NewEIB financing secured

Behavioral AI infrastructure

Observe, model, and act on behavioral signals in real time. Self-supervised foundation models predict next actions, personalize interaction surfaces, and automate decision logic — from sub-millisecond inference to billions of events per day.

Behavioral Data Hub
Behavioral Data Hub

Join innovative organizations using Synerise Platform

and explore hundreds of low code use cases

Banco Ripley
Carrefour
Castorama
CCC
Coca-Cola
Crocs
Decathlon
DOZ
Drogaria São Paulo
Empik
eobuwie
G2A
H&M
Homla
Hugo Boss
IKEA
InPost
JD
mBank
Media Expert
Modivo
New Balance
Nike
Orange

Why Synerise?

Built different. Built to last.

Most platforms assemble third-party tools and call it a product. Synerise builds every layer from scratch — creating the only AI infrastructure where data, intelligence, and automation are truly one system.

0% VENDOR LOCK-IN

Proprietary Technology

Every layer — from TerrariumDB to BaseModel.ai — is built in-house. No dependency on third-party AI, databases, or cloud vendors.

0K PREDICTIONS/SEC

Award-Winning AI

The behavioral foundation model wins international competitions and powers real-time predictions at enterprise scale.

<0MS LATENCY

Real-Time by Default

From event capture to profile enrichment to AI decision — everything happens in under 50 milliseconds. Not batch. Not near-real-time. Real-time.

0 DAYS TO ROI

Instant Time-to-Value

Go live in weeks, not months. Pre-built AI models, ready-made integrations, and guided onboarding mean you see measurable ROI within 90 days — not after a year-long implementation project.

0 INDEPENDENT HUBS

Composable Architecture

Deploy only what you need. Each hub works independently or together — from data ingestion to AI to automation to experience delivery.

0B EVENTS/MONTH

Planetary Scale

Built to handle 57 billion events per month, 50.19 billion API calls, and 17.68 billion workflow decisions — with linear horizontal scalability.

ISO 27001 CERTIFIED

Enterprise Security

SaaS, Private Cloud, or On-Premise. GDPR, SOC 2, zero-trust architecture. Your data, your rules, your infrastructure.

0 PLATFORM, NOT 12 TOOLS

End-to-End Platform

Replace your disconnected stack of CDPs, personalization engines, analytics tools, and marketing automation with one unified system.

Strategic Direction

Agentic AI as Strategic Direction

Synerise treats agentic automation not as an experiment, but as a core strategic pillar. It shapes how we build products, allocate resources, and define our long-term vision. This commitment is formalized across the organization — from dedicated policy frameworks to cross-functional teams driving execution.

01

Policy

A formal agentic AI policy governs how agents operate within the platform — defining boundaries, data access rules, compliance requirements, and ethical guardrails that ensure responsible automation at scale.

02

Roadmap

A dedicated agentic automation roadmap drives product development — with clearly defined milestones for agent capabilities, integration protocols, model support, and quality evaluation frameworks.

03

Teams

Cross-functional teams are organized around the agent ecosystem — combining AI research, platform engineering, product design, and domain expertise to deliver cohesive agent experiences.

04

Strategy

Agentic automation is embedded in the company's strategic plan — aligning business objectives, go-to-market positioning, and technology investments around the vision of AI-driven productivity.

The Platform

One Platform. Infinite potential.

A single, unified environment that brings data, intelligence, and automation together — powered entirely by proprietary Synerise technology.

Behavioral Data Hub

Behavioral Data Hub

Absorb & Transform Behavioral Profiles In Real Time and at Any Scale

The proprietary Terrarium data engine continuously captures, processes, and enriches behavioral data into living, evolving profiles. Each signal becomes part of a dynamic identity graph — enabling instant insight, personalization, and prediction.

Learn more about Behavioral Data Hub
Behavioral Data Hub
Integrate data

Unified Signal Ingestion Layer

Consolidate heterogeneous behavioral, operational, and contextual data streams into a single observation plane — with deterministic deduplication and sub-second latency.

ProductsOffersClick StreamsCalls & Chat LogsOnline & POS PurchasesBehavioral Profiles3rd Party SystemsWeather DataLocations
Web SDK
Mobile
POS
API
CRM
Cloud
Unified Data Layer
Real-time ingestion pipeline
Unify Information

Continuous Profile State Unification

Merge interaction sequences, transaction histories, attribute vectors, and activity timelines into a single evolving entity representation.

Consent managementUnified profile viewProfile mergingLoyalty programsReal-time messagingDeep analyticsPolicy controlDynamic attributes
Unified Profile
ID: 8f3a…e91d
Live
2,481
Events
94
Score
$1.2k
LTV
Email opened2m ago
Product viewed12m ago
Purchase #48211h ago
App session3h ago
Manage Data & Control Access

Access Control & Data Governance Primitives

Formal access control policies, audit-complete data lineage, and schema-flexible object management — built for regulatory compliance at enterprise scale.

API accessCatalog managementStream events managerSelf-service data importerCustom objectsAdvanced role filteringACL & password policies
Access Control
Admin
Analyst
Viewer
Audit Log
API key rotated · 4m ago
Role updated · 18m ago
Export blocked · 1h ago
Analyze Lifetime Data Streams

Temporal Event Stream Analysis

Move beyond static dashboards to longitudinal analysis — observing how behavioral distributions, cohort dynamics, and system states evolve over time.

Attribution modelingMetricsDynamic segmentationFunnelsHistogramsTrendsSankey diagramsChurn analyticsDashboards
Analytics Dashboard
1D7D30D
JanJunDec
Conversion Funnel
Visits
Views
Cart
Buy
Predict, Decide & Personalize

Predictive Inference & Recommendation Models

Apply supervised and self-supervised learning to infer intent, generate item-level recommendations, and personalize interaction surfaces — with continuous online model updates.

ScoringAI-driven recommendationsPredictionsPropensity modelsLookalike audiencesVisual searchBehavioral segmentation
AI Engine
Processing 65K decisions/sec
Purchase likelihood
Churn risk
Engagement score
Recommendations
Automate & Orchestrate

Orchestration & Adaptive Workflow Execution

Compose multi-step directed workflows with conditional branching, system synchronization, and feedback-driven optimization loops.

A/B/X testingData transformationsSystem synchronizationWorkflow automationContent studiosService prioritizationContextual messaging
Workflow Builder
Active
Trigger
Cart abandoned
Wait 2 hours
Delay node
A/B Split
Even split
Email
Personalized
Push
Mobile app
Deliver Content & Activate Profiles

Multi-Modal Channel Distribution

Dispatch behavioral interventions across the full channel topology — with context-aware selection, frequency optimization, and real-time signal feedback.

SMSEmailMobile PushWeb Dynamic ContentIn-AppExternal Apps & POSSocial NetworksAd NetworksLocations
Campaign Manager
Broadcasting
Email
Sent 12.4K
SMS
Sent 8.1K
Push
Sent 24.7K
Web
Live
In-App
Live
Social
Queued
Create, Connect & Extend

Extensibility Layer: APIs, SDKs & Custom Portals

A programmable extension framework — APIs, SDKs, identity services, and template systems — designed for deterministic behavior and enterprise-grade security.

Registration as a ServiceLogin as a ServiceUnified API AccessTemplates & CookbooksSAMLDesign SystemSDKs & Webhooks
API Console
GET/v4/profiles200
POST/v4/events/batch201
GET/v4/recommendations200
Webhooks
12 active
SDKs
JS · iOS · Android
SAML & SSO
Azure Entra ID · OIDC
New Capability

Brickworks

Schema-based behavioral CMS that lets you build custom data structures — loyalty programs, catalogs, campaigns — with AI-native personalization and single-API delivery.

Schema Flexibility
AI-Native Personalization
Zero-Copy Federation
Single API Delivery
Content Brick: LoyaltyProgram
Live
tier_name:"Gold"string
multiplier:2.5xfloat
benefits:[4 items]array
ai_variant:high_valuecomputed
7ms
Latency
3
Sources
v3.2
Schema
Satya Nadella
"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

CEO, Microsoft

Microsoft

Global scale

The backbone for behavioral intelligence

Synerise powers real-time data, AI, and automation at planetary scale — enabling organizations to understand and act on behavior across people, systems, and environments.

57B

Events collected per month

229B

Queries to the TerrariumDB per month

5.53B

AI recommendations, searches & predictions per month

1.85B

Page visit events collected per month

1.5B

Mobile view events collected per month

129TB

Data sent via API per month

>1.19B

Unique dynamic content generated per month

~207K

Queries to the TerrariumDB per second at peak

35K

API calls per second at peak

65K

AI decisions per second at peak

150B EUR

GMW processed annually

1.97B

Hyper-personalized messages sent per month

17.68B

Decisions in workflows per month

50.19B

API calls per month

6B

Behavioral profiles scanned daily

16,400

Kubernetes pods

750+TB

Disk size

890+

Kubernetes nodes

420+

Virtual machines

40M+

SKUs handled in feeds

71+TB

RAM

14,400+

vCPU

114

Database clusters

42B+

Rows in Postgres clusters

3K

Active operators on the Synerise platform

70+

Active Partners worldwide

1,000+

Synerise Certificates issued

>540

Production Workspaces

>220

Organizations

49

Countries from 6 continents

+138 % vs HSTU — New SOTA in sequential recommendation
basemodel.ai

The behavioral foundation model.

A single self-supervised model that ingests your entire data warehouse and turns any behavioral question about any individual into a production prediction — in hours, not months, without scaling your team.

8 B+
Events per training run
18 M+
Clients per training run
<1 ms
Classification latency
9 ms
6 M-item recs per user (1× H100)

The Pre-Training & Fine-Tuning Pipeline

From raw behavioral data to production-ready predictions — a four-stage pipeline that eliminates traditional ML complexity.

Phase 1Cleora-NX

Proprietary Hypergraph Embeddings

Each behavioural event is a hyperedge linking everyone who took part — user, product, category, brand, timestamp — simultaneously. Cleora-NX — Synerise's proprietary graph-embedding engine, building on the research published at ICONIP 2021. Production-only; with substantial performance and functionality extensions — not open-sourced. Runs in time proportional to the number of hyperedges; converges in a handful of iterations on graphs with billions of interactions in minutes.

Cleora-NX — proprietary, never published; the open-source Cleora is its predecessor
Multi-modality support and temporal-interaction extensions on top of the public Cleora update
Deterministic — no training variance, no GPU required
Scales to graphs with millions of nodes and billions of interactions
Phase 2TREMDE

Proprietary Density Sketches

Cleora-NX embeddings (plus any text, image, or tabular embeddings) are aggregated into fixed-size density sketches by TREMDE — Synerise's proprietary, temporally-aware extension of the open-source EMDE algorithm (Synerise, ICONIP 2021). TREMDE produces sparse codes that are composable under summation — adding two entities' codes yields the code for their combined behaviour — and adds temporal-interaction modelling, modality-specific extensions, and other internal improvements not described in the original EMDE paper. Each modality is sketched independently and concatenated; the sketch shape itself is auto-tuned by the pipeline from your data — no manual config, no hyperparameter sweep. New items with content features get meaningful codes immediately — true cold-start with no retraining.

TREMDE — proprietary, never published; the open-source EMDE is its predecessor
Adds temporal-interaction modelling and per-modality extensions on top of public EMDE
Sketch shape auto-tuned from your data — zero manual config
Multimodal: graph, text, image, tabular features
Phase 3Foundation

Customized FFN Backbone

Not a textbook MLP. A customized feed-forward backbone purpose-built for behavioural sequences, carrying proprietary inductive biases tuned to the structure of event data. No attention, no recurrence — yet it captures temporal structure, cross-feature interactions, and modality fusion that an off-the-shelf MLP cannot. The objective is distributional matching: each depth of the target sketch is normalized to sum to 1, and the loss is the cross-entropy between predicted and true depth distributions, averaged across depths and modalities.

Captures temporal structure without recurrence or attention
Models cross-feature interactions and modality fusion in one pass
Single foundation transfers across scenarios — no per-task retraining
Distributional cross-entropy across depths and modalities
Phase 4Serving

Proprietary Scoring · Ray Serve

Candidate items are encoded into TREMDE sparse codes (composable under summation). Predictions are scored using a proprietary aggregation method optimized for behavioral sketches. Sub-millisecond classification latency; 9 ms per user to score the full 6 M-item rel-avito catalog on 1× H100. Served via Ray Serve.

Proprietary aggregation method optimized for behavioural sketches
Sub-millisecond latency for classification / regression
9 ms per user to score 6 M-item catalog on 1× H100
Ray Serve deployment for batch and online scoring

Embedding Space Visualization

BaseModel.ai projects every customer into a shared behavioral embedding space. Similar behaviors cluster together — enabling instant similarity search, segmentation, and anomaly detection.

High-Value Shoppers
At-Risk Churners
New Users
Power Users
Seasonal Buyers
Deal Seekers

Behavioral Clustering

Users with similar browsing, purchasing, and engagement patterns naturally cluster in embedding space — no manual segmentation rules needed.

Real-Time Drift Detection

When a customer's embedding vector moves toward a different cluster (e.g., from "loyal" to "at-risk"), the system triggers proactive interventions.

Analogical Reasoning

The embedding space supports vector arithmetic: "High-value shopper" minus "frequent buyer" plus "new user" reveals emerging high-value prospects.

Cross-Entity Relationships

Products, campaigns, and channels exist in the same space as users — enabling nearest-neighbor recommendations and content-user matching.

cleora.ai

Cleora — All Random Walks. One Matrix Multiply.

A Rust-powered graph embedding engine that computes the exact distribution of every possible walk in a single sparse matrix power — no random walks, no negative sampling, no GPU. The result: deterministic, production-grade embeddings from one CPU core, with the highest accuracy on real-world graphs where other libraries score in the single digits.

The open-source Cleora is the published predecessor of Cleora-NX — Synerise's proprietary, multi-modal, temporally-aware engine that provides the pre-training signal inside BaseModel.ai. Both rest on the same deterministic update T_{k+1} = normalize(P · T_k) introduced in the Cleora paper (ICONIP 2021).

Read more about Cleora.ai View on GitHub

240×

Faster than GraphSAGE

50×

Less memory than NetMF

5 MB

Total install size

0

GPUs required

No Sampling, No Training

Captures all walk distributions exactly via matrix powers — no random walks, no skip-gram training, no stochastic noise. Same input always produces the same output, guaranteed.

240× Faster Than GraphSAGE

Zomato reported embedding generation in under 5 minutes with Cleora vs. ~20 hours with GraphSAGE on the same dataset. A Rust core with adaptive parallelism makes every CPU cycle count.

Stable & Inductive

Embeddings are stable across runs and support inductive learning — new nodes can be embedded without retraining the entire graph. Production-ready from day one.

Heterogeneous Hypergraphs

Natively handles multi-type nodes and edges, bipartite graphs, and hypergraphs. TSV input with typed columns like `complex::reflexive::product` — no preprocessing needed.

ego-Facebook (SNAP · 4K nodes · 88K edges)

AlgorithmAccuracyTimeMemory
Cleora0.9901.23 s22 MB
Node2Vec0.95867.9 s—
NetMF0.95728.8 s1,098 MB

Cleora hits 99.0 % accuracy and uses 50× less memory than NetMF. Source: SNAP ego-Facebook (4K nodes · 88K edges). snap.stanford.edu

Cleora (open source)Predecessor algorithmCleora-NX (proprietary)
Raw eventsHypergraph constructionCleora-NX
Cleora-NXBehavioral embeddingsBaseModel pre-training

BaseModel.ai vs. the Alternatives

See how BaseModel.ai compares to traditional ML pipelines and the latest generation of LLM-powered AI tools.

CapabilityTraditional MLLLMs / AI AgentsBaseModel.ai
What it outputs
One model per business question
Text, code, or analysis
Production-ready predictive models
Data scale
Curated datasets (GB)
Context window (128K–1M tokens)
Petabyte-scale data warehouses
Feature engineering
Months of manual work per model
Can generate feature code — still single-purpose
Fully automated from raw events
Time to first model
3–6 months
Hours (but builds traditional pipelines)
12 h foundation training on 1× A100 for ~8 B events; scenario fine-tune in hours
Population modeling
Aggregate statistics and segments
One user at a time via prompts
Individual-level models for entire population
Cross-domain transfer
Not possible — each model is siloed
Not applicable — no persistent learned state
Built-in — one model serves all domains
Knowledge persistence
Retrain from scratch for each question
No memory between sessions
Foundation reused across every scenario
Cold-start handling
Requires minimum data thresholds
Requires detailed prompt context
Inductive sketches from first interaction
Team required
5–15 ML engineers + data scientists
Data scientist + prompt engineer
Data engineer or ML engineer; a single ML engineer suffices for typical deployments
Latency at scale
50–500 ms typical
1–30 seconds per generation
Sub-ms classification; 9 ms/user to score 6 M items on 1× H100

Blazingly fast. No clusters needed.

Train on ~18 M clients / ~8 B events in 12 h on a single A100. Serve classification and regression at sub-millisecond latency, and 6 M-item recommendations in 9 ms per user on 1× H100. No distributed infrastructure required.

RelBench MAP — BaseModel vs. best baseline (higher is better)

TaskBaseModelBaselineBaseline name
rel-amazon review2.531.63ContextGNN
rel-amazon rate3.062.25ContextGNN
rel-hm purchase3.672.93ContextGNN
rel-avito ad-visit4.683.94RelGNN

Source: BaseModel paper, RelBench (12 tasks). BaseModel matches or exceeds the best published baseline on 10 of 12 tasks; four largest wins shown.

<1 ms

Sub-millisecond latency

Per-request inference time for classification and regression at production scale.

18 M+
Clients per training run
~8 B events on 1× A100 in 12 h
1
Single GPU
No cluster required
<1 ms
Classification latency
Sub-ms per request
9 ms
6 M-item recs
Per user on 1× H100

Real-World Deployment Impact

Across the Synerise platform powered by BaseModel — numbers from 340+ production deployments.

340+
Production Deployments
Active enterprise deployments across 4 continents
2.8B
Daily Predictions
Real-time predictions served every day
+23%
Avg. Revenue Lift
Average incremental revenue for retail customers
+8.2pp
Model Accuracy Gain
Average AUC improvement over incumbent ML models
14 days
Deployment Speed
Median time from kickoff to production predictions
67%
Cost Reduction
Reduction in ML infrastructure and team costs

One model. Every question. Any industry.

Select an industry to see what BaseModel.ai can answer — out of the box.

General

"How do daily customer interactions influence their future behaviors?"

AI
ML
DL

360° behavioral understanding

Zero-shot ready

Built different

Six architectural principles that make BaseModel.ai the most advanced behavioral AI system ever built.

Reusable Foundation

The foundation model is trained once on your behavioural data. Every new scenario — churn, LTV, recommendations — reuses those embeddings via a quick fine-tune; no full retraining for each question.

Multimodal Density Sketches

Graph embeddings, text, images, and tabular features are sketched into a single fixed-size representation per entity — built on-the-fly with cost linear in interactions.

Zero-Shot Predictions

Answer behavioural questions you've never explicitly modelled — such as patient readmission risk, subscriber upgrade propensity, or employee flight risk — by defining a target function and reusing the foundation embeddings.

Real-Time Inference

Sub-millisecond classification and regression latency; recommendations score the full 6 M-item rel-avito catalog in 9 ms per user on 1× H100. Served via Ray Serve for batch and online workloads.

Self-Hosted by Design

Deploy inside Snowflake Container Services, Databricks, or your own GPU cluster. Behavioural data never leaves your environment; no shared model corpus across customers.

Cross-Domain Transfer

Behavioural knowledge learned in one domain transfers to another. Purchase patterns improve fraud detection; engagement signals sharpen churn predictions — cross-domain transfer that no single-purpose model can replicate.

Model Governance & Explainability

Enterprise-grade governance built into every layer — from training data lineage to production prediction auditing.

YAML-Defined Targets

Every fine-tuned scenario is described by a single YAML config — source tables, target function, training window. Reproducible by design; rerunning the config rebuilds the same model.

Configurable Sample Weights

Per-event sample weights and target balancing controls let teams tune fairness and recency trade-offs at training time, with full visibility into how examples are weighted.

Self-Hosted Data Sovereignty

Deploy in Snowflake Container Services, Databricks, or your own GPU cluster. Behavioural data and embeddings stay inside your environment — no shared model corpus across customers.

Reproducible Configs & Lineage

Foundation training and every fine-tune run are pinned to a YAML config and a connector schema, so you can trace any prediction back to the exact tables, time window, and parameters that produced it.

Versioned Foundations & Adapters

Foundation checkpoints and per-scenario fine-tunes are versioned independently. Roll back a scenario without retraining the foundation; promote a new foundation when you're ready.

Monitoring Hooks

Streamed metrics for input distribution, prediction confidence, and business KPI correlation — wire into your existing observability stack to catch drift early.

Recognized by the scientific community

BaseModel.ai is built on Synerise's published research — the Cleora and EMDE papers (ICONIP 2021) and the BaseModel preprint — and rolls up under Synerise's broader 40+ publications across NeurIPS, KDD, ICONIP, and ACM RecSys.

Preprint 2025

BaseModel: A Foundation Model for Behavioral Data

The BaseModel.ai paper. Defines the foundation-model formulation for behavioural event streams and the benchmark suite reported on /research. Production BaseModel runs Cleora-NX, TREMDE, and a customized FFN backbone — proprietary internal extensions of the published Cleora and EMDE foundations, not described in the paper.

ICONIP 2021

Cleora: A Simple, Strong and Scalable Graph Embedding Scheme

Synerise's published hypergraph-embedding scheme. Defines the deterministic, parameter-free update T_{k+1} = normalize(P·T_k). The open-source predecessor of Cleora-NX, the proprietary engine that powers BaseModel.ai in production.

ICONIP 2021

An Efficient Manifold Density Estimator for All Recommendation Systems

The EMDE paper. Introduces compact density sketches whose sparse codes compose under summation. The open-source predecessor of TREMDE, the proprietary, temporally-aware density-sketch engine inside BaseModel.ai.

Make a single data scientist 10× more effective.

BaseModel.ai reduces the modeling lifecycle from months to days and supercharges behavioral ML at every level.

Mariano Gomide de Faria
"Synerise is a hidden pearl from Krakow, Poland. The Synerise product BaseModel is the world's most advanced private foundation model for behavioral data. Synerise is a powerful and efficient way for your company (tech, SI, retail, manufacturer or brand) leapfrog the AI application world, producing fast results and avoiding spending millions of CAPEX on your data engineering model. I am proud to see the most advanced tech companies raised and born in emerging markets. We are pleased to choose Synerise as the VTEX AI infrastructure engine. We follow heads down the mission to be the backbone for connected commerce. The AI functionalities VTEX will be able to deploy with Synerise is disruptive."

Mariano Gomide de Faria

Co-CEO, VTEX

VTEX

TerrariumDB

TerrariumDB.
The behavioral data engine.

TerrariumDB captures, resolves, and activates every behavioral signal in real time. It's the living data foundation that powers BaseModel.ai — turning raw events into dynamic, evolving customer identities at massive scale.

57B+
Events per month
<50ms
Processing latency
6B+
Behavioral profiles
99.99%
Uptime SLA

From event to insight in milliseconds

A four-stage pipeline that transforms raw behavioral noise into actionable, real-time customer intelligence.

01

Capture

Event Ingestion

Every behavioral signal — from clicks and page views to transactions and API calls — is captured in real time via SDKs, APIs, or server-side connectors.

02

Enrich

Identity Resolution

Raw events are matched to unified profiles through deterministic and probabilistic identity resolution. Cross-device, cross-channel, cross-session — one identity.

03

Profile

Dynamic Aggregation

Every resolved event updates living behavioral profiles in real time. Aggregates, sequences, and computed attributes refresh within milliseconds.

04

Activate

Graph & Stream

Enriched profiles feed the identity graph and stream to downstream systems — powering BaseModel.ai, decisioning engines, and personalization in real time.

Built for behavioral data at scale

Six core capabilities that make TerrariumDB the most advanced real-time behavioral data engine available.

Real-Time Ingestion

Captures every behavioral signal the moment it happens — clicks, transactions, page views, API calls — with zero lag between the event and your data layer.

Living Profiles

Data isn't stored as static records. Every event enriches a dynamic, evolving behavioral identity that reflects who the customer is right now — not who they were last week.

Unified Identity Graph

Every event enriches a unified identity graph across channels and devices. Anonymous sessions, logged-in users, and cross-device journeys merge into a single truth.

Sub-Second Processing

From event capture to profile enrichment in under 50 milliseconds. Real-time decisioning depends on real-time data — TerrariumDB delivers both.

Schema-Free Ingestion

No predefined schemas needed. Send any JSON payload and TerrariumDB automatically indexes, enriches, and makes it queryable. Data models evolve in real time as your business does.

Event Streaming & CDC

Built-in change data capture, webhooks, and real-time data connectors. Stream enriched events to any downstream system — data warehouses, ML pipelines, or activation engines.

Designed for horizontal scalability

Every layer of the stack is built to scale linearly — from stateless services to sharded databases and independent compute zones.

Horizontal Scalability

  • Stateless services
  • Load Balancing
  • Microservices architecture

Database Scalability

  • Core technologies linearly scalable (TerrariumDB, Kafka, ScyllaDB)
  • Data sharding by profileId

Event-Driven Reactive Architecture

  • Data streaming via Kafka
  • Asynchronous processing in stateless microservices

Auto-Scaling Infrastructure

  • Containerization (Docker)
  • Kubernetes native workloads
  • Horizontal pod autoscaler
  • Cloud-Based Auto-Scaling

Monitoring

  • Continuous monitoring with long history

Noisy Neighbor Solution

  • Separate Kafka topics per workspace
  • Dedicated compute zones for workflows and AI recommendations
  • Zones scale independently

The data foundation for BaseModel.ai

TerrariumDB isn't just a database — it's the living substrate that feeds the world's first behavioral foundation model. Every event captured, every profile enriched, and every identity resolved by TerrariumDB becomes training signal for BaseModel.ai.

Real-time behavioral sequences flow directly into model training
Identity graph ensures embedding quality across fragmented journeys
Schema-free architecture adapts to new data sources without re-engineering
Sub-50ms enrichment enables real-time inference at prediction time
Live Pipeline
Events Captured
1.4B/day
Profiles Updated
892M/day
Identity Matches
234M/day
Avg Latency
23msp99

Enterprise-grade architecture

Built from the ground up for mission-critical behavioral data workloads at planetary scale.

Multi-Region Deployment

Deploy in any cloud region with automatic data residency compliance. GDPR, CCPA, and LGPD ready out of the box with configurable data sovereignty controls.

Guaranteed Durability

Write-ahead logging, multi-replica synchronization, and point-in-time recovery ensure zero data loss. Every event is durable the moment it's acknowledged.

Security & Compliance

SOC 2 Type II certified with end-to-end encryption at rest and in transit. Role-based access control, audit logging, and data masking built into every layer.

Your data, alive and ready.

See how TerrariumDB transforms raw events into real-time behavioral intelligence — powering predictions, personalization, and decisioning at scale.

Synerise AI Research

Science-driven AI

World-class research that wins global competitions and advances the state of the art in recommendation systems, graph learning, and behavioral AI.

50+
Published papers
15+
Competition wins
1,200+
Citations

Featured Wins

Dominating global AI competitions

KDD Cup — 1st place
ACM RecSys Challenge — Multiple wins
Booking.com Challenge — 1st place
Rakuten Data Challenge — 1st place
OGB Large-Scale Challenge — Top ranking
"Synerise has built one of the most impressive applied AI research teams I've encountered. Their work on graph embeddings and behavioral modeling is advancing the state of the art."

Prof. Jure Leskovec — Stanford University

Latest from SAIR

Research8 min read

BaseModel vs TIGER for sequential recommendations

The comparison between BaseModel and TIGER reveals substantial differences in their architectural choices and performance.

Research8 min read

BaseModel vs HSTU for sequential recommendations

To evaluate BaseModel against HSTU, we replicated the exact data preparation, training, validation, and testing protocols.

Research8 min read

Fourier Feature Encoding of numerical features

Pre-processing raw input data is a very important part of any machine learning pipeline, often crucial for end model performance.

Engineering12 min read

EMDE vs Multiresolution Hash Encoding

When we created our EMDE algorithm we primarily had in mind the domain of behavioral profiling.

Future6 min read

Why We Need Inhuman Artificial Intelligence

We continuously wonder how much longer it will take until AI reaches human skill level in these tasks.

Tools8 min read

Efficient integer pair hashing

Mental models are simple expressions of complex processes or relationships.

Colloid Design System

The design language
behind the platform.

Colloid is Synerise's proprietary, open-source design system — a library of 117 independently versioned React component packages, 1,189 custom icons, and a complete theming infrastructure powering every screen of the platform.

Built with TypeScript, Styled Components, and react-intl from day one. Not a wrapper around Material UI or Ant Design — a ground-up system designed for the unique demands of behavioral data interfaces, real-time dashboards, and AI-driven workflows.

117

Component packages

1,189

Custom icons

4

Icon size variants

100%

TypeScript coverage

Explore Design System View on GitHub
Colloid Design System — Synerise UI components

117 React Packages

Each component is an independently versioned npm package — from ds-button to ds-table to ds-wizard. Install only what you need.

1,189 Custom Icons

A proprietary icon library across 4 size variants (M, L, XL, color) — purpose-designed for data platforms, analytics, and behavioral AI interfaces.

Open Source Storybook

Every component is documented and interactive in a public Storybook instance. Designers review, engineers build, QA tests — from a single source of truth.

TypeScript Native

Written entirely in TypeScript with predictable static types. Full IntelliSense, compile-time safety, and zero ambiguity across every component API.

Styled Components

Theme-driven styling with Styled Components. Every token — color, spacing, typography, elevation — flows from a central theme object for instant theming.

i18n by Default

Internationalization built in via react-intl. Every label, placeholder, and message is translatable — supporting global enterprise deployments out of the box.

Component Showcase

Colloid Design System — Live Preview

Visual replicas of @synerise/ds-* components, matching the Colloid design tokens and patterns used in production.

ds-button variants

Button sizes

import DSButton from '@synerise/ds-button';
<DSButton type="primary">Primary</DSButton>

Components follow Colloid design tokens — spacing, colors, and typography match the production design system.

Open Storybook

Real Components from the Repository

Every component below is a real, independently published @synerise/ds-* npm package — sourced directly from the open-source GitHub repository.

Data Entry18
InputInputNumberSelectAutocompleteCascaderCheckboxRadioSwitchSliderDatePickerDateRangePickerTimePickerColorPickerEmojiPickerFileUploaderCodeAreaInlineEditSubtleForm
Navigation12
MenuAppMenuNavbarSidebarSidebarObjectTabsCardTabsStepperPaginationBreadcrumbsWizardPageHeader
Data Display17
TableListListItemCardCardSelectMetricCardAvatarAvatarGroupBadgeTagTagsStatusDescriptionTypographyStepCardInformationCardItemsRoll
Feedback16
AlertInlineAlertModalDrawerToastPopconfirmPopoverTooltipResultLoaderSkeletonProgressBarSectionMessageBannerBroadcastBarConfirmation
Layout & Structure12
GridFlexBoxLayoutPanelPanelsResizerDividerBlockTrayToolbarFooterScrollbarActionArea
Advanced17
FilterItemFilterItemPickerFactorsOperatorsConditionLogicManageableListSortableCollectorColumnManagerMappingInsightSearchBarContextSelectorCompletedWithinEstimation
Open Source · MIT License

Explore the living documentation.

Every component ships with interactive Storybook documentation — showing variants, states, props, accessibility notes, and real-world usage examples. Designers review. Engineers build. QA tests. All from the same source.

Colloid isn't just a UI kit — it's the shared language between product, design, and engineering at Synerise. It encodes years of learnings about building interfaces for behavioral data, AI predictions, and real-time automation at planetary scale.

View on GitHub synerise-design
@synerise/ds-*

// Install individual packages

yarn add @synerise/ds-core

yarn add @synerise/ds-button

yarn add @synerise/ds-table

// Wrap your app

import { DSProvider } from '@synerise/ds-core'

import Button from '@synerise/ds-button'

<DSProvider>

<Button>Click Me!</Button>

</DSProvider>

Enterprise Security

Enterprise-grade security.
Your terms.

Deploy Synerise on your infrastructure, in a private cloud, or fully managed SaaS — with SOC 2, GDPR, and end-to-end encryption at every layer.

SaaS

Fully managed cloud

Private Cloud

Dedicated infrastructure

On-Premise

Your data center

SOC 2 Type II

Audited security controls

GDPR

EU data protection

CCPA

California privacy compliance

ISO 27001

Information security

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