Foundation models like ChatGPT, GPT-4, DALL-E 3, and Stable Diffusion have revolutionized text and image processing. A single large model trained on massive datasets can replace thousands of specialized models. For the first time, BaseModel allows the same principle to be applied to behavioral data.

Reduce your modeling life-cycle to days instead of months.

Understanding complex and intricate patterns of interactions is a super-human challenge. Imagine a single model that could learn from all your raw data. Such a model could form a foundation for solving any applied task with unparalleled efficiency and quality. This is exactly what BaseModel does.

Until now:

Each ML project required careful manual labor, starting with analyzing available data sources.
Countless handcrafted features had to be created using expert knowledge.
Despite best efforts, important behavioral cues were often lost due to human limitations.
The information content of raw data was orders of magnitude richer than the actual input of models.

BaseModel.AI eliminates these problems and supercharges behavioral ML.

Example questions BaseModel.AI can answer:

General: How do daily customer interactions influence their future behaviors?
Ecommerce: What is the customer’s likelihood of using a special offer? Which products/promotions/offers/categories is the customer interested in?
Fashion: Will the customer make a purchase next week? What steps need to be taken to increase the chance of purchase?
Home & Furniture: How can the customer population be split into behaviorally distinctive groups
Retail: How much will the customer spend in a specific category next week?
Banking: What is the utility of the customer for your business, and what are the behavioral and sociodemographic factors affecting it? What is the customer’s projected profitability in the next quarter?
Insurance: How many insurance policies will the customer subscribe to this year? Will the customer churn in the near future, and what events had an impact on that?
Payments: Is the recent behavior of the customer inconsistent with past habits?
Telco: How much data traffic will the customer use this month?
Automotive: What kind of product/category is the customer interested in, and why?
Gaming: How many power-ups/bundles will the gamer buy this month?
Travel: What is the customer’s expected number of trips this year?
And many many more…

Interpretability and Explainability

BaseModel.AI provides possibility to demystify black-box model and provide interpretability of the model. We undestand that this is incredibly significant for organizations in order to make fair and unbiased predictions.

How does BaseModel.AI work under the hood?

BaseModel.AI automatically finds proper representations suitable for aggregation of data, such as:

Graphs
Texts
Numbers
Categorical variables

It utilizes a mix of Graph ML, differential geometry, and deep learning. BaseModel.AI uses proprietary research to represent complex multi-modal, multi-source histories of behavior in the form of sparse vectors, called Universal Behavioral Representations. Technically, these vectors represent probability density estimates over Riemannian product manifolds and can serve as both inputs and targets for neural network training.

In simpler words, BaseModel.AI compresses multi-modal event series into very wide fixed-length sparse vectors.

The key property of BaseModel’s representations is that they are approximately reversible. This means it is mathematically possible to query a Universal Behavioral Representation about the elements aggregated within, with high accuracy.

BaseModel.AISynerise

BaseModel.AI

Synerise

BaseModel.AI

Synerise

BaseModel.AI
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