Airstrip AI, AI Legal Co-pilot for startups and small businesses

Hacker News

Airstrip AI is a legal co-pilot that provides tailored/custom accurate legal documents and legal assistance and for startups and small businesses. Currently, you can create legal docs and simplify legal documents. We are a very early-stage company and we are making Airstrip AI better and accurate. Airstrip is using several LLM including GPT-4 and fine-tuned models. Airstrip can also pull information from the documents you upload(RAG) and make conversations accurate. So far for testing, we have created 500+ legal documents, and the results have been quite accurate with minor changes/reviews needed by a lawyer. Website link - https://useairstrip.com/ Demo(YouTube) - https://www.youtube.com/watch?v=etQr_1Ii82I Your feedback is very valuable to us, please try it out and let us know your thoughts, whether it's helpful, other features you might want, or if you find any issues. Also, feel free to share any constructive criticism!

Visit product View on Hacker NewsSep 27, 2023

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, including · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, using · Missing: mac, agents, macos

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: reviews · Missing: plus, platform, intuitive

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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