Apers vs. Clik AI

Overview

Clik AI specializes in extracting structured data from commercial real estate documents — primarily rent rolls and trailing-12 income statements. It reads PDF documents that would take an analyst hours to process manually and produces clean, structured data tables. For teams whose bottleneck is specifically getting data out of documents and into a usable format, Clik AI is a focused solution.

Apers includes document extraction as part of a broader underwriting system. The UDPE (Unstructured Data Processing Engine) reads the same documents Clik AI handles, but goes further — reconciling data across multiple documents, mapping extracted data to financial model assumptions, and generating complete Excel workbooks. The comparison is between a document extraction specialist and a full underwriting platform.

Feature Comparison

Table 1 — Clik AI excels at document extraction, particularly rent rolls. Apers extends the pipeline from extraction through model generation, debt sizing, and waterfall distributions.

Extraction Depth

Clik AI built its reputation on rent roll extraction, and it's good at it. Upload a messy PDF rent roll — irregular formatting, merged cells, handwritten annotations — and Clik AI returns structured data: unit numbers, types, square footage, current rent, market rent, lease expiration, concessions. For teams that process dozens of rent rolls per week, this eliminates hours of manual data entry.

Apers UDPE handles the same documents but approaches extraction differently. Rather than treating each document as an independent extraction task, UDPE processes all deal documents as a set — understanding that the rent roll, T-12, and OM describe the same property from different angles. This context awareness improves extraction accuracy because the system can cross-reference fields between documents.

After Extraction

This is where the tools diverge sharply. Clik AI's output is a data table — clean, structured, ready to paste into your own spreadsheet. You still need to:

1. Open your firm's model template.
2. Paste the extracted data into the appropriate cells.
3. Map each field to the right model input (is "Total Monthly Rent" gross or net? Does it include utility reimbursements?).
4. Build or update the cash flow projections, debt sizing, waterfall, and sensitivity analysis.
5. Create the source citation trail manually.

Apers eliminates steps 1 through 5. Extracted data goes directly into a complete financial model, with each assumption linked to its source document. The output is an IC-ready Excel workbook, not a data table that feeds into your existing manual workflow.

The question is how much of the total underwriting time extraction alone saves. If data entry from documents is 30% of the total workflow, Clik AI saves 30%. If the remaining 70% — model construction, debt sizing, waterfall modeling, sensitivity analysis — is also automated, the total time savings is fundamentally different.

When Clik AI Wins

• You have great templates. If your firm's Excel templates are well-maintained, battle-tested, and exactly how your IC expects to see models, you may prefer extracting data into your existing templates rather than generating new models. Clik AI feeds your existing workflow.
• Volume rent roll processing. If you process hundreds of rent rolls per month — for portfolio management, benchmarking, or rent roll analysis rather than deal-specific underwriting — Clik AI's focused extraction pipeline is efficient for that use case.
• Simple deal types. If your deals are straightforward — stabilized multifamily acquisitions without complex capital structures — the model construction step may not be the bottleneck, and extraction alone may be sufficient.

When Apers Wins

• Full pipeline needed. If you need to go from documents to IC-ready model without manual model construction in between, Apers handles the entire pipeline.
• Complex deal structures. Waterfall distributions, multi-tranche debt, LIHTC, development pro formas. Once the data is extracted, you still need a system that can model these structures — and Clik AI doesn't do that.
• Cross-document reconciliation. When the rent roll and T-12 disagree, you need a system that flags the discrepancy before it propagates into your model.
• Audit trail requirements. Cell-level citations — not just "this came from the rent roll" but "this came from page 3, row 47 of the rent roll" — are built into every Apers model, critical for thorough due diligence.

How to Evaluate

Upload the same rent roll and T-12 to both tools. Then ask two questions:

1. Extraction accuracy: Did both tools correctly extract every field? Compare the outputs row by row.
2. Time to IC-ready model: From the moment you upload documents, how long until you have a model your IC would review? With Clik AI, measure extraction time plus your manual model-building time. With Apers, measure total time to complete model.

If extraction accuracy is comparable and the total time to model differs significantly, the difference is the value of integrated model generation. For a broader view, see our full comparison overview.