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:
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
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