Litify
The legal operating system built on Salesforce for high-growth firms and corporate departments.
The first Predictive GenAI platform that turns data into business-ready predictions using natural language.
Pecan AI represents a significant shift in the 2026 analytics landscape, pioneering the 'Predictive GenAI' category. The platform utilizes a proprietary generative engine that allows business users to define predictive models using natural language prompts rather than complex code or SQL. Technically, it bridges the gap between raw data warehouses (like Snowflake and BigQuery) and actionable business outcomes by automating the entire data preparation, feature engineering, and model training lifecycle. Its architecture is optimized for high-velocity business environments where traditional data science cycles are too slow. Pecan focuses on 'low-code' predictive power for use cases like customer churn, lifetime value (LTV), and marketing mix modeling (MMM). By 2026, it has integrated deep reinforcement learning capabilities into its automated pipelines, allowing models to self-correct as market conditions shift. The platform is designed specifically for data-driven teams who need enterprise-grade machine learning without the overhead of a dedicated PhD-level data science department, positioning itself as the 'AutoML for the AI era.'
A Large Language Model (LLM) interface that translates business questions into complex SQL and Python-based predictive pipelines.
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.
Uses temporal feature generation to automatically create thousands of potential variables from raw event data.
Continuous tracking of model accuracy and data drift with automated alerts for retuning.
Blends predictive modeling with MTA to determine the true value of marketing touchpoints.
Uses predictive scores to export high-value segments directly to Meta and Google Ads.
Augments small datasets with statistically similar synthetic data to improve model robustness.
Provides SHAP values and feature importance scores for every single prediction generated.
Identifying which users will cancel their subscription before they do.
Registry Updated:2/7/2026
Trigger personalized win-back emails.
Inventory stockouts and overstocking during seasonal shifts.
Sales teams wasting time on low-intent leads.