The world’s most powerful private foundation model for behavioral data
Foundation models like ChatGPT, GPT-4, Dall-E 2, StableDiffusion have revolutionized Text and Image processing. A single large model trained on massive datasets can replace thousands of specialized models. BaseModel.AI uniquely applies the same principles to behavioral data!
Our mission is to understand each and every action as accurately as possible from all available data. To predict behaviors. To make the right choices. To help you care.
Join innovative organizations in using BaseModel.ai (Synerise) as a core AI framework to query the future with multiple questions in such segments like: retail 🔗 telco 🔗 finance 🔗 and more.
General
How do daily customer interactions influence their future behaviors?
Customer service
What is the customer’s likelihood of using a special offer?
Insurance
How many insurance policies will the customer subscribe to this year?
Gaming
How many power-ups/bundles will the gamer buy this month?
Banking
What is the customer’s projected profitability in the next quarter?
Travel
What is the customer’s expected number of trips this year?
Health
How many diagnostic tests will the patient need this year?
Telco
How much data traffic will the customer use this month?
E-Commerce
Which products/promotions/ offers/categories the customer is interested in?
Automotive
What kind of product/category is the customer interested in and why?
Home & Furniture
How to split the customer population into behaviorally distinctive groups?
Payments
What is the utility of customer for your business and what are the factors affecting it?
Fashion
Will the customer make a purchase next week?
Fashion
What steps need to be taken to increase the chance of purchase?
Security
Is recent behavior of the customer inconsistent with past habits?
Compliance
Are there outlier customers in the population, who might be worth looking into?
News & Publishing
Will the reader subscribe to a premium plan?
Why?
Predicting behaviors is a key component of the future
Rapid shifts caused by the exponential growth of AI capabilities will cause profound ripples in the fabric of the economy, society and civilization. Humanity forms a global community, yet every person is unique. Societies function cohesively because we can build mental models of other people.
Mental models allow us to predict what makes other people happy, what behaviors are best avoided, what is amusing, and what is boring. What motivates, and inspires, and causes boredom, discomfort, or pain? Knowing how someone else might react is the foundation of our civilization. As humans, we do that naturally, but our abilities did not evolve to scale beyond a few dozen people within our closest communities. Caring about millions or billions of people is a super-human task, and we use AI to solve it.
By analyzing the full spectrum of observable information about people - their historical interactions with content, actions, decisions, and choices, we build foundation AI models to predict individual future behaviors in real and hypothetical scenarios.
Jack Dąbrowski
Chief AI Officer Synerise / Founder Basemodel.ai
Organization
Understanding people and predicting their behaviors at scale is hard
Many companies have teams of Data Scientists & ML engineers but the complexity and availability of data grow exponentially. They struggle with:
Lack of time and capacity to understand and utilize all available data
Evolving data schemas and new incoming data sources
Creating, maintaining and debugging data pipelines
Handcrafting features describing behaviors
Very high dimensional observation & decision spaces
Each model requires different features and/or input formats
Guaranteeing fundamental correctness (no data leakage, proper temporal splits)
People
Human ability to manually wrangle data scales sub-linearly.
All of this leads to:
A hard cap on the number and complexity of predictive models
A lot of spaghetti code, impossible to grow, hard to maintain
Entrenchment in a "data science" silo, with arcane knowledge and little results to show
Employees becoming complacent and defending their "arcane ways"
State of the now
The ways how Data Scientists, Data & ML/AI Engineers work with behavioral data today are severe bottlenecks.
Understanding complex & intricate patterns of interactions is a super-human challenge. Imagine a single model could learn from all your raw data. Such a model could form a foundation for solving any applied task with unparalleled efficiency and quality.
Manual labor & lack of time
Each ML project required careful manual labor, starting with analyzing available data sources. Lack of time and capacity to understand and utilize all available data
Expert knowledge
Evolving data schemas and new incoming data sources. Countless handcrafted features had to be created using expert knowledge
Human limitations
Despite best efforts, important behavioral cues were often lost due to human limitations. Handcrafting features describing behaviors.
Complicated data pipelines
Creating, maintaining and debugging data pipelines is hard taks.
Multiple models
Each model requires different features and/or input formats.
Data bias
The information content of raw data was orders of magnitude richer than the actual input of models. Very high dimensional observation & decision spaces.
What
BaseModel.ai reduces modeling life-cycle to days instead of months & eliminates common data engineering problems and supercharges behavioral ML.
A single data scientist becomes supercharged 10x 🚀
Automatic feature creation & foundation model training from raw data
No more messy pipelines
No more unused / underutilized data sources
No more problems with evolving data sources
No more maintenance work for keeping the foundations in shape
BaseModel learns to extract, link and interpret information from multiple data sources
No longer constrained by simplistic human heuristics
No more challenges with "multi-modal" or "multi-dataset" fusion
No more months-long trial & error on "what features to use for model X"
No more need for manual feature creation
Fine-tuned models trained just by defining the objective
No more custom projects for each use-case
No more plumbing for every single model
All models benefit from all the data
No more mistakes and problems with information leakage
No more arcane ways of training for specific purposes
Assign scores to every single interaction in raw data
Current standard approach to ML
The current outdated standard approach to machine learning often involves manual feature engineering and model selection, which can be time-consuming and less accurate. It also typically relies on batch learning, which is incapable of adapting to new data in real time, limiting its flexibility and responsiveness.
BaseModel.ai approach to ML
Modern approaches to machine learning leverage automation, with techniques such as deep learning and autoML reducing the need for manual feature engineering and model selection.
Dailymotion.com has applied EMDE/BaseModel.ai to personalize video recommendations in native applications, leading to improved relevance and catalog coverage
EMDE (BaseModel.ai) gives us a generalised framework for recommendations. The embedding generation was superfast (i.e <5 minutes). For context, do remember that GraphSAGE took ~20hours for the same data in the NCR region.
BaseModel.ai is core for all AI services offered in Synerise platform
BaseModel’s powerful behavioral analysis enables us to make sure that our clients receive communication tailored specifically to their preference.
Just 5 days: from configuration to first trained model
Basemodel.ai encapsulates the rapid and streamlined process of setting up and training a machine learning model with modern tools and techniques. This expedited timeline, which includes data preparation, model configuration, training, and evaluation, highlights the efficiency and agility of current ML methodologies.
Features
No more „plumbing pipelines” and „handcrafting model inputs”. Ideate, prototype, evaluate!
Thanks to BaseModal.ai, applied models train faster, require less labelled data and perform better than classic approaches.
BM performs advanced feature engineering, representation learning & training automatically
Benefits
Your private foundation model can bootstrap various applications
Just like large language models can be fine-tuned to any application, BaseModel.ai is not restricted to a predefined set of use cases – you can unleash your creativity freely.
Propensity prediction
Uncover the hidden desires of your customers. BaseModel can accurately predict human inclination towards services, products, brands, categories, styles, or other fine-grained attributes, helping you strategize your marketing approach and increase sales.
Churn prediction
Stay ahead of human attrition with our churn prediction capabilities. BaseModel estimates the likelihood of a customer discontinuing your services or products at any level of granularity, enabling you to take preventative actions and maintain your client base.
Recommendations
Craft personalized human journeys. BaseModel offers individualized recommendations on services, products, brands, styles and any fine-grained attributes you may find relevant - enhancing human satisfaction and loyalty.
Anomaly detection
Spot the unexpected early! On an individual level, BaseModel identifies unusual changes in user behavior, helping you respond promptly to potential issues or opportunities. On an aggregate level, users with atypical behavior patterns can be easily spotted for further analysis.
Profile scoring
Understand your customers better than ever before. BaseModel estimates customer loyalty, lifetime value, their propensity to engage with your communications, or any other metric you come up with - ensuring you can tailor your approach to maximize business success.
Profile matching
Find your perfect match! BaseModel helps identify the most suitable customer groups for campaigns, upsells, and cross-sells, ensuring your efforts reach the most responsive audience.
Hyper-segmentation
Segment like never before. BaseModel can divide your customer base into distinct groups based on behavior, enabling hyper-targeted marketing and improved customer relationships. Forget about manually defining segments based on socio-demographic attributes - let behaviors speak for themselves!
Time to event predictions
Stay one step ahead with BaseModel's time-to-event predictions. Whether it's anticipating the next purchase or predicting when a customer will top up their account, our AI keeps you in the know.
Uplift estimation
Find your perfect match! BaseModel helps identify the most suitable customer groups for campaigns, upsells, and cross-sells, ensuring your efforts reach the most responsive audience.
Unprecedented accuracy of behavioral modeling to ML
BaseModal.ai looks at all human interactions with your organization and learns to predict future behaviors. Automatically. At scale.
Integrations
Simple, secure and quick deployment, with zero overhead
We’ve made sure that starting the journey with BaseModel.ai is smooth,
can be deployed on your own cloud subscription, or on-premise (NVIDIA GPU-equipped server required)
available as a hardened Docker image. It doesn’t require clusters, multiple micro-services or standalone databases
does not duplicate your data. It reads the necessary data directly from your data-warehouse in a streaming way, during model training & inference
easily auditable and can pass the most stringent security requirements.
Case study
BaseModel.ai is blazingly fast. No clusters needed
Our mission to equip every organization with a private behavioral foundation model makes us very conscious about resources. That’s why we take cost-efficiency seriously & optimize our algorithms to be lightning-fast and extremely scalable.
A full self-supervised training pipeline for 10M+ customer profiles with 1 year worth of data can complete within a few hours on a VM equipped with a single GPU. Subsequent fine-tuning for supervised applications is even faster, and requires very little labeled data.
Research
Challenging top AI powerhouses
We take science seriously. We verified BaseModel.ai power at the most prestigious research venues, where early versions of BaseModel.ai took the podium competing with global giants like Google DeepMind, Baidu Research, NVIDIA, Intel, Oppo Research, Xerox PARC, Rakuten and 800+ teams from leading universities worldwide. The difference? Our competitors spent weeks hand-crafting their solutions for every problem separately, with zero reusability. We just applied BaseModel.ai to all the problems.
Solving applied problems becomes a piece of cake.
Solving applied problems becomes a piece of cake thanks to understanding the full spectrum of behaviors. The foundation model is trained only once, and then adapted to specific tasks in a process called fine-tuning. Not having to relearn behaviors from scratch, a fine-tuned model only needs a target to zoom in on.
Careers
What we offer:
very high impact - changing how organizations work, globally
defining the cutting edge of AI and ML
a very different perspective, very different kind of data & problems from all the other AI labs
extremely bright and experienced team
very high organizational flexibility
competitive compensation
100% remote work, possibility to work in a hybrid mode, or on-site in any of our office locations (Poland: Warsaw, Krakow, US: San Francisco)
the scientifically strict, but caring and kind atmosphere
What we expect:
you did not stop learning after graduating
you can both generate ideas and execute them to completion
you are not ashamed of communicating your needs, problems and concerns
you judge ideas based on their merit as opposed to their source, authority or way of communication
you are not afraid to fail, but instead treat failures as "training data" for self-improvement
We are sharing our ideas with others!
Our research papers based on BaseModel.ai framework
Multidimensional Hopfield Network
Redefining Graph Clustering: A Convergence of Algorithms and Networks
Temporal graph models fail to capture global temporal dynamics
We propose a trivial optimization-free baseline of "recently popular nodes" outperforming other methods on all medium and large-size datasets in the Temporal Graph Benchmark.
Temporal graph models fail to capture global temporal dynamics
We propose a trivial optimization-free baseline of "recently popular nodes" outperforming other methods on all medium and large-size datasets in the Temporal Graph Benchmark.
Sair is a lab focused on behavioral modeling, recommendations, large-scale data and graphs processing. We share our ideas, models, and experimental results, also presenting our take on important breakthroughs and interesting technologies. We hope to build a better and more thorough understanding of the field. We believe in the importance of this research not only from a business perspective but most importantly as a study of human decision-making processes.
Having achieved remarkable successes in natural language and image processing, Transformers have finally found their way into the area of recommendation.
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