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AI can help commercial real estate (CRE) firms turn information in operating statements, rent rolls, leases, and other deal documents into structured data for underwriting and related workflows. In a July 2020 interview, Clik.ai co-founder and CEO Parag Goswami described software that extracted financial figures and used them to populate lender-specific loan-sizing or cash-flow spreadsheets. Clik.ai’s current website presents a broader operations-infrastructure platform, but neither source independently verifies the company’s performance figures.
What the 2020 interview said AI could do for CRE
Angela Scott-Briggs’s TechBullion interview with Parag Goswami, published July 22, 2020, described Clik.ai as enterprise software for banks, asset managers, and real estate investors. Its central use case was automating the movement of financial information from deal documents into underwriting spreadsheets.
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Goswami described the problem as manual data transfer that can create errors, costs, and inefficiencies. In the workflow he outlined, software reads documents such as operating statements and rent rolls, parses financial data, and places it into a bank- or lender-specific loan-sizing or cash-flow model. That is a practical form of document automation: it reduces repetitive entry work and helps analysts work with information in a consistent format. It does not, by itself, determine whether a property is a good investment or replace a lender’s underwriting judgment.
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From source documents to structured figures
CRE deal information may arrive in PDFs, scans, spreadsheets, and tabular statements, with labels and layouts that vary from property to property. The interview described parsing financial information from operating statements and rent rolls. Once extracted, figures can be organized for review rather than rekeyed one field at a time.
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From extracted figures to lender-specific models
The intended output is not simply a searchable document or a generic summary. Goswami said Clik.ai could use the extracted information to create lender-specific loan-sizing or cash-flow underwriting spreadsheets. The interview also described an Excel add-in for placing rent-roll data into spreadsheets and APIs for connecting with deal-pipeline, CRM, and servicing systems.
This document-to-model link matters because lenders use their own templates and assumptions. Automation is useful only when the data lands in a format that fits the relevant workflow and exceptions can be checked. The interview describes the workflow and the company’s capabilities; it does not independently test extraction quality or establish how human review was handled.
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Other CRE workflows mentioned in the interview
The 2020 article also described possible uses beyond initial underwriting:
- Document search and due diligence: parsing documents and finding information across collections during transaction review.
- Lease abstraction: extracting key terms and data from leases for analysis and ongoing operations.
- Mortgage servicing and asset management: using document data in downstream servicing and property-management processes.
- ARGUS modeling: supporting work associated with ARGUS models, as described in the interview.
- System connections: APIs intended to connect with deal-pipeline, CRM, and servicing platforms.
These are capabilities described by Goswami in 2020, not independent findings about product performance or confirmation that every workflow remains available in the same form today.
What Clik.ai advertises today
Clik.ai’s current official homepage describes the company as an operations-infrastructure platform for commercial real estate and banking. The listed offerings extend across underwriting, loan operations, lease data, reporting, and technology services:
- Automated underwriting and population of Fannie Mae and Freddie Mac workbooks.
- Loan onboarding, 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 prospective clients to schedule a demo. It also displays organizational logos and a testimonial attributed to Eric Tupta, Director of Digital Transformation at Bellwether Real Estate Capital. Those displays establish what the company presents on its site; they do not establish contract terms, deployment scope, or independently measured customer outcomes.
How to interpret the company’s performance figures
The figures below are claims attributed to Clik.ai, not independently verified benchmarks. The 2020 figures were reported by Goswami in the TechBullion interview; the current figures appear on Clik.ai’s homepage accessed in 2026. The homepage does not state measurement methods for its displayed figures.
| Source and date | Figure presented | What it establishes |
|---|---|---|
| Goswami in TechBullion, July 2020 | “Over $12 billion” processed since inception | A company claim reported in the interview; no independent validation or calculation method is provided there. |
| Goswami in TechBullion, July 2020 | “More than 99% accuracy” for key financial-data extraction | A company claim reported in the interview, not a published independent accuracy test. |
| Goswami in TechBullion, July 2020 | “More than 100,000 financial documents” underwritten | A company claim reported in the interview; the article does not provide a counting method. |
| TechBullion interview, July 2020 | Analysts spend “over 5-6 hours of the day” filtering information | A claim stated in the interview, not an industry-wide statistic supported by a cited study. |
| Clik.ai homepage, accessed 2026 | 99% accuracy across financial documents | A current company-presented figure; the reviewed page gives no measurement method. |
| Clik.ai homepage, accessed 2026 | 90% reduction in manual data-processing time | A current company-presented figure; the reviewed page gives no measurement method. |
| Clik.ai homepage, accessed 2026 | 3x deal-pipeline capacity without proportional hiring | A current company-presented figure; the reviewed page gives no measurement method. |
| Clik.ai homepage, accessed 2026 | 5+ vendors replaced by one platform | A current company-presented figure; the reviewed page gives no measurement method. |
| Clik.ai homepage, accessed 2026 | 24-hour turnaround for underwriting that previously took days | A current company-presented figure; the reviewed page gives no measurement method. |
The numbers are useful as statements of how Clik.ai characterizes its offering, but they should not be treated as proof of expected savings or accuracy for another firm. The 2020 interview also discussed AWS-powered security features, server-side encryption, activity logging, and SOC II compliance. That historical description does not establish current security controls or current compliance status.
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What CRE firms should check before adopting document automation
Before relying on an AI extraction workflow, a CRE firm can evaluate the system against its actual documents, models, and controls. Useful questions include:
- Document coverage: Which operating statements, rent rolls, leases, scans, and spreadsheet formats are accepted? How does performance vary across layouts and document quality?
- Model fit: Can extracted data populate the firm’s lender-specific templates and downstream models without breaking formulas or assumptions?
- Review and exceptions: How are low-confidence fields, missing values, and conflicting figures flagged for human verification?
- Integrations: Which servicing, CRM, pipeline, and reporting systems connect to the workflow, and what implementation work is required?
- Security evidence: What current controls and independent compliance evidence can the vendor provide? Do not rely on a historical interview as proof of present status.
- Operational economics: What implementation, support, and ongoing costs apply, and how do they compare with the specific manual tasks and error risks the firm wants to reduce?
A representative pilot using the firm’s own document mix and underwriting templates is a more relevant basis for a decision than an unqualified marketing percentage.
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