Feasibly
AI platform for bank-ready real estate feasibility studies
Website: https://www.planfeasibly.com/
Cover Block
PUBLIC
| Attribute | Value |
|---|---|
| Name | Feasibly |
| Tagline | AI platform for bank-ready real estate feasibility studies |
| Headquarters | Park City, Utah, United States |
| Founded | 2024 |
| Stage | Pre-Seed |
| Business Model | SaaS |
| Industry | Proptech |
| Technology | AI / Machine Learning |
| Geography | North America |
| Growth Profile | Venture Scale |
| Founding Team | Co-Founders (2) |
| Funding Label | Pre-seed |
| Total Disclosed | ~$1,000,000 |
Links
PUBLIC
- Website: https://www.planfeasibly.com
- LinkedIn: https://www.linkedin.com/company/feasibly
Executive Summary
PUBLIC Feasibly is an early-stage attempt to automate a costly, time-intensive corner of commercial real estate analysis, using a multi-agent AI system to generate bank-ready feasibility studies in days rather than months [Brief Glance, December 2025]. The company launched publicly in December 2025 with a $1 million pre-seed round, positioning its software as a wedge into a market traditionally dominated by high-priced consulting firms [Business Wire, December 2025]. Its product is described as combining AI-driven data synthesis with expert human oversight to produce reports covering demographics, competitive benchmarks, and financial projections, with pricing starting at $10,000 per study [Feasibly website, December 2025].
Founder and CEO Brian Connolly is presented as a veteran of market and financial feasibility work, though the public record offers limited detail on his specific project history or the identity of his co-founders [Brief Glance, December 2025]. The business model is a straightforward SaaS and services hybrid, targeting developers, lenders, and investors who need rapid analysis to support capital commitments. Over the next 12-18 months, the critical watchpoints will be the emergence of named customer logos, validation of the AI system's accuracy in a regulated lending environment, and the company's ability to scale beyond one-off reports into a recurring revenue stream.
Data Accuracy: YELLOW -- Key claims sourced from launch coverage and company website; team and traction details lack independent corroboration.
Taxonomy Snapshot
| Axis | Classification |
|---|---|
| Stage | Pre-Seed |
| Business Model | SaaS |
| Industry / Vertical | Proptech |
| Technology Type | AI / Machine Learning |
| Geography | North America |
| Growth Profile | Venture Scale |
| Founding Team | Co-Founders (2) |
Company Overview
PUBLIC
Feasibly was founded in 2024, according to PitchBook records, and emerged publicly in December 2025 with a $1 million pre-seed round and the launch of its AI platform for real estate feasibility [PitchBook] [Brief Glance, December 2025]. The company is headquartered in Park City, Utah, a detail confirmed in its official launch announcement [Business Wire, December 2025]. The founding narrative centers on a team of veterans with decades in market and financial analysis, aiming to modernize a process they describe as traditionally slow and consultant-dependent [Brief Glance, December 2025].
Key milestones are limited to this launch period. The company's public debut was marked by a press release distributed via Business Wire and syndicated to financial news outlets, which disclosed the funding and the core product promise of delivering bank-ready studies in days [Business Wire, December 2025] [Yahoo Finance, December 2025]. No earlier beta programs, pilot customers, or prior funding events are documented in public sources.
Data Accuracy: YELLOW -- Company founding and headquarters corroborated by PitchBook and press release; funding round and launch date reported by a single outlet.
Product and Technology
MIXED Feasibly’s product is a software platform that aims to automate the generation of commercial real estate feasibility studies, a process the company says traditionally takes months of consultant work. The core claim is delivering a “bank-ready” report covering demographics, competitive benchmarks, financial modeling, and cash-flow projections within three to five days [Feasibly how-it-works, 2026]. The company positions this speed as its primary wedge, with pricing starting at $10,000 for a single study [Feasibly website, December 2025].
The technical approach is described as a combination of “multi-agent artificial intelligence with expert human oversight” [Brief Glance, December 2025]. This suggests a system where specialized AI agents handle distinct analytical tasks, with human experts performing quality assurance on the final output. The architecture is noted as patent-pending, though no technical white paper or detailed system diagram is publicly available. The platform’s reliance on “trusted data sources” is mentioned, but the specific data providers or integration partners are not named [Crunchbase].
- Report composition. Outputs are synthesized into a single narrative report intended for stakeholders like developers, lenders, and investors [Feasibly website, December 2025].
- Service model. The company offers tiered plans including one-time studies, multi-packs, and subscriptions, indicating a move toward a recurring SaaS model beyond project-based consulting [Brief Glance, December 2025].
- Human-in-the-loop. The integration of expert oversight is a critical, publicly stated component for ensuring the accuracy required in financial decision-making [Business Wire, December 2025].
Data Accuracy: YELLOW -- Product claims are sourced from company materials and one press article; technical architecture and data source details are not independently verified.
Market Research
PUBLIC The market for rapid, data-driven feasibility analysis is expanding as capital deployment in commercial real estate grows more selective, forcing developers and lenders to validate project economics earlier and with greater precision.
No third-party TAM, SAM, or SOM figures for AI-driven real estate feasibility studies are cited in the available sources. The company's launch materials position the service as an alternative to traditional consulting, a market whose size can be approximated by adjacent sectors. For context, the global management consulting market was valued at approximately $340 billion in 2024, with real estate consulting representing a significant segment [Statista, 2024]. A more direct analog is the broader proptech analytics and valuation software market, which PitchBook reported reached $12.4 billion in 2023 and is projected to grow at a 12.1% CAGR through 2028 [PitchBook, 2024]. Feasibly's $10,000 starting price point suggests it is targeting the high-value, project-specific segment of this market, where individual consultant-led studies can cost $50,000 to $200,000 and take months to complete [Brief Glance, December 2025].
Demand is driven by several converging factors. Rising interest rates and tighter credit conditions since 2022 have increased lender scrutiny, requiring more robust, bank-ready documentation before financing commitments [Federal Reserve, 2024]. Simultaneously, the commercial real estate development cycle has compressed, with competitive land acquisition and entitlement processes creating pressure for faster preliminary analysis. The proliferation of granular, often disparate, real estate datasets (demographic, traffic, zoning, sales comps) has also created an operational bottleneck that manual analysis struggles to overcome efficiently. These conditions create a tailwind for any platform promising to synthesize complex data into a credible narrative report within days rather than quarters.
Key adjacent and substitute markets illustrate both the opportunity and the competitive context. The primary substitute remains the incumbent manual consulting model, dominated by large firms like CBRE, JLL, and Cushman & Wakefield, as well as specialized boutique shops. Adjacent markets include broader proptech data platforms (CoStar, Reonomy) and financial modeling software (Argus, RealPage), though these tools typically provide raw data or modeling environments rather than finished, narrative-driven feasibility studies. The regulatory landscape presents a moderate force; while feasibility studies for bank financing are not directly regulated, their acceptance hinges on adherence to appraisal standards (USPAP) and lender-specific underwriting criteria, creating a high bar for perceived accuracy and methodological rigor.
| Metric | Value |
|---|---|
| Management Consulting (2024) | 340 $B |
| Proptech Analytics Software (2023) | 12.4 $B |
| Projected Growth Rate (2023-2028) | 12.1 % CAGR |
The sizing context, while not specific to Feasibly's niche, frames the addressable wedge. The company is not attempting to displace the entire consulting market but to capture a portion of the high-friction, high-cost project analysis segment within it. The growth trajectory of the underlying proptech analytics category suggests institutional willingness to adopt software solutions for core underwriting workflows.
Data Accuracy: YELLOW -- Market sizing relies on analogous sector reports; specific TAM for AI feasibility studies is not publicly available.
Competitive Landscape
MIXED Feasibly enters a market where the primary alternatives are not other AI startups but established human-led consulting firms, a positioning that defines both its immediate opportunity and its long-term challenge.
| Company | Positioning | Stage / Funding | Notable Differentiator | Source |
|---|---|---|---|---|
| Feasibly | AI platform for bank-ready real estate feasibility studies | Pre-seed, $1M (2025) [PUBLIC] | Multi-agent AI with human oversight for speed (days vs. months) [Feasibly website] |
The competitor table is sparse on public details, but the map clarifies on a segment basis. The competitive landscape breaks into three distinct tiers. The incumbents are boutique consulting firms and large advisory arms of firms like CBRE, JLL, and Cushman & Wakefield, which command premium fees and deep client relationships built on decades of institutional trust [PUBLIC]. The challengers are a handful of early-stage software and data platforms, like Zenerate and GrowthGrid, which aim to automate parts of the analysis but often stop short of delivering the full, narrative-driven "bank-ready" report that Feasibly targets [PUBLIC]. Adjacent substitutes include general-purpose financial modeling software (e.g., Argus, Excel) and market data aggregators (e.g., CoStar), which provide components but require significant manual synthesis by the user [PUBLIC].
Feasibly's defensible edge today rests on its claimed integration of speed, cost, and a specific output format. The platform's proposed 3-5 day turnaround for a comprehensive study, priced starting at $10,000, undercuts the traditional consulting timeline of months and fees that can reach six figures [Feasibly website, December 2025]. This edge is currently perishable, however, as it depends entirely on unproven execution. Its durability hinges on two factors: the accuracy and acceptance of its AI-generated reports by lending institutions, and its ability to build a proprietary dataset or workflow that becomes harder for later entrants to replicate. Without demonstrated bank approvals or a growing corpus of validated studies, the speed and cost advantage remains a marketing claim.
The company's most significant exposure is to the incumbents' entrenched distribution and regulatory moat. Boutique feasibility consultants own deep, trust-based relationships with developers and, critically, with the commercial lenders and credit committees that ultimately accept a study as "bank-ready." A named competitor like a top-tier advisory firm at JLL has a specific advantage: its brand on a report carries implicit weight in a risk-averse financing process, an advantage built over decades that an AI startup cannot manufacture quickly. Furthermore, Feasibly does not own the direct sales channel to large institutional developers, who typically procure these services through long-standing RFP processes or personal referrals.
The most plausible 18-month scenario sees the market bifurcating. The winner will be the first AI-native platform that successfully partners with a regional bank or debt fund to codify its analysis as an accepted underwriting input, moving from a "nice-to-have" tool to a mandated step in the lender's own process. The loser will be any pure-play software solution that fails to move beyond serving early-adopter developers and cannot demonstrate that its outputs reliably pass scrutiny in a live debt transaction. For Feasibly, the path to winning involves transitioning its human oversight from a quality-control function into a business development arm that can forge those essential lender partnerships.
Opportunity
PUBLIC The size of the prize for Feasibly is the automation of a multi-billion dollar professional services market, replacing months of high-cost consulting with a software-driven process that scales with data and compute.
The headline opportunity is to become the default due-diligence platform for mid-market commercial real estate development. This outcome is reachable because the company is targeting a specific, high-friction workflow where speed is a direct competitive advantage for its customers. The traditional feasibility study, often costing six figures and taking months from boutique consultancies, represents a bottleneck in capital deployment [Brief Glance, December 2025]. By standardizing the core analytical components,demographics, competitive benchmarks, financial modeling,into a repeatable software process, Feasibly could capture the volume of projects that are currently underserved or delayed by cost and time constraints. The initial wedge of delivering reports in 3-5 days for a starting price of $10,000 creates a clear value proposition for developers and lenders seeking faster decision cycles [Feasibly website, December 2025].
Growth from this wedge could follow several concrete paths. The table below outlines two plausible scaling scenarios.
| Scenario | What happens | Catalyst | Why it's plausible |
|---|---|---|---|
| The Embedded Lender Tool | Feasibly becomes a white-labeled or integrated due-diligence module for regional and community banks. | A partnership with a mid-sized commercial bank to pilot the platform for its loan officers. | The product claim of "bank-ready" reports directly addresses lender needs for standardized, auditable analysis [Feasibly website, December 2025]. Embedding into a bank's workflow creates a high-stickiness, recurring revenue stream. |
| The Municipal Procurement Standard | City and county planning departments adopt Feasibly for evaluating public-private partnership (P3) proposals. | A successful pilot with a municipal economic development authority, leading to a procurement contract. | Founder Brian Connolly's background includes work on economic impact analysis and P3 negotiations for major facilities, suggesting existing domain relationships in this sector [Columbia University SPS]. Municipal budgets for consultant studies are often public and predictable. |
Compounding for Feasibly would likely manifest as a data and workflow moat rather than a classic network effect. Each completed study generates proprietary data on local market comparables, project assumptions, and financial outcomes. This dataset, if aggregated and anonymized, could improve the accuracy and predictive power of the platform's models over time, creating a feedback loop where more usage yields better benchmarks. Furthermore, adoption by lenders or municipalities would create distribution lock-in; once a financial institution's underwriting process is built around a Feasibly report format, switching costs for both the bank and its developer clients become significant.
The size of the win can be framed by looking at the market for real estate consulting and advisory services. While a direct public comparable is scarce for a pure-play feasibility software company, the broader commercial real estate services market is substantial. Firms like CBRE and JLL generate billions in revenue from advisory segments that include feasibility and valuation work. A more focused scenario valuation might look at what a scaled software business capturing even a single-digit percentage of this spend could be worth. If Feasibly executed on the "Embedded Lender Tool" scenario and achieved, for example, $50 million in annual recurring revenue from bank partnerships, a SaaS multiple in the range of 8-12x revenue would imply a valuation of $400-600 million (scenario, not a forecast). This outcome is contingent on proving the accuracy and adoption of its AI-human hybrid model at scale.
Data Accuracy: YELLOW -- Opportunity analysis is based on company claims and founder background; market size and scaling scenarios are extrapolated, not yet evidenced by public customer traction.
Sources
PUBLIC
[Brief Glance, December 2025] Feasibly's AI Cuts Real Estate Analysis from Months to Days | https://briefglance.com/articles/feasiblys-ai-cuts-real-estate-analysis-from-months-to-days
[Business Wire, December 2025] Feasibly Transforms Real Estate Feasibility Analysis With Multi-Agent AI Software | https://www.businesswire.com/news/home/20251202514806/en/Feasibly-Transforms-Real-Estate-Feasibility-Analysis-With-Multi-Agent-AI-Software
[Feasibly website, December 2025] Feasibly | https://www.planfeasibly.com/
[PitchBook] Feasibly 2025 Company Profile: Valuation, Funding & Investors | PitchBook | https://pitchbook.com/profiles/company/1083185-02
[Yahoo Finance, December 2025] Feasibly Transforms Real Estate Feasibility Analysis With Multi-Agent AI Software | https://finance.yahoo.com/news/feasibly-transforms-real-estate-feasibility-230200315.html
[Feasibly how-it-works, 2026] How It Works | Get Feasibility Fast - Start Today | https://www.planfeasibly.ai/how-it-works
[Crunchbase] Feasibly - Crunchbase Company Profile & Funding | https://www.crunchbase.com/organization/feasibly
[Statista, 2024] Management Consulting Market Size | Not publicly available
[PitchBook, 2024] Proptech Analytics Software Market Report | Not publicly available
[Federal Reserve, 2024] Federal Reserve Interest Rate Policy | Not publicly available
[Columbia University SPS] Brian Connolly | Columbia University School of Professional Studies | https://sps.columbia.edu/person/brian-connolly
Articles about Feasibly
- Feasibly's $10,000 AI Report Aims for the Banker's Desk — The pre-seed startup promises to shrink a months-long consulting process to days, but its accuracy must pass the lender's test.