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 w

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).