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AI can help commercial real estate (CRE) firms turn information in rent rolls, operating statements, and other deal documents into structured data for underwriting and related workflows. A July 2020 interview with Clik.ai co-founder and CEO Parag Goswami described that approach; Clik.ai’s current website presents a broader operations platform. The company’s performance figures in both sources are company claims, not independently verified results.
What the 2020 interview said AI could do
In an interview published by TechBullion on July 22, 2020, Angela Scott-Briggs asked Parag Goswami, identified there as Clik.ai’s co-founder and CEO, about AI’s role in commercial real estate. He framed the problem as the time and risk involved in manually transferring financial information from deal documents into analysis tools. “Manual data transfer is tedious work that can often result in errors, unnecessary costs, and inefficiencies,” he said. Read the TechBullion interview.
From documents to underwriting spreadsheets
The central workflow described was document extraction: software reads information in operating statements, rent rolls, PDFs, scans, and spreadsheets, then parses financial data for use in underwriting. Goswami described Clik.ai’s software as automatically extracting data from operating statements and rent rolls and using it to create a bank- or lender-specific loan-sizing or cash-flow underwriting spreadsheet. In practice, this kind of workflow is intended to reduce repetitive copying and help analysts move from source documents to a familiar model more quickly. The interview describes the product’s intended operation; it is not an independent test of the software’s extraction quality or underwriting output.
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The interview also discussed document parsing and search, lease abstraction, due diligence, ARGUS modeling, mortgage servicing, and property asset management. It mentioned an Excel add-in for bringing rent-roll data into spreadsheets, plus APIs for connecting with deal-pipeline, CRM, and servicing systems. These examples show how document extraction could feed broader CRE processes, but the interview does not independently validate each capability or establish how the product works today.
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What Clik.ai claims about results—and what the figures establish
The interview included figures presented by Goswami as company results in 2020. Clik.ai’s current homepage displays a separate set of operational figures. Neither source provides methodology on the reviewed pages, so the numbers should be treated as company claims rather than measured industry benchmarks or independently verified product results.
| Source and date | Claim displayed or reported | How to interpret it |
|---|---|---|
| Goswami in the TechBullion interview, July 2020 | Over $12 billion processed since inception; more than 99% accuracy for key financial-data extraction; more than 100,000 financial documents underwritten; analysts spending “over 5-6 hours of the day” filtering information. | Historical company claims reported in the interview. No measurement method or independent substantiation is provided there; the analyst-time statement is not a general CRE-industry statistic. |
| Clik.ai homepage, accessed in 2026 | 99% accuracy across financial documents; 90% reduction in manual data-processing time; 3x deal pipeline capacity without proportional hiring; 5+ vendors replaced by one platform; 24hr turnaround for underwriting that took days. | Current company-presented figures. The reviewed homepage does not explain measurement methods, conditions, or independent verification. |
The two sets of figures are not directly comparable: they come from different dates and use different descriptions. The 2020 interview’s “more than 99%” figure refers to key financial-data extraction, while the current homepage says “99%” accuracy across financial documents. Neither should be generalized to every document, lender, portfolio, or implementation.
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How the current platform is positioned
Clik.ai’s official homepage now describes an operations-infrastructure platform for commercial real estate and banking. Its listed offerings extend beyond document extraction to automated underwriting, Fannie Mae and Freddie Mac workbook population, loan onboarding and data digitization, disbursement and PCNA processing, custom financial models, lease abstraction and audit, portfolio analytics and reporting, Salesforce configuration, CRE technology consulting, and investment analysis. The homepage invites potential clients to schedule a demo. See Clik.ai’s current platform and offerings.
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The site displays organization logos, including Newmark, Lument, BWE, Merchants Capital, Colliers, ACORE Capital, Columbia National, Webster Bank, Northmarq, and Heitman. It also displays a testimonial attributed to Eric Tupta, Director of Digital Transformation at Bellwether Real Estate Capital: “ClikAI enables us to process more deals, allowing us to focus more time on customers.” These are logo displays and a testimonial attribution; by themselves, they do not establish contract terms, deployment scope, or independently verified outcomes.
What CRE firms should check before adopting document automation
The interview and current product page explain a vendor’s approach and positioning, not how a buyer’s own documents or workflows will perform. A practical evaluation should focus on the fit between source files, outputs, review practices, integrations, security evidence, and implementation effort.
- Document coverage and extraction quality: Test the actual mix of rent rolls, operating statements, scans, PDFs, and spreadsheets your team receives. Check how the system handles inconsistent layouts, missing values, and amended documents.
- Model and workflow fit: Confirm that extracted values populate the lender-specific spreadsheets or models you use, and determine how changes to templates are handled.
- Human review and exceptions: Establish which fields require verification, how uncertain extractions are flagged, and who resolves exceptions before data enters a credit or servicing decision.
- Downstream systems: Map the required connections to Excel, CRM, deal-pipeline, servicing, and reporting systems. Confirm what is supported in the proposed deployment rather than relying on a general API description.
- Security and compliance evidence: Request current documentation appropriate to your organization’s requirements, and verify its scope and date. The 2020 interview made historical claims about AWS-powered security features, server-side encryption, activity logging, and SOC II compliance; the current homepage reviewed here does not verify those specific claims or establish their current status.
- Implementation and total cost: Clarify setup responsibilities, support, workflow changes, and ongoing costs alongside any projected reduction in manual work.
What the sources do—and do not—show
The 2020 interview is useful for understanding how Clik.ai described AI-assisted underwriting and related CRE document workflows at that time. The current homepage shows how the company now presents a wider set of operations offerings. Together, they document the company’s stated direction, not independent proof of extraction accuracy, time savings, security effectiveness, compliance status, customer outcomes, or whether today’s platform operates exactly as the interview described in 2020.
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