The Axonis federated AI platform
On-premise AI execution for mission-critical enterprise environments
A single node or a powerful AI federation
When connected, these nodes form a federated AI mesh, sharing decision context and AI execution so organizations can operate AI across silos without centralizing risk or control.
Full on-premise AI capability in every node
Deploy the cloud-native stack in minutes. Connect to any data source.
Your data stays in place
No enterprise can centralize 100% of its data. Axonis brings AI to your data wherever it lives – cloud, on-prem, or at the edge. With Axonis, you can train better models and run real-time inference on your production data—transactions, events, logs, images, videos, sensor signals, and more.
Federated data & AI execution
The magic of the Axonis platform is full federation, and it starts at the data layer. Federated data prep and feature engineering means the data never has to leave Axonis to train or fine-tune sophisticated models. Federated learning means models travel to the data to train - without sharing the data. And federated inference, RAG, and MCP means that AI execution can happen wherever it is required, securely.
Models & connectors included
Axonis can complement your AI/ML and data investments. With a full API, built-in models (like PyTorch, TensorFlow, XGBoost, etc.), Hugging Face integration, extensible data connectors, and a cloud-native stack, Axonis can add federation to any AI strategy. Deploy Axonis on-top of Databricks or Snowflake to instantly add decision intelligence, or connect to legacy systems directly to bypass centralization altogether.
Integrated Enterprise MCP
Axonis extends MCP beyond basic connectivity into an enterprise-grade control plane that governs how agents access data, invoke actions, and contribute to decisions, while enforcing security, policy, and accountability at every step.
Every agent interaction flows through Axonis’ secure backend, where data-level security, attribute-based authorization, and role-aware access controls are applied consistently across humans, models, and agents. There is no implicit trust between tools, systems, or workflows, ensuring that AI-assisted actions operate strictly within enterprise policy.
Decision Context Built-In
New! The Axonis platform now includes an entire AI-assisted decision provenance layer, built-in. Exposed through the Axonis MCP and designed to work with Axonis Decision Intelligence, the platform becomes the system-of-record for decision traces, AI-interactions, human attestation, and cryptographically sealed decision records.
Once you capture your decision context, you can optimize it with LLM analysis and even model fine-tuning - all possible in Axonis.
Cell-level data security
Born in classified environments, Axonis delivers military-grade, zero-trust security with data-level access control for your most sensitive, regulated data. With ABAC (attribute-based access control), Axonis can label data upon ingest to enforce access policies, even down to the cell level of data, on any model training or AI execution.
With security embedded in the data, you get more secure AI agents and chatbots, out-of-the-box. In Axonis, if a user (or an agent) shouldn't have access to data, they will never even know it exists.
Built for high-stakes environments
Zero trust
Every action is auditable, with identity and access enforced before any data is returned.
Data level security
Granular controls down to the field or element level. Only authorized data is returned, including within natural language and agent workflows.
Compliance ready
Designed for regulated environments — HIPAA, PCI DSS, PHI and PII, SOX, HITRUST, ICD 503, GDPR, CCPA, LGPD, POPI, SOC 2, and ISO 27001.
Secure MCP ready for agents
AI access without compromising security, governance, or data controls
No implicit trust. No disconnected workflows.
Frequently Asked Questions
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.
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.