I built an AI that analyzes lease agreements before you sign (and finds hidden fees).

El pomberito 2.0 — Mon, 20 Apr 2026 13:44:45 +0000

Most people don’t read their lease agreements.

And even if they try… they don’t really understand them.

I realized this after almost signing a rental contract in the U.S. that looked completely fine — until I actually took the time to read it carefully.

What I found?

Hidden fees
Ambiguous clauses
Penalties buried in legal jargon

That’s when I thought:

👉 Why isn’t there a tool that just explains this in plain English?

So I built one.

🚨 The problem with lease agreements

Lease contracts are not written for tenants.

They are written:

by lawyers
for landlords
in complex legal language

And most people:

skim them
trust the process
sign quickly

This creates a huge imbalance.

⚠️ Common issues people miss

Here are a few real examples I found while analyzing leases:

Automatic renewal clauses

If you don’t notify in advance, your lease renews automatically.

Sometimes under worse conditions.

Early termination penalties

Leaving early can cost:

2–3 months of rent
or even the full remaining lease

Hidden fees

These are often buried:

maintenance fees
administrative fees
cleaning charges

Vague legal wording

Clauses that are intentionally unclear…

And guess who benefits from that ambiguity.

🤖 Building the solution

I wanted something simple:

👉 Upload a lease → get a clear explanation of risks

So I built an AI-powered lease analyzer.

Core idea:
Extract text from the document (PDFs, even scanned ones)
Process it with AI
Detect risky patterns
Translate legal language into plain English
🧠 Key technical challenges

Handling messy PDFs

Lease documents are often:

poorly formatted
scanned images
inconsistent structure

Solution:

OCR for scanned documents
chunking large files
context-aware parsing

Context understanding

Contracts are not just text — they’re relationships.

Example:

A clause may reference another clause 10 pages later.

Solution:

semantic grouping
cross-reference linking
structured extraction

Long context limits

Leases can be 20–50 pages.

Solution:

chunking + summarization
hierarchical analysis
merging outputs into a final report

Making it actually useful

Not just “AI output”.

Users need:

clear risks
simple explanations
actionable insights

So instead of generic summaries, the output includes:

risk flags
highlighted clauses
explanations in plain English
🚀 The result: GoLeazly

I ended up building:

👉 https://www.goleazly.com

It lets you:

Upload your lease
Detect hidden fees
Identify risky clauses
Get a lease risk score
Understand everything before signing
💡 Why this matters

A lease is one of the most expensive things people sign.

Yet it’s one of the least understood.

Spending a few minutes analyzing it can literally save:

💸 Thousands of dollars
💸 Legal issues
💸 Stress

🔮 What’s next

I’m currently working on:

State-specific legal insights
Negotiation suggestions
Better detection of edge-case clauses
👀 Final thought

Before this, people had two options:

read everything (and struggle)
or sign blindly

Now there’s a third:

👉 Use AI to understand what you’re signing.

If you’ve ever signed a lease without reading it fully… you’re not alone.

But now you don’t have to.

🔗 Try it

👉 https://www.goleazly.com

I Analyzed 100 Rental Leases Using AI — Here’s What I Found

El pomberito 2.0 — Thu, 09 Apr 2026 18:20:10 +0000

Most people don’t fully read their lease agreements.

Not because they don’t want to — but because they’re hard to understand.

So I decided to test something:

👉 What if we analyze leases using AI?

Over the past few weeks, I ran around 100 rental lease agreements from different U.S. states through an analysis system.

The results were surprisingly consistent.

Common Patterns Across Leases

Here’s what kept showing up:

Hidden or poorly explained fees
Clauses allowing landlords to change certain conditions
Automatic renewal terms buried deep in the contract
Early termination penalties that can cost thousands

None of these are necessarily illegal.

But they are very easy to miss.

The Real Problem

Lease agreements are:

long (often 30–50 pages)
written in legal language
not designed for clarity

This creates a gap between what people sign and what they actually understand.

Why AI Makes Sense Here

Natural Language Processing (NLP) can:

extract key clauses
simplify legal language
highlight potential risks
provide contextual understanding

Especially when combined with:
👉 state-specific knowledge

What I Built

To explore this further, I built a small tool called GoLeazly.

It allows users to:

upload a lease
get a risk score
identify clauses worth reviewing
understand the contract in plain English

The goal isn’t to replace legal advice —
but to help people understand what they’re signing.

👉
Key Insight

Most renters aren’t careless.

They just don’t know what to look for.

Question

How do you usually review a lease before signing?
Do you read everything — or just trust it’s standard?

DEV Community: El pomberito 2.0

I built an AI that analyzes lease agreements before you sign (and finds hidden fees).

I Analyzed 100 Rental Leases Using AI — Here’s What I Found