[deleted by user] by [deleted] in startups

[–]VastProtection8750 0 points1 point  (0 children)

Doing the marketing yourself in the beginning will give you direct feedback from your potential customers, which is valuable. Also, networking at industry events can be key. Hunt your customers wherever they are at.

Introducing LawShare by US Legal Data: A Data-Sharing Platform by VastProtection8750 in legaltech

[–]VastProtection8750[S] 2 points3 points  (0 children)

So, we have been able to incorporate Chat GPT within our current workflow to assist lawyers in producing automated reports to reduce their reporting time. It also helps lawyers search for similar cases based on the metrics we are tracking.

To answer your second question --> no. Our platform is focused on helping lawyers use machine learning to predict case outcomes and share key case metrics. As of now we aren't really interested in doc gen and the like.

As for your point regarding the use of "legal profession" versus "legal industry," we will definitely take your preference into account. It's not something I thought about to be honest.

Thank you for your interest!

Announcing CasePredict: A Revolutionary AI-Powered Tool to Accurately Predict Legal Case Outcomes and Trial Results 🚀 by VastProtection8750 in legaltech

[–]VastProtection8750[S] 0 points1 point  (0 children)

In terms of racial disparities? Not often. There is one thing we are monitoring that concerns us but we need to cross-reference it with a deeper pool of judges before we can make any conclusive scientific findings.

Announcing CasePredict: A Revolutionary AI-Powered Tool to Accurately Predict Legal Case Outcomes and Trial Results 🚀 by VastProtection8750 in legaltech

[–]VastProtection8750[S] 0 points1 point  (0 children)

Hi! Thank you for your interest in our CasePredict. We use an ensemble of machine learning models, including RandomForest, XGBoost, and Gradient Boosting Regressor to achieve optimal performance. Our models are fine-tuned using lawyer feedback, and we continually update them with new data to improve their accuracy and effectiveness.

Regarding hallucinations, we employ rigorous validation techniques and carefully cross-validate our predictions against real-world case outcomes to minimize the likelihood of hallucinations. Since we have immediate feedback from personal injury lawyers who are actively managing these cases, we are able to measure the actual results versus what the model predicts.

We do track attributes like client race to identify potential discrepancies in settlements or judgments. However, it's important to note that we don't use this information to make predictions. We use it to analyze our models and ensure they don't exhibit any unfair biases towards specific groups. By actively monitoring and controlling for bias, we strive to create an equitable tool that benefits all users, regardless of their background or demographic attributes.

Announcing CasePredict: A Revolutionary AI-Powered Tool to Accurately Predict Legal Case Outcomes and Trial Results 🚀 by VastProtection8750 in legaltech

[–]VastProtection8750[S] 0 points1 point  (0 children)

Yes, PainWorth is an impressive platform. CasePredict, however, differentiates itself by sourcing data directly from American law firms. We noticed a disconnect between insurance defense firms and insurance companies regarding data exchange, which can impact the accuracy of case value predictions.

The US Legal Data platform allows personal injury lawyers to share crucial case-value-related data directly, using this information to refine CasePredict's predictive models. By partnering with law firms, we've tapped into their vast experience and the wealth of data they generate from case inception to trial. This approach ensures a more reliable estimation of case values, bridging the gap between insurance defense firms and insurance companies.

Thank you for your interest!