Axonis Decision Intelligence

Axonis deploys lightweight compute nodes directly next to your data sources. These nodes perform everything locally: feature engineering, transformation, training, and inference. Only encrypted model updates or aggregated statistics leave the boundary, never raw data. This dramatically reduces your security footprint compared to centralization.

Each site trains models locally on its own production data. Axonis securely aggregates the model parameters, builds a global model, and returns updated model weights to each location. Axonis also maintains compliance and data-level security at every step of the lifecycle.

• Horizontal Federated Learning: Same schema, different rows (stores, regions, hospitals).
  
  
• Vertical Federated Learning: Different attributes for the same entities split across systems or companies (bank risk + merchant data).
  
  
• Federated Transfer Learning: Used when both features and samples differ but shared learning improves accuracy.
  
  

Axonis supports all three natively and does so on real production data. Most federated learning tools do not.

• Axonis computes statistics locally (covariances, means, summaries), securely aggregates them across sites, and derives global values without exposing raw data. This enables advanced transformations - PCA, normalization, embeddings - while keeping all sensitive data in place.

Axonis is designed for interoperability, not replacement. It works with your existing ecosystem:

• ML Frameworks: PyTorch, TensorFlow, JAX, XGBoost, Keras‍
• Pipeline Tools: Airflow, Kubeflow, MLflow‍
• Access & Identity: Keycloak
• ‍Serving: Built-in serving or export to your existing cloud-hosted serving stack

You retain full ownership and portability of your trained models and can serve them in any environment. Axonis does not require proprietary serving infrastructure. 

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Inference happens next to the data, not across a network. This avoids latency, cloud routing, and regional bottlenecks. The result is faster, more predictable performance, especially in edge or multi-region scenarios.

Axonis eliminates multi-TB data transfers, reduces ETL and cleaning, and trains models across all sites in parallel. Instead of waiting months to engineer production integration, models can be served immediately because Axonis is already wired into live systems.

By keeping data where it is:

• No data duplication
• No egress fees
• No cloud storage of sensitive data
• Minimal cloud compute for preprocessing
• Reduced cybersecurity surface area (fewer systems to secure)

Axonis customers frequently cut cost by 50–75% compared to centralized AI architectures, based on Axonis customer deployments.

Yes. Axonis was built for everything from a single on-prem GPU to massive cloud clusters. It handles edge nodes, air-gapped boxes, hybrid clouds, and large multi-region federations.

Yes. Axonis is built on zero-trust architecture proven in DoD and U.S. intelligence community deployments – environments where security requirements exceed every commercial compliance framework. The platform was hardened inside T2S Solutions serving IC programs before Axonis launched commercially in December 2025. Security requirements in those environments are stricter than any commercial sector.  It is often recognized as a “superset” of all commercial compliance frameworks.

Key security features include:

• Data-level security (fine-grained field-level controls)
• Zero-trust architecture
• Localized model training (no raw data ever leaves)
• Secure aggregation
• Full auditability, monitoring, and logging

Encryption in transit and at rest

Axonis aligns with global standards including: HIPAA, PHI/PII, PCI-DSS, HITRUST HIGH, SOX, ICD-503 (the security directive governing U.S. intelligence community systems), GDPR, CCPA, LGPD, POPI, SOC 2, ISO 27001.

Federated learning also reduces compliance risk by minimizing data movement.

Nearly any system including commercial, legacy, open source, or proprietary environments:
S3, GCS, Azure Blob, PostgreSQL, MySQL, Oracle, Snowflake, BigQuery, Redshift, Hive, HDFS, MongoDB, Elasticsearch, FTP/SFTP, REST APIs, and many more.

Axonis handles all modalities: structured, unstructured, logs, time-series, text, images, video, sensor/telemetry streams, and multimodal combinations.

Yes. Modelers and data scientists can:

• Explore and visualize data
• Perform distributed feature engineering
• Build and evaluate models
• Deploy models instantly to production systems
  
  

Code-first users can bring their own frameworks and libraries.

Industry standards including ONNX, TensorFlow SavedModel, TorchScript, XGBoost, MXNet, and full export to external serving stacks.

No. Axonis integrates with Airflow and other orchestrators. It enhances your pipelines by enabling federated compute and in-place model training, rather than replacing existing workflows.

Anywhere:

• On-premises (server, cluster, or isolated environment)
• Any major cloud
• Hybrid architectures
• Edge sites, factories, and branch locations
• Fully air-gapped environments
• Multi-agency or multi-subsidiary federations

No. Axonis was designed specifically so companies can avoid multi-year migrations, cloud re-architecture, or consolidation projects. You start using your production data immediately.

Most organizations connect initial systems and begin federated training in typically 2 to 5 days, without requiring data migration or infrastructure overhaul

Axonis Decision Intelligence operationalizes AI-assisted decision-making by capturing every AI-assisted decision as a structured, auditable record: the evidence used, the model that produced it, the policy context applied, and the human attestations associated with it. AI decisions stop being black boxes and become defensible, reviewable artifacts that regulators, auditors, and internal reviewers can inspect at any time.

Every AI-assisted decision processed through Axonis is recorded with its full decision trace: the input data, the model version and configuration, the inference result, the policy constraints in effect at the time, and any human review or attestation actions. This creates a living system of record that survives audit, regulatory review, and internal investigation.

Yes. When a regulator or auditor asks why the AI recommended a particular action, Axonis provides a complete decision trace: the data inputs, the model and version used, the policy constraints active at decision time, and the chain of human attestations. AI recommendations become accountable records, not unexplained outputs.

Emerging AI regulations -- including EU AI Act requirements, SR 11-7 model risk management guidance for financial services, and healthcare AI accountability standards -- require organizations to explain and justify AI-assisted decisions. Axonis Decision Intelligence provides the structured evidence trail that makes AI decisions legible to regulators without requiring organizations to rebuild their AI infrastructure.

Axonis Federated MCP is an enterprise implementation of the Model Context Protocol (MCP) that embeds governance and security controls directly into AI agent workflows. Even agentic AI systems -- autonomous agents that act across systems and data sources -- operate within enforced data access boundaries and cannot reach data they are not authorized to touch.

Axonis Federated MCP iStandard MCP deployments give AI agents broad data access that is incompatible with regulated environments. Axonis Federated MCP extends the zero-trust security perimeter into the agentic layer: every agent action is governed by the same data-level access controls, authorization policies, and audit logging that apply to the rest of the Axonis platform.s an enterprise implementation of the Model Context Protocol (MCP) that embeds governance and security controls directly into AI agent workflows. Even agentic AI systems -- autonomous agents that act across systems and data sources -- operate within enforced data access boundaries and cannot reach data they are not authorized to touch.

Yes. Axonis Federated MCP allows AI agents to be deployed in regulated, classified, and multi-organizational environments without creating new security or compliance exposure. Agents see only what they are authorized to see. They cannot exfiltrate data, escalate privileges, or act outside their authorized scope.