Use Cases

Early engagements
available now.

The following engagements are structured around InvarOS's current, demonstrated capabilities. No fabricated outcomes. No invented customers. Each use case reflects what the platform can deliver in a real enterprise environment today.

Engagement model: InvarOS is in public soft launch. Software packaging is in progress. All engagements below are available as consulting, pilot design, and architecture assessment services. Two now build on demonstrated runtime capability — a governed agent pilot (deterministic pre-execution authorization at the agent gateway) and deterministic deployment (the lifecycle that reached a healthy, governed deployment on real hardware). Reach out directly to begin a conversation.

Available Now

AI Infrastructure & Governance Assessments

Enterprise AI deployments frequently introduce agent systems — LangChain pipelines, MCP-connected tools, AutoGPT-style automation — with limited visibility into the resulting operational topology. InvarOS assessments deploy InvarOS Runtime in observe-only mode to produce a validated, fingerprinted TBoM of the infrastructure your AI systems touch: interfaces, bridges, agent composition, and capability registrations.

Deliverables

  • Validated, fingerprinted TBoM topology artifact from your environment
  • Interface and bridge map across agent execution hosts
  • Identification of unmanaged agent connections and shadow tool registrations
  • Architecture report with governance gap analysis
  • Recommended policy boundaries for the InvarOS platform
Regulated enterprise Financial services Healthcare
Available Now

Topology Extraction and Environment Mapping

The first requirement for governing an AI system is understanding what it actually connects to. Topology extraction deploys InvarOS Runtime to systematically chart the execution environment — host interfaces, container bridges, virtual ethernet peers, and tunnel overlays — producing a machine-readable TBoM artifact.

What gets extracted

  • Node inventory: bridge, physical, logical, tunnel, and neighbor nodes
  • Edge map: bridge membership and connection relationships
  • Host fingerprint and topology fingerprint per observation epoch
  • Optional ES256-signed Runtime Evidence Record via the Runtime Evidence Plugin
Any Linux host Container environments Edge hardware
Available Now

Pilot Design and Integration Planning

Bridging from initial topology extraction to a governed production deployment requires careful architectural planning. InvarOS pilot design engagements define the deployment footprint, plugin configuration, policy scope, and control plane integration path before a single line of integration code is written.

Pilot design covers

  • Target hardware selection (server, edge router, or both)
  • Plugin scope and C ABI configuration
  • Policy definition and ZK schema generation
  • Evidence pipeline and receipt archival design
  • Kubernetes admission controller integration plan
  • Success criteria and governance boundary definition
Pre-production Architecture phase
Available Now

Regulated and Disconnected Deployment Planning

Defense agencies, intelligence organisations, critical infrastructure operators, and highly regulated financial institutions face a common constraint: always-on cloud connections are architecturally prohibited. InvarOS's air-gap model was designed explicitly for this requirement.

Deployment planning engagements assess existing classified or restricted network architectures, design asynchronous evidence transport paths, and define operating procedures for moving recognition records and CBOM artifacts across the air-gap.

Design targets

  • Offline policy compilation and ZK schema distribution
  • Local evidence generation without cloud telemetry
  • Physical transport protocols for recognition records
  • Kubernetes admission controller in disconnected mode
  • invaros-mcp-gateway Helm chart configuration for isolated cluster deployment
Defense Intelligence Critical infrastructure
Available Now

Kubernetes Governance Integration

The InvarOS ValidatingAdmissionWebhook enforces governance boundaries at pod admission time through InvarOS MCP Gateway, the separate Python FastMCP/ASGI integration service. Integration engagements configure the webhook handler, define SLSA attestation requirements, configure hardware attestation descriptor registries (structural validation, not live TPM/SGX/SEV verification), and set cross-tenant namespace trust contracts.

Integration scope

  • Webhook handler deployment and TLS certificate provisioning (cert-manager)
  • SLSA annotation policy definition per namespace
  • Hardware attestation descriptor registry configuration
  • Cross-tenant trust contract enforcement rules
  • Fail-closed posture validation and rollback procedures
Kubernetes Container platforms
Available Now

MCP and Agentic Architecture Hardening

The Model Context Protocol (MCP) is the primary integration surface for agentic AI systems and a significant attack vector (OWASP MCP Top 10 includes context over-sharing, tool poisoning, and prompt injection). InvarOS hardening engagements map and document the agent tool surface in your environment, define trust boundaries, and — where the environment supports it — introduce deterministic, pre-execution authorization of tool calls, which is live-qualified today at the OpenClaw gateway.

Hardening scope

  • MCP server and tool inventory across the agentic environment
  • Tool permission boundary documentation
  • Context over-sharing risk assessment
  • LangChain adapter integration using InvarOS commitment toolkits
  • Governed agent pilot: deterministic DENY / ALLOW with signed Decision Receipts and Authorization Leases (see Governance)
Agentic AI LangChain MCP deployments
Available Now

Research and Academic Collaboration

InvarOS is built upon proprietary mathematical methods for deterministic AI governance, verification, and capability analysis. The public benefit commitment means academic institutions and safety research organisations receive full access to enterprise features for non-commercial research.

Research collaboration interests include: mathematical AI safety, formal verification of agent behaviour bounds, cryptographic provenance for AI systems, and privacy-preserving governance under the ZK compliance claim framework.

Academic institutions AI safety labs Non-commercial research
Consulting Structure

How engagements are structured

Phase 1

Discovery Sweep

Deploy InvarOS Runtime in observe-only mode. Run topology extraction. Map undocumented agent flows and interface structures. Deliver a validated, fingerprinted TBoM artifact.

Phase 2

Architecture Audit

Evaluate network layout against governance requirements. Identify proxy bypass risks and trust boundary gaps. Construct dynamic TBoM maps and governance scope definition.

Phase 3

Deployment Design

Author canonical governance policies. Generate ZK verification schemas. Plan runtime and control plane deployment. Licence server and edge runtimes.

Get started

Begin a conversation.

All engagements above are available now. We work directly with founders, CTOs, and security architects to scope and execute the right entry point.

Contact InvarOS