Federate everything. Replace nothing.
The Federated Data Mesh (FDM) platform layers over the databases, data products, models, agents, and compute engines your agency already runs. Connect legacy systems, cloud, programs, and mission domains through one governed federation layer, preserving local ownership while enabling secure data products, analytics, AI, and authorized agents, without moving data into a central repository.
What it is
The Federated Data Mesh (FDM) platform is a governed control plane between the data you already have and the mission outcomes you are after.
It does not replace your data platforms or your mission systems. It sits between them and your outcomes, making distributed data discoverable, trustworthy, and actionable, while each program retains ownership of its own systems and data.
Certified data products
Searchable, owned, and documented data products that authorized users can find across the enterprise.
Quality & lineage everywhere
Machine-readable contracts, column-level lineage, and quality controls applied consistently across domains.
Governed AI & analytics on top
Permission-aware models, applications, and agents drawing from trusted, governed mission context.
Inside your boundary
Deployed as containers within agency-controlled environments, from commercial and government cloud to air-gapped.
The federal data challenge
When the mission needs an answer now, the data is the bottleneck.
Agencies run on decades of legacy mission systems, spread across multiple programs and components, separate funding lines and approval authorities, distinct security boundaries, and cloud and on-premises environments. When operational tempo rises and leadership wants capability fielded fast, that fragmentation is what slows the response.
- Standing up each new capability means another custom, point-to-point integration, measured in months the mission does not have.
- Programs funded and approved through separate authorities are now told to interoperate, with no common data layer between them.
- Surges, contingencies, and high-tempo operations demand elastic scale that brittle pipelines cannot deliver.
- Central data teams become the bottleneck; mission users wait in a queue for access and for answers.
- Accreditation and fielding timelines stretch, delaying capability that is needed now, not next year.
- Teams cannot get representative, governed data to prototype, test, and prove a new idea quickly.
- Copying data between systems spawns conflicting versions of the truth, and moving it adds latency the edge cannot absorb.
- Definitions and metrics differ across components, so leaders cannot trust a single readiness picture.
- Lineage is hard to establish and access approval is slow, so accountability and audit lag behind the mission.
- AI and analytics initiatives stall because models cannot reach trusted, permission-aware mission context.
Why traditional approaches fall short
Each established approach solves part of the problem. None provides the whole model.
These technologies are not obsolete, most agencies will keep using them. They tend to address one layer well, but rarely provide a single model covering distributed ownership, shared semantics, federated policy, reusable data products, and mission activation together.
Warehouses, lakes & lakehouses
Centralize storage and analytics, but require moving and copying data, creating duplication, latency, and competing versions of the truth.
Data catalogs
Help you find and describe data, but do not enforce access, deliver governed data products, or activate data for operations and AI.
Data virtualization
Federates queries, but often stops short of contracts, semantics, lineage, productization, and policy applied as one trust model.
Integration & ETL platforms
Build pipelines, but multiply point-to-point connections and brittle copies that grow with every new use case.
API management
Manages endpoints and traffic, but is not aware of data meaning, quality, classification, or domain ownership.
Custom modernization programs
Can work, but become long, expensive, and risky, and rarely produce reusable patterns the enterprise inherits.
The federated approach
A governed connective layer across the systems you already run.
Programs retain control of their data. Authorized users and applications gain a consistent way to discover, understand, request, and use trusted data products across the enterprise, starting with one domain and expanding incrementally.
Data stays put
Data can remain within its existing system and security boundary. Computation is pushed to the source, so only results move.
Policy governs access
Access is governed by agency policy at the point of use, not by copying data into a new perimeter.
Investments are preserved
Existing platforms and mission systems remain part of the architecture, with no rip-and-replace required.
Adoption is incremental
Begin with one mission use case or domain. Add systems and data products over time as confidence grows.
How it works
Connect, understand, govern, productize, and activate.
Connect
Connect approved databases, applications, repositories, APIs, events, cloud platforms, and mission systems through source-agnostic adapters.
Discover & understand
Capture metadata, schemas, entities, ownership, classifications, relationships, lineage, and business definitions automatically.
Govern
Apply identity, access, policy, privacy, retention, quality, and audit requirements consistently across participating domains.
Publish data products
Programs publish reusable datasets, APIs, events, models, and services, each with documented ownership and controls.
Activate
Deliver governed data to mission applications, dashboards, analytics, workflows, AI systems, and authorized agents.
Platform capabilities
Six governed layers, one platform.
API-first and containerized, from federation at the base up to governed agents at the top. A single reusable application programming interface (API) fabric spans every layer.
Agents & generative AI Available
A configuration-driven agent harness and visual builder compose supervisors, classifiers, and retrieval using only approved models, every call governed, traced, and auditable.
ML / LLM operations Available
Experiment tracking and a versioned model registry, Kubernetes-native serving, and full tracing for every model and large language model (LLM) call.
AI & ML models Available
Bring your own model or plain training code into a governed home. Any model type, any hosting pattern, drawn from an approved-model catalog with full traceability.
Semantic layer & knowledge graph Available
Define-once metrics, entities, and dimensions; a shared feature store; standards-based ontology mapping and graph relationships across domains.
Governance, quality & lineage Available
Machine-readable data contracts, drift and freshness alerts, column-level lineage, and role- and attribute-based access control with full access audit.
Federation Available
One structured-query (SQL) engine across all sources. Computation is pushed to the source so data is never copied, blend clinical, operational, and administrative domains without copy pipelines.
Reference architecture
Federate the source. Govern the flow.
FDM is the governed control plane between your mission data sources and the systems and people that consume them. No data movement is required, and one trust model spans all of it.
No data movement · One trust model · Existing systems remain the system of record
Solution in focus · Health missions
The mesh in action for a health mission.
The same federation layer, shown for a health agency. Clinical and operational systems on the left stay where they are. The mesh federates and governs them in the middle. Clinicians, care teams, logisticians, and leadership receive trusted outcomes on the right. The pattern applies across military health, veterans health, public health, and health regulatory missions.
Semantic interoperability
Define once. Reuse everywhere.
Different programs often use different identifiers and definitions for the same person, asset, case, facility, provider, grant, or transaction. The semantic layer maps them to one agreed meaning, so dashboards, pipelines, and agents all reason from the same definitions.
Supports shared mission entities, common definitions, domain vocabularies, mappings, relationships, ontologies, metrics, policies, classification, and machine-readable context, applied consistently across heterogeneous systems and aligned to recognized interoperability standards where they exist.
Data products
Data managed like a product: owned, documented, and reusable.
A data product may be an approved dataset, an API, an event stream, a semantic model, a domain service, a governed query, a reusable feature set, or a mission-entity view. Each carries its ownership and controls with it.
Example: Enterprise resource-status view
Illustrative exampleEach data product also records description, refresh frequency, policies, service expectations, and version, so consumers know exactly what they are using and producers can evolve it safely.
Security & governance
One trust model, applied at the point of access.
Security is treated as a first-class layer of the platform, not an add-on. FDM is designed to support Zero Trust principles and to integrate with agency-defined controls.
Identity-based, least-privilege access
Role-based and attribute-based access control (RBAC and ABAC), with row- and column-level controls tied to agency identity providers.
Policy-based authorization
Policy is enforced on every call at the point of access. Access decisions are evaluated against agency-defined rules, not bypassed by data copies.
Classification & data-level controls
Data classification and data-level access controls govern who can see which fields, products, and domains.
Lineage & provenance
Column-level lineage and provenance make it possible to report on exactly how data moved and who touched it.
Audit & continuous monitoring
Every data-product access is logged. Report at any time on who accessed which product, and who should not have.
Privacy, retention & human review
Privacy controls, records and retention policies, and human approval workflows can be applied to sensitive operations.
FDM is designed to support alignment with recognized federal data principles and to be deployed within environments subject to agency security requirements, including segmented and restricted environments. Formal authorizations, accreditations, and impact-level approvals are obtained by the agency for its specific deployment, see the disclosure in the footer.
Deployment flexibility
In your boundary, on your terms.
FDM is deployed as containers inside agency-controlled environments. It does not require sending data to a vendor-controlled public environment. The same platform and the same governance run everywhere.
Government & commercial cloud
Deployed within an agency cloud tenant. Multi-cloud capable; you control where data resides and how it is accessed.
On-premises & private cloud
Run inside agency data centers and private cloud, within existing network and security boundaries.
Edge & disconnected
Kubernetes-native serving brings real-time inference and federated access to forward and disconnected environments.
Air-gapped & restricted
Designed to operate in segmented and air-gapped environments where outbound connectivity is restricted.
Built for elastic, Kubernetes-native scaling with self-healing, rolling updates, and consistent governance, RBAC, and observability across every environment, so any data product or model can scale independently.
AI & agent enablement
AI as a governed consumer of the data foundation, not a black box.
The same governance that protects your data products protects your models and agents. AI draws on trusted, permission-aware, traceable mission context, and inherits the platform's access controls and audit.
Trusted, permission-aware retrieval
Retrieval and agents respect the same access policy as every other consumer. Users only ever retrieve what they are authorized to see.
Traceable citations & provenance
Every model and agent call is traced. Decisions can be traced back to the data, configuration, and version that produced them.
Approved models & tools only
A governed catalog routes requests to approved models and scoped tools. Every call passes a safety pipeline with human review points where required.
A governed home for any model
Third-party or in-house models are shared, registered, versioned, validated, and monitored for drift, deployed inside your boundary, with no black boxes.
Agents as configuration
Define a prompt, choose an approved foundation model, grant scoped tools, and deploy. A visual builder composes supervisors, classifiers, and retrieval, no code project required.
Model-flexible & restricted-ready
Any model type and hosting pattern (real-time API, autoscaling service, batch, or edge), including restricted and disconnected environments.
Federal use cases
Mission patterns the platform is built to support.
Each begins as a bounded first win on data you already have, inside your environment, and becomes a reusable pattern the enterprise inherits. The examples below are illustrative mission archetypes, not references to specific agency deployments.
Real-time inventory & resource tracking
Challenge: Critical resource status is scattered across systems with no enterprise view.
Pattern: Federate status across source systems in place; publish a governed, real-time resource-status data product with quality rules and lineage.
Outcome: A current common operating picture without copy pipelines.
Users: operations & logistics leaders, domain stewards
Enterprise capacity & flow management
Challenge: Coordinating capacity across facilities requires data from many autonomous systems.
Pattern: Federate capacity and flow data while each system retains autonomy and security boundaries, no migration.
Outcome: Enterprise-wide visibility and faster coordination across the network.
Users: mission coordinators, planners
Workforce optimization
Challenge: Staffing decisions span planning, scheduling, and operational-tempo data in separate systems.
Pattern: Integrate workforce data into governed products; apply analytics and forecasting on top.
Outcome: Better access, balanced workload, and readiness aligned to demand.
Users: workforce planners, analytics teams
Legacy modernization & onboarding
Challenge: Replacing established mission systems is slow, costly, and risky.
Pattern: Federate and govern existing systems incrementally; onboard new sources at the configuration layer.
Outcome: Interoperability without rip-and-replace.
Users: enterprise architects, modernization teams
Secure interagency & partner sharing
Challenge: Sharing data across organizational and security boundaries is slow and manual.
Pattern: Publish permission-aware data products with policy enforced at access and full audit.
Outcome: Governed sharing across boundaries with provenance and accountability.
Users: data governance officers, security architects
Trusted AI & agent enablement
Challenge: AI initiatives stall on fragmented, ungoverned, non-permission-aware data.
Pattern: Give models and agents governed, permission-aware context with full tracing and approved-model routing.
Outcome: AI that is auditable, policy-aware, and deployable in restricted environments.
Users: AI & analytics leaders, security architects
Differentiation
How FDM is different.
FDM connects semantics, governance, access, data products, and AI in one governed fabric, across the systems you already run.
| Dimension | Traditional centralized / point tools | FDM |
|---|---|---|
| Data location | Move and copy data into a central store | Query in place; data stays in approved systems |
| Existing systems | Often replaced or bypassed | Preserved as systems of record; remain in the architecture |
| Ownership | Central team owns the platform and becomes the bottleneck | Domains retain ownership; central team sets the model |
| Scope | One layer: catalog, or warehouse, or integration, or governance | Semantics, governance, access, products, and AI together |
| Trust model | Stitched across many tools and copies | One trust model: contracts, lineage, RBAC/ABAC, audit |
| AI & agents | Bolt-on, often ungoverned | Governed consumers of the same permission-aware foundation |
| Deployment | Often vendor-controlled cloud | Sovereign: your boundary, including edge and air-gapped |
| Adoption | Large, multi-year program | Bounded first win; reusable patterns the enterprise inherits |
Where we would start
A bounded first step, on data you already have, in your environment.
Each first step is a configuration exercise, not a build, first results in weeks, and each becomes a reusable pattern the enterprise inherits.
A few federated data products
Blend two domains that need separate stewards today into one governed, certified product, with quality rules and lineage. Proves federation without moving data.
One external model, governed
Take a model from a partner or in-house team, register it, and stand it up in the governed lifecycle, in your tenant, with traceability and drift monitoring.
One approved-model agent
Deploy a single configuration-driven agent for a bounded workflow using only your approved models, with full decision lineage.
Frequently asked
Answers for federal decision-makers.
Does FDM require centralizing all of our data?
Will we have to replace our existing mission systems?
How does it preserve program, bureau, and domain ownership?
Can it connect programs that are funded and approved through separate authorities?
How does it support surges and high operational tempo?
How does it support Zero Trust and policy-based access?
Can it operate in hybrid, edge, and air-gapped environments?
Is FDM FedRAMP authorized or accredited?
How does it fit our Authority to Operate (ATO) and fielding path?
How is this different from a data catalog, lake, or integration platform?
Can our teams prototype and test a new idea before committing?
How quickly can we see value?
Request a federal briefing
Start with one bounded win, on your data, in your environment.
Request a briefing tailored to your mission environment. We will walk through the reference architecture, the security and governance model, deployment options for your boundary, and a low-risk first step.
- Review the reference architecture
- Discuss a mission use case
- Explore deployment options for your environment
- Scope a bounded pilot