is timing: a firm that has been tracking the signals and building a relationship since the project's capital allocation stage enters the formal procurement process from a fundamentally different position than one that reads about the RFP on a construction news feed.

a16z Speedrun and the AI cycle caveat

a16z Speedrun's involvement carries a standard early-stage caveat: it validates market interest at a moment when pre-bid intelligence tools have attracted significant investor attention broadly. The more durable signal will come from whether Cascade's clients demonstrate win-rate improvements that genuinely exceed what their existing relationship infrastructure would have generated — and whether those improvements persist across market cycles and geographies. Whether Cascade can move from US infrastructure projects (the stated client profile) into European construction markets — where procurement processes, public-record availability, and regulatory transparency differ significantly — will also determine the company's addressable market ceiling.

Sources

  1. 01AI engineering project predictor startup Cascade wins backing from a16z accelerator — Tech.eu
  2. 02Cascade raises $3.5M to help construction firms predict the future and win more projects — Business Partner Magazine
  3. 03Ex-Google, Amazon duo land $3.5M to help construction firms spot deals before they hit the market — Tech Funding News
  4. 04How Cascade Plans To Help Constructors Win New Business Worth Millions — Forbes

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