I built a tool to analyze lease agreements and uncover hidden costs before signing

Source: Dev.to

The problem nobody talks about

If you’ve ever rented in the US, you’ve probably received a lease and thought, “Looks standard.”
That assumption can be expensive.

Lease agreements often include things that aren’t obvious:

• hidden or loosely defined fees
• legal language that isn’t designed to be easy to understand

Even if you read everything, understanding the implications isn’t trivial.

The solution: GoLeazly

I created GoLeazly – a tool that lets you upload your lease and get a focused analysis.

• Upload your lease → receive a report that highlights risky clauses.
• Instead of a generic AI dump, the output is structured like a concise report, making it easier to see what matters.

Try it:

Technical challenges

Parsing real‑world PDFs is painful:

• Leases are often scanned PDFs, not clean text files.
• Simple text extraction isn’t enough; OCR is required to handle scanned documents.

The real challenge is understanding the extracted text:

• Identifying which clauses are risky.
• Filtering out noise and highlighting only the relevant information.

Avoiding “AI dump” UX

A long wall of AI‑generated text isn’t helpful.
The output is organized into sections, providing clarity with less fluff.

Scaling the cost of analysis

Initially the full analysis ran before payment, which didn’t scale.
Now the workflow is:

1. Upload → preview
2. Proceed only if the preview looks good.

This keeps the service efficient and sustainable.

What GoLeazly does today

If you’re about to sign a lease, you can:

• Upload it at
• See any risks, hidden fees, or unclear clauses
• Get results in minutes

Why this matters

• Most people don’t have a lawyer reviewing their lease, yet the contract is written with legal expertise.
• A small check can save a lot of money and trouble later.
• Understanding the lease before signing is a smarter, safer approach.

Try it

• GoLeazly:

Feedback is welcome, especially from developers working with document parsing. The hardest part wasn’t building the AI—it was deciding which information truly helps users avoid bad decisions.