Litify
The legal operating system built on Salesforce for high-growth firms and corporate departments.
The No-Code Predictive Analytics Platform for Growth-Minded Business Teams.
Graphite Note is a sophisticated No-Code Predictive Analytics platform designed to bridge the gap between raw business data and actionable foresight. By 2026, it has solidified its position as a leading 'Citizen Data Scientist' tool, utilizing an automated machine learning (AutoML) engine that handles feature engineering, algorithm selection, and hyperparameter tuning without requiring Python or R expertise. The technical architecture is built for rapid iteration, allowing business analysts to upload datasets and generate predictive models—such as binary classification, regression, and time-series forecasting—within minutes. Its core value proposition lies in 'Explainable AI' (XAI), providing users with SHAP values and feature importance scores to justify model outputs to stakeholders. The platform integrates seamlessly into the modern data stack via native connectors for Snowflake, BigQuery, and various CRM/ERP systems. For the 2026 market, Graphite Note focuses on operationalizing AI, enabling teams to not only predict outcomes like customer churn or lead conversion but also to simulate 'what-if' scenarios to optimize strategic decision-making.
Algorithms automatically create new variables from existing data to improve model accuracy.
The legal operating system built on Salesforce for high-growth firms and corporate departments.
Empowering investment and credit management with AI-driven operational alpha and cloud-native agility.
The legal industry's gold standard for AI-driven research, analytics, and Shepard’s® validated insights.
A fast, distributed, high-performance gradient boosting framework based on decision tree algorithms.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
A sandbox environment to manually adjust input variables and see real-time shifts in predicted probability.
Implementation of SHAP (SHapley Additive exPlanations) to provide individual prediction transparency.
The system runs XGBoost, Random Forest, and Neural Networks in parallel to find the champion model.
Proprietary temporal models that account for seasonality and trends in sequential data.
Natural Language Processing (NLP) module to convert text data into numerical scores for modeling.
Monitors live model performance over time and alerts users when accuracy decays due to changing data patterns.
Identifying which customers are likely to cancel before they do.
Registry Updated:2/7/2026
Stockouts and overstocking issues causing capital inefficiency.
Sales teams wasting time on leads that never convert.