Feasibly, an AI-powered real estate analysis platform developed by market and financial feasibility veterans, has launched to modernize how developers, investors and lenders evaluate the viability of commercial development projects.
Feasibly was co-founded by Brian Connolly, Eric Habermas and Walter Franco, long-time feasibility industry collaborators with expertise across market, financial and strategic analysis. Blending AI speed with human expertise, Feasibly’s patent-pending software delivers bank-ready market feasibility studies in days instead of months.
“Feasibly’s founders spent decades conducting feasibility studies for complex development projects,” said Connolly, founder and CEO. “Based on that experience, we built Feasibly with proven analytical methods, proprietary data and AI automation to make professional-grade feasibility analysis accessible to more builders, funders and planners.”
At launch, Feasibly supports six project types: multifamily, retail, hotel, office, sports, entertainment and mixed-use development. Future sectors will include single-family residential, student housing, medical, storage and more.
Feasibly’s multi-agent AI uses specialized large language models to automate market analysis, financial modeling and reporting. Dedicated AI agents handle data from retrieval to narrative synthesis, working alongside Feasibly’s human analysts. This speeds up production of bank-ready studies, and every report is expertly reviewed for accuracy and reporting best practices.
Feasibly delivers comprehensive analysis including demographics and socioeconomics, comparable development trends, competitive benchmarking, market demand and cash-flow projections – ready for internal or external use.
Feasibly provides premium, bank-ready feasibility reports starting at $10,000, with an average three-day turnaround, significantly faster than traditional consulting firms. Backed by $1 million in pre-seed funding, Feasibly is engineered for scale, continuous learning and rapid expansion into new asset types; with long-term differentiation rooted in deep domain expertise, proprietary datasets and human-in-the-loop oversight.
