Join innovative organizations using Synerise Platform Explore
and explore hundreds of low code use cases
Nike - BasemodelAsics - BasemodelIKEA - BasemodelOrange - BasemodelCompany logoCompany logoCompany logoCompany logoCompany logoZabka - BasemodelCompany logoCompany logoCompany logoCompany logoCompany logoCompany logoCompany logoCompany logoCompany logo
Recommendations on an empty basket
Use the potential of emptied basket by displaying AI recommendations
Segment creation based on quantiles
Reach a specific audience by defining a segment based on quantiles
Recommendations compliant with the Omnibus
Create personalized recommendations that show the lowest price over the last 30 days for discounted products
Low-stock abandoned cart campaign
Create low-stock campaign for customers with abandoned carts
Dynamic report for products bought together with top 10 products
Create a report with top 10 complementary products to top 10 bestsellers
Recommendations of similar products with item context
Create a carousel of product recommendations similar to items recently added to favorites
Personalized promotion in the mobile app with display time limitation
Create a personalized mobile promotion available to the customer within a specified time frame
Send a list of profiles from Synerise to Google Ads
Send propenisty-based customer segmentation to Google Ads
What we offer?

Developer & business centric experience in one place

We built an industry agnostic & multi-feature platform. Controlling data processing, AI pragmatic tools and execution scenarios in one place give the best results.
Synerise Experience Platform is all in one self-service platform for building exceptional experiences with thousands of features and ready to go use cases.

Built for everyone with mouse IQ who wants to grow organization with self-service tools.

Pricing based on datapoints usage

The most advanced in the world private foundation model for behavioral data.

Built for developers, data engineers, data scientists.

Commercial License
Open source general-purpose model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data.

Built for developers, data engineers, data scientists.

Open source & Commercial License
Efficient Manifold Density Estimator is a framework utilizing arbitrary vector representations with the property of local similarity to succinctly represent smooth probability densities on Riemannian manifolds.

Built for developers, data engineers, data scientists.

Open source
Ultra-fast disk & in-memory column & row store engine designed specifically for real-time heterogeneous data collection & processing, behavioral intelligence, data management & execution of various business scenarios at scale.

Available only with Synerise Platform has applied EMDE/ to personalize video recommendations in native applications, leading to improved relevance and catalog coverage

EMDE ( 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. 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.

Unified Synerise Experience Platform

A fully integrated suite of behavioral intelligence products

We created an end-to-end experience & continuous intelligence framework - connecting modern data collection, processing methods & analytics with AI-driven business scenarios execution.

Integrate data

Heterogeneous data sources integration                  
Products & offers
Click Streams
Calls, surveys & chats logs
Online & POS Purchases
Behavioral Profiles
3rd party data

Unify Information

Behavioral profiles & actions in one place                
Consent Management
Unified Profile View
Profile Merging policy
Deep Analytics (LTV, CLV,RFM)
Dynamic attributes
Loyalty program
Expressions & Dynamic Aggregates
Direct messaging

Manage Data & Controll Access

Security by design                                                       
ACL & Password policies
Data self-service importer
API Access Management
Stream Events Manager
Custom objects
Params manager
Advanced filtering

Analyze lifetime data streams

Actionable & advanced analytics & BI                      
Attribution modeling
Dynamic Metrics
Histograms & Trends
Sankey Diagrams
Churn Analytics
Reporting & Dashboarding

Predict, decide & personalize

ML & deep learning supported decisions                  
Scoring & Propensity
Automatic Insights
Price & Assortment Logic
AI Search
Personalised offers
Time optimizer

Automate, Optimize & Execute

Experience orchestration.                                          
A/B/X Testing
Data Transformations
External System Sync
Service Prioritization
Workflows building
Content Studios
Autonomous scenarios
Contextual Messaging

Deliver content & activate profiles

Omnichannel communication                                    
Mobile Push
Web Push
Web Dynamic Content
External Apps & POS
Social Networks
AD Networks

Create, connect and extend features

Custom extensions, APPs & dedicated portals        
Registrationas a Service
Login as a Service
Unified API Access
Templates & Cookbooks
Design System
Hot & Cold storage access
SDKs & Webhooks

Synerise is able to track every event, across every channel, for the customer - whether it's mobile, it's web, it's retail, physical presence.

All of that is signal that's being continuously collected, processed, and then in turn AI is being applied, workflows are being applied to drive the experience
Satya Nadella about Synerise
CEO, Microsoft

Apply science to behavioral data. Automatically.
Get answers for all crucial questions.

Reduce your modeling life-cycle to days instead of months


How do daily customer interactions influence their future behaviors?


How much will the customer spend in a specific category next week?


What is the customer’s expected number of trips this year?

Customer Service

What is the customer’s likelihood of using a special offer?


How much data traffic will the customer use this month?


How many diagnostic tests will the patient need this year?


How many insurance policies will the customer subscribe to this year?


How many power-ups/bundles will the gamer buy this month?


What is the customer’s projected profitability in the next quarter?


Which products/promotions/ offers/categories the customer is interested in?

Home & Furniture

How to split the customer population into behaviorally distinctivegroups?


What kind of product/category is the customer interested in and why?


Will the customer churn in the near future and what events had an impact on that?


What is the utility of customer for your business and what arethe behavioral and sociodemographic factors affecting it?


Will the customer make a purchase next week? Whatsteps need to be taken to increase the chance of purchase?


Is recent behavior of the customer inconsistent with past habits?


Are there outlier customers in the population, who might be worth looking into?

News & Publishing

Will the reader subscribe to a premium plan?
Simple. Fast. Powerful.


General-purpose model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data.
Case Study

Zabka Group Implementation

CEE largest convenience store chain - 12 million consumers (2x the population of Arizona) live
no more than 300 m from the nearest Zabka store
> 10 K
connected offline stores in real time
4 B
API requests per month
automated decisions made per month
active promotions and rewards
behavioral events tracked monhtly

Digital wallet with
flexible loyalty program

Whether you have a team of 2 or 200, our shared team inboxes keep everyone on the same page and in the loop.

Headless Experience API
for mobile APP

Measure what matters with Untitled’s easy-to-use reports. You can filter, export, and drilldown on the data in a couple clicks.
iPhone mockup

AI Predictions & Recommendations with CX Platform

An all-in-one customer service platform that helps you balance everything your customers need to be happy.

AI Personalisation &
Promotion Engine

Solve a problem or close a sale in real-time with chat. If no one is available, customers are seamlessly routed to email without confusion.

We are sharing our ideas with others!

Our research papers based on Synerise framework
Multidimensional Hopfield Network
Redefining Graph Clustering: A Convergence of Algorithms and Networks
A Foundation Model for Behavioral Event Data
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023
Real-Time Multimodal Behavioral Modeling
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022
An Efficient Manifold Density Estimator for All Recommendation Systems
International Conference on Neural Information Processing (ICONIP 2021)
Cleora: a Simple, Strong and Scalable Graph Embedding Scheme
International Conference on Neural Information Processing (ICONIP 2021)
Twitter User Engagement Prediction with a Fast Neural Model
15th ACM Conference on Recommender Systems RecSys Challenge Workshop, 2021
Node Classification in Massive Heterogeneous Graphs
ACM's Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) KDD Cup Open Graph Benchmark (OGB) Challenge Workshop, 2021
Efficient Manifold Density Estimator for Cross-Modal Retrieval
The 43th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) eCom Workshop, 2020
Modeling Multi-Destination Trips with Sketch-Based Model
14th ACM International Web Search and Data Mining Conference (WSDM) WebTour Workshop on Web Tourism, 2021
On the Unreasonable Effectiveness of Centroids in Image Retrieval
International Conference on Neural Information Processing (ICONIP 2021)
Interpretable Efficient Multimodal Recommender
Thirty-seventh International Conference on Machine Learning (ICML) Machine Learning for Media Discovery (ML4MD) Workshop, 2020
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.