# BaseModel.ai

> BaseModel.ai is an enterprise-grade behavioral foundation model built by Synerise. It replaces the traditional ML stack (manual feature engineering plus a separate model per use case) with a single self-supervised model trained directly on raw event data from your data warehouse. From one foundation, lightweight "scenario heads" handle churn, LTV, recommendations, fraud detection, and more — going from raw events to production-ready predictions in hours or days instead of months. BaseModel.ai is deployed inside the customer's environment (Snowflake Native App, Databricks, or Docker on GPU) so behavioral data never leaves the customer's VPC.

## Core pages

- [Home](https://basemodel.ai/): Introduces the behavioral foundation model concept, headline performance (+138% vs HSTU), the pre-training & fine-tuning pipeline, and deployment impact across industries.
- [Platform](https://basemodel.ai/platform): The end-to-end architecture from data ingestion to production serving, core capabilities, the proprietary Cleora-NX hypergraph embeddings, and governance & compliance.
- [How it works](https://basemodel.ai/how-it-works): The two-stage flow — (1) train one foundation on universal behavioral patterns across the full population, (2) attach lightweight scenario heads for each business objective — plus the adoption path and automated feature engineering.
- [Data models](https://basemodel.ai/data-models): Input requirements (≥10k profiles, ≥100k interactions/month), recommended hardware (NVIDIA A100), training options, and the per-entity split strategy used for robust validation.
- [Benchmarks](https://basemodel.ai/benchmarks): SOTA results on RelBench and Amazon datasets, comparisons against the field, and production A/B test outcomes (e.g. +21% open rate at a major retailer).
- [Use cases](https://basemodel.ai/use-cases): Supported prediction types — Binary, Multiclass, Multi-Label, Regression, Recommendations — and the end-to-end flow from target definition to deployment, with production case studies.
- [Industries](https://basemodel.ai/industries): Sector-specific scenarios — banking (−60% false positives in fraud), telco (+18% ARPU), retail/ecommerce, travel, healthcare, gaming, and more.
- [Integrations](https://basemodel.ai/integrations): Native connectors for Snowflake, BigQuery, Azure Synapse, Databricks, Hive, ClickHouse, and Parquet, with declarative YAML pipeline configuration.
- [Snowflake Native App](https://basemodel.ai/integrations/snowflake): Deployment as a Snowflake Native App so data never leaves the customer's Snowflake account, with a full UI inside Snowsight.
- [Docker / GPU](https://basemodel.ai/integrations/docker): Deployment to a customer-managed GPU cluster via Docker and Ray Serve.
- [Research](https://basemodel.ai/research): Foundational publications — the BaseModel preprint, Cleora & EMDE (ICONIP 2021), the Monad papers, and the RecSys Challenge 2025 work — plus the proprietary Cleora-NX and TREMDE extensions.
- [About](https://basemodel.ai/about): Synerise's history since 2014, the "query the future" mission, company values, and global presence across 32 countries.
- [FAQ](https://basemodel.ai/faq): How BaseModel differs from LLMs and transformers, how data protection works (behavioral sketches stay in the customer's VPC), deployment, governance, and pricing.

## Industry pages

- [Retail](https://basemodel.ai/industries/retail): Next-best-offer, churn prevention, customer lifetime value, and dynamic pricing for retailers (+23% average revenue lift).
- [Banking](https://basemodel.ai/industries/banking): Fraud detection, credit-risk scoring, next-best-product, and churn for banks (−60% false positives).
- [Ecommerce](https://basemodel.ai/industries/ecommerce): Real-time recommendations, cart-abandonment prediction, and lifetime-value modeling for online stores.
- [Telco](https://basemodel.ai/industries/telco): Churn reduction, ARPU growth, and next-best-action for telecom operators (+18% ARPU).
- [Travel](https://basemodel.ai/industries/travel): Personalized offers, demand prediction, and dynamic pricing for travel and hospitality.
- [Healthcare](https://basemodel.ai/industries/health): Patient-behavior prediction, risk stratification, and engagement for healthcare providers.
- [Insurance](https://basemodel.ai/industries/insurance): Risk scoring, claims prediction, churn, and cross-sell for insurers.
- [Gaming](https://basemodel.ai/industries/gaming): Player LTV, churn, and engagement prediction for games and live services.
- [Automotive](https://basemodel.ai/industries/automotive): Lead scoring, service-demand prediction, and customer retention for automotive.
- [Software / SaaS](https://basemodel.ai/industries/software): Activation, expansion, and churn prediction for software and SaaS products.
- [Payments](https://basemodel.ai/industries/payments): Fraud detection, transaction risk, and customer lifetime value for payment providers.
- [Cybersecurity](https://basemodel.ai/industries/security): Anomaly and threat detection from behavioral signals.
- [Fashion](https://basemodel.ai/industries/fashion): Trend-aware recommendations, returns prediction, and lifetime value for fashion retail.
- [Customer Service](https://basemodel.ai/industries/customer-service): Intent prediction, deflection, and proactive support routing.
- [News & Publishing](https://basemodel.ai/industries/news-publishing): Content recommendations, subscription churn, and engagement for publishers.

## Optional

- [Full content (llms-full.txt)](https://basemodel.ai/llms-full.txt): The complete readable body content of every page above, concatenated as Markdown in reading order — for ingesting the full substance of the site in a single fetch.