Choosing a MarTech platform today comes down to a single question: are you buying a set of integrated modules, or one data and AI engine built from the ground up. Synerise is a Customer Data & AI platform that combines its own database, its own foundation model and omnichannel orchestration in a single system - instead of stitching together several separate tools. Below we compare it with the largest MarTech platforms on the market and show 50 features that give it the edge.
How does Synerise differ from other MarTech platforms?
Most MarTech platforms were created by combining acquired products or run on general-purpose databases. Synerise is built around its own TerrariumDB database and its own BaseModel.ai foundation model, trained on behavioral data - not on language. This makes it possible to predict customer behavior (churn, LTV, purchase propensity) without a data science team and without fine-tuning models.
Three differences that make the case:
- Proprietary core technology - a database and an AI model written from scratch for behavioral scale, not assembled from off-the-shelf building blocks.
- Real-time with no compromises - every event reaches the profile instantly and immediately influences recommendations and decisions.
- AI without coding - predictions and recommendations are built in, ready to be used by the marketing team.
What is the scale of Synerise's operations?
Processing scale is the hardest proof of a platform's maturity. Synerise processes over 31 billion events per month, handles over 90 billion database queries and makes 12 billion automated AI decisions in the same period. On top of that come 3.8 billion AI recommendations and forecasts per month, over 28,000 API requests per second at peak, and over 84 TB of data transferred through the API each month - all while operating in 49 countries and across more than 500 production implementations.
50 features that give Synerise the edge over the competition
AI and foundation models
The most important difference between Synerise and the rest of the market lies in the AI layer. Synerise is the only MarTech platform with its own foundation model trained on behavioral data rather than on language (LLM).
| Feature | Edge | Edge-case scenario |
|---|---|---|
| Foundation Model (BaseModel.ai) | The only MarTech with its own model for behavioral data | Behavior prediction without fine-tuning |
| Cleora.ai - graph embeddings | Open-source, deterministic, no GPU, orders of magnitude faster | Recommendations from relational graphs |
| EMDE algorithm | A density estimator in real time at massive scale | Profiling millions of users live |
| AI Predictions (Churn, LTV, Propensity) | Built in, zero coding | "Customer X: 87% chance of churn in 14 days" → auto-action |
| AI Recommendations (graph-based) | Multimodal embeddings: text + image + behavior | Recommending shoes based on a photo and history |
| Semantic AI Search | NLP + visual search + personalization in one | "Something warm for winter" → personalized results |
| Visual Product Discovery | Image search integrated with the profile | Upload a photo → products matched to your taste |
| Lookalike Audiences (AI) | Segments based on embeddings, not rules | "Find 100k customers similar to the top 1000 VIPs" |
| AI Time Optimizer | The best send time per user | Email to one person at 7:23, to another at 21:15 |
| A/B/X Testing with AI | Auto-allocation of traffic to the winner in real time | Testing 5 variants → AI finishes in 2 h with a winner |
Data platform and the TerrariumDB engine
| Feature | Edge | Edge-case scenario |
|---|---|---|
| TerrariumDB - proprietary database | Sub-millisecond latencies, 90 billion+ queries/month | A customer profile in under 1 ms at peak |
| Real-time Behavioral Profiles | Every event instantly in the profile | Add to cart → the very same second changes recommendations |
| Cross-device Identity Resolution | Merging profiles without cookies | Desktop + mobile + store = one profile |
| 6 billion profiles scanned daily | A scale unattainable without infrastructure expansion | Daily churn scoring across the entire base |
| Expressions & Aggregates | No-code calculations on live data | "How much has the customer spent in category X over 90 days?" - live |
| Catalogs (Item Feed) | A dynamic catalog feeding the AI | Automatic price and stock updates in recommendations |
| GDPR Consent Management | Consent management built in, not an add-on | Automatically pausing communication after consent is withdrawn |
| Data Import/Export | CSV, API, BigQuery, Pub/Sub, Azure, webhooks | A nightly data import from a legacy ERP |
| Event Schema Management | Full control and validation of the event structure | Standardizing data from 50 sources without ETL |
| 84 TB+ of data through the API/month | Enterprise-class throughput | Integration of POS + e-commerce + mobile + IoT |
Automation and orchestration
| Feature | Edge | Edge-case scenario |
|---|---|---|
| Visual Workflow Builder | Drag & drop, multi-branch, AI nodes | Journey: browse → abandon → wait 2 h → push → email |
| 12 billion automated decisions/month | A scale the competition cannot reach combined | Black Friday: millions of decisions per second |
| Trigger-based Automation | Event → action in under 100 ms | Entering the store (beacon) → push with an offer |
| Omnichannel Orchestration | Email + SMS + Push + WhatsApp + Web + In-App in one | An email → SMS → push sequence based on reactions |
| Voucher & Coupon Engine | Code pools, limits, uniqueness, expiration | 500k unique codes on Black Friday |
| Promotions Engine | Rules by tier, segment, product | "10% off shoes for Gold, max 3 uses" |
| Dynamic Content Personalization | 1 billion+ dynamic pieces of content/month (Jinjava) | The same email, 50k content variants |
| MCP Server (AI Agent Protocol) | Native integration with AI agents | An agent builds segments and workflows through conversation |
| Webhook & API Nodes | Calling any API from a workflow | Workflow → pricing API → set a discount → push |
| Connections (Unified Auth) | Central credentials for integrations | One place for Google, Meta and Twilio keys |
Channels and experiences
| Feature | Edge | Edge-case scenario |
|---|---|---|
| In-App Messages (HTML/JS) | Games, quizzes, forms without an app update | An advent calendar with mini-games inside the app |
| Web Layers & Pop-ups | Behavioral targeting + AI + A/B | A pop-up only for propensity to buy above 70% |
| Mobile SDK (iOS + Android) | Tracking + push + in-app + geolocation | The full journey in the mobile app |
| Web SDK | A lightweight tracker + front-end personalization | Recommendations without burdening the backend |
| Email Templates (Jinjava) | Conditional logic in templates | "If tier=Gold show the VIP section, else standard" |
| WhatsApp Communication | A native channel in the workflow | An abandoned cart reminder via WhatsApp |
| Web Push | Browser push with AI personalization | "Your favorite sneakers are back in stock" |
| SMS Gateway | Built in or via integration | A transactional SMS with points after a purchase |
| Landing Pages | No-code with personalization | A campaign page per segment |
| Screen Views (Mobile) | Personalized screens in the app | A different home screen for every customer |
Analytics and intelligence
| Feature | Edge | Edge-case scenario |
|---|---|---|
| Real-time Dashboards | Live dashboards to share externally | The CMO sees campaign revenue live |
| Conversion Funnel Analysis | Multi-step funnels with drop-off analysis | "Where are we losing them: PDP → Cart → Checkout?" |
| RFM Segmentation | Automatic Recency/Frequency/Monetary segmentation | Exporting RFM segments to GA |
| Attribution Modeling | Linking AI recommendations to conversion | "How much revenue did the AI recommendation drive?" |
| Predicted LTV Export | Activating CLV data in other systems | Export to Google Cloud Storage |
| Profile & Event Aggregates | Sum, Avg, Min, Max, First, Last, Median | AOV and points balance calculated live |
| Expressions with functions | Abs, Floor, Round, If/Else, Concat, dates | Custom metrics without code |
| Custom Events | User-defined events | Tracking bottle returns, recycling, reviews |
| Segmentations | Groups by attributes, events, aggregates, expressions | Lookalike and campaign targeting |
| Operators in analytics | String, Number, Boolean, Date, Array, Null | Precise profile filtering |
Synerise vs. other MarTech platforms - the key differences
| Area | Synerise | A typical MarTech platform |
|---|---|---|
| Database | Proprietary (TerrariumDB), sub-ms | General-purpose or acquired |
| AI model | Own behavioral foundation model | An LLM or bought/integrated models |
| Predictions (churn, LTV) | Built in, no-code | Require a data science team |
| Real-time | Event → profile → action instantly | Often batch or delayed |
| Scope | CDP + AI + orchestration in one | Combined modules from different products |
FAQ - Synerise and MarTech
What is Synerise?
Synerise is a Customer Data & AI platform that combines data collection (CDP), its own AI foundation model and omnichannel communication orchestration in a single system. It lets you personalize experiences, predict customer behavior and automate campaigns without combining many separate tools.
Is Synerise better than other MarTech platforms?
Synerise stands out in that it has its own database (TerrariumDB) and its own foundation model trained on behavioral data, whereas the competition usually integrates modules and requires a data science team for predictions. For companies that need real-time and AI without coding, Synerise offers an advantage in implementation time and maintenance cost.
What is BaseModel.ai?
BaseModel.ai is Synerise's proprietary foundation model, trained on behavioral data rather than on language like classic LLMs. It is used to predict customer behavior - the risk of leaving (churn), lifetime value (LTV) and purchase propensity - without the need for fine-tuning.
How much data does Synerise process?
Synerise processes over 31 billion events per month, handles over 90 billion database queries and makes 12 billion automated AI decisions on a monthly scale, operating in 49 countries and across more than 500 production implementations.
Does Synerise require a data science team?
No. AI predictions (churn, LTV, propensity) and recommendations are built in and ready to be used by the marketing team without coding and without building your own models. This is one of the main differences from platforms that require a separate team of analysts.
See how much Synerise can bring to your business
As one of Synerise's key implementation partners in Poland, we help translate these capabilities into a concrete business outcome. Calculate the potential with our CLV, churn cost and loyalty program calculators, read about churn prediction, or book a call to discuss the implementation step by step.