Curious about how you handle clause level parsing when clauses are highly context dependent (e.g., indemnity linked to limitation of liability, or IP ownership tied to subcontractor terms).
Also how do you approach cross referencing between clauses? A lot of “AI contract analysis” tools I’ve seen tend to miss dependencies like “subject to Section 10(b)” or misinterpret defined terms.
Is your model resolving definitions and references in a structured graph, or relying purely on token level embeddings?
We’re a small team of engineers and lawyers who got tired of reading 50-page contracts line by line. Reviewing NDAs, SaaS agreements, and employment contracts was slow, repetitive, and error-prone and even with templates, humans miss things.
Why we built it
Last year, we were working with a few early-stage startups that were drowning in vendor and customer contracts. Lawyers were expensive; founders didn’t have time; AI tools at the time were either too generic (“chat with your PDF”) or too opaque to trust.
-Understands legal context (not just raw text)
-Explains clauses clearly (“This indemnity clause exposes you to X risk”)
-Highlights risks, missing terms, and inconsistencies
-Lets you compare versions to see what changed
Under the hood:
-We use LLMs fine-tuned on contract-specific datasets (public agreements + anonymized real samples).
-A clause-level parser breaks down contracts into semantic sections (e.g., Termination, Liability, IP, etc.).
-We run a hybrid rule-and-LLM pipeline: rules handle structure / entities; the model handles nuance and reasoning.
-Everything is stateless and localizable, with optional on-prem deployment for privacy-sensitive orgs.
-We also built a comparison engine that highlights redlines across different drafts (useful for negotiation).
Nice work! I use Claude for task like this with a simple prompt. I’m not a lawyer so I’m certain my current process is risky.
I think your site needs to call out that problem more clearly. The two pains you solve for me:
- saving money on a lawyer for first pass / basic contracts
- Reducing risk of my current “vibe red lining”
I also think you need establish your credibility on the site. My first thought was “did some kid vibe code this? Or is this someone with an actual JD.”
Thanks so much for the thoughtful feedback this is super helpful You’re exactly right: a lot of users are doing “vibe redlining” with AI tools, which saves time but definitely adds risk. I’ll make sure the site messaging calls that out more directly that ContractAnalize helps people save money on lawyers and reduce risk by offering a more reliable first pass.
Great point too about credibility. I’m planning to add a clear About section to highlight the team’s background and experience in both law and AI so visitors know this isn’t just a hobby project but built with real legal and product expertise behind it.
Appreciate you taking the time to share this it’s exactly the kind of feedback that helps make the product stronger.
Curious about how you handle clause level parsing when clauses are highly context dependent (e.g., indemnity linked to limitation of liability, or IP ownership tied to subcontractor terms).
Also how do you approach cross referencing between clauses? A lot of “AI contract analysis” tools I’ve seen tend to miss dependencies like “subject to Section 10(b)” or misinterpret defined terms.
Is your model resolving definitions and references in a structured graph, or relying purely on token level embeddings?
We’re a small team of engineers and lawyers who got tired of reading 50-page contracts line by line. Reviewing NDAs, SaaS agreements, and employment contracts was slow, repetitive, and error-prone and even with templates, humans miss things.
Why we built it
Last year, we were working with a few early-stage startups that were drowning in vendor and customer contracts. Lawyers were expensive; founders didn’t have time; AI tools at the time were either too generic (“chat with your PDF”) or too opaque to trust.
-Understands legal context (not just raw text)
-Explains clauses clearly (“This indemnity clause exposes you to X risk”)
-Highlights risks, missing terms, and inconsistencies
-Lets you compare versions to see what changed
Under the hood:
-We use LLMs fine-tuned on contract-specific datasets (public agreements + anonymized real samples).
-A clause-level parser breaks down contracts into semantic sections (e.g., Termination, Liability, IP, etc.).
-We run a hybrid rule-and-LLM pipeline: rules handle structure / entities; the model handles nuance and reasoning.
-Everything is stateless and localizable, with optional on-prem deployment for privacy-sensitive orgs.
-We also built a comparison engine that highlights redlines across different drafts (useful for negotiation).
Nice work! I use Claude for task like this with a simple prompt. I’m not a lawyer so I’m certain my current process is risky.
I think your site needs to call out that problem more clearly. The two pains you solve for me: - saving money on a lawyer for first pass / basic contracts - Reducing risk of my current “vibe red lining”
I also think you need establish your credibility on the site. My first thought was “did some kid vibe code this? Or is this someone with an actual JD.”
Thanks so much for the thoughtful feedback this is super helpful You’re exactly right: a lot of users are doing “vibe redlining” with AI tools, which saves time but definitely adds risk. I’ll make sure the site messaging calls that out more directly that ContractAnalize helps people save money on lawyers and reduce risk by offering a more reliable first pass.
Great point too about credibility. I’m planning to add a clear About section to highlight the team’s background and experience in both law and AI so visitors know this isn’t just a hobby project but built with real legal and product expertise behind it.
Appreciate you taking the time to share this it’s exactly the kind of feedback that helps make the product stronger.