A unified, modular and composable data and AI platform
The operational layer that brings together data integration, artificial intelligence and activation on real processes. The same platform powers CDP, SAIP and Anomaly Detection.
Three layers, independent yet orchestrated
Data Integration & Management
Batch and real-time ingestion, data modeling, identity resolution. Ready connectors, ETL/ELT engine, native integration with leading data lakehouses (Databricks via MLflow, Unity Catalog, zero-replication queries).
Analytics & AI Engine
Full ML lifecycle: from EDA to deployment, from monitoring to retraining. Off-the-shelf or custom models. Built-in MLOps — versioning, drift detection, alerting, audit trail.
Application & Activation
APIs and applications to bring data and models into processes: audience builder, dashboards, journey orchestration, integration with marketing, sales, service and operations.
Machine Learning Environment
- Python-native, with programmatic access to unified data and no manual exports.
- EDA, prototyping, hyperparameter optimisation and tracked experiments.
- Built-in ML interpretability: feature importance, explanations, model comparison.
- A linear path from notebook to production: deployment, monitoring and retraining managed by the same platform.
Enterprise security
Access controls, audit logs, segregation of duties, ISO 9001 and ISO 27001 compliance.
Data governance
Lineage, quality, consent management, GDPR alignment.
Scalability
Modular node-based architecture, capacity that grows linearly.
Interoperability
Open APIs, integration with leading data stacks, no technology lock-in.
- SaaS — fast activation, minimal setup. Ideal for validating the first use cases.
- Private Cloud — dedicated infrastructure, full governance, total control.
- Managed Private Cloud — dedicated infrastructure operated by Statwolf.
- On-premise and air-gapped — when data sensitivity requires it.
- SaaS → Private — start in SaaS and migrate with nothing to rewrite.
Three ways to fit into your architecture
- Data sync — data replicated into the platform for maximum independence.
- Zero-copy — direct queries on your data lakehouse, no duplication.
- Zero-compute — computation stays on your systems, the platform orchestrates and activates.
