CaseCrunch

Upload batch of claim PDFs to CaseCrunch via API.

System extracts key facts and party details.

Model compares facts against recent judicial rulings.

System outputs a 'Success Probability' and 'Recommended Settlement' score.

High-risk claims are flagged for manual legal review.

Ingest the target company's legal department data.

CaseCrunch scans all active and threatened litigation.

System calculates the potential total loss liability based on predictive outcomes.

Report is generated for the acquisition team to adjust bid price.

Feed current internal policies into the NLU engine.

CaseCrunch compares policies against new FCA/EU directives.

Identify specific clauses that increase the risk of regulatory fines.

Generate corrected policy language recommendations.

Input the core grievance and evidence summary.

CaseCrunch analyzes historical class action success rates in that jurisdiction.

Simulate different legal arguments to find the strongest path to certification.

Provide a ROI forecast for the litigation funding partner.

Input the specific case type and court location.

CaseCrunch queries its database for firm performance in similar cases.

System ranks firms by 'Win Rate' and 'Average Settlement Time'.

Output a data-backed recommendation for legal procurement.

Analyze the proposed contract drafts.

Compare clauses against a database of litigated contract disputes.

Flag 'High Risk' clauses that traditionally result in court battles.

Suggest alternative wording with lower historical litigation rates.

Identify the assigned judge.

CaseCrunch retrieves the judge's past rulings and linguistic style.

System analyzes the judge's 'leaning' on specific types of evidence.

Lawyer adjusts the pleading to emphasize points favored by the judge.