Ben Yoskovitz

Meaningful support for early stage startups is absolutely crucial to lay the foundation for scaling later stage innovation. hashtag #feedthefunnel

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Culture doesn't scale by accident. Most founders build culture early—when everyone fits in a room, shares values, and knows each other. Then they hire fast, and suddenly the culture they built gets diluted. The companies that scale with culture intact aren't the ones that get lucky. They're the ones that intentionally codify what matters, hire for it, reinforce it, and evolve it as they grow. That's not about ping pong tables or free snacks. It's about clarity on values, consistency in how you treat people, and leadership that models what you say you believe. Culture is your competitive advantage. But only if you treat it like the strategic priority it is.

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Great press for Mercator.ai in GlobeSt.com! "Canadian startup Mercator.ai is developing an artificial intelligence platform that aims to forecast future construction activity, giving contractors and eventually investors and developers a way to anticipate shifting demand in commercial real estate markets. The platform is designed to help general contractors identify upcoming projects long before a builder is even attached. If successful, the same technology could eventually guide developers, investors, and other real estate players in anticipating what types of properties will be needed and where. The company was launched in Canada and worked with some of the country’s largest contractors, but its leadership quickly found that the local market posed significant hurdles. “The Canadian markets were actually quite challenging for us to go and build,” CEO Chloe Smith told GlobeSt.com. “There's a lot of different regulations, and it’s very, very expensive to access data sets.” Rather than starting in its home market, Mercator chose to focus on Texas, where accessing the right information was comparatively simpler. That access is critical because the platform relies on both clear and nuanced signs of development activity. Rezoning applications, building and trade permits, and other scattered data points are fused together to form a broader picture of what projects are about to emerge. The challenge lies in turning fragmented and largely unstructured information into something coherent. Mercator uses large language models to interpret documents, such as zoning files or permit applications, and machine learning tools to categorize the data so it can be analyzed. “Sometimes we bring in PDFs, so we actually have to go and process all those PDFs ourselves and pull out the relevant fields and create all of that schema and metadata,” Smith explained. Creating that schema provides the backbone of a structured database, while metadata helps define exactly what each field or entry represents. The immediate goal is to give general contractors an advanced prospecting tool, enabling them to identify early-stage projects before competitors. But Mercator’s ambitions stretch beyond construction leads. “The evolution of our product and where we're headed is really in market analysis,” Smith said. “Where are we growing? How are cities changing? How can we help folks forecast where they need to be? Because a lot of times the reason why the construction industry sees losses in terms of companies shutting down is because they're not able to get ahead of the curve fast enough.” If the platform works as intended, contractors could gain insight into shifts in demand—for instance, if multifamily projects slow down while demand grows for warehouses, medical office buildings, or data centers. Investors, too, could use the same intelligence to target the right land, choose the best partners, and position themselves in high-growth areas before competitors catch on."

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