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. Reach out directly to begin a conversation.
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 the invarosd daemon in observe-only mode to produce an authenticated TBoM of the infrastructure your AI systems touch: interfaces, bridges, agent composition, and capability registrations.
Deliverables
- Authenticated TBoM 3.0.0 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
Topology Extraction and Environment Mapping
The first requirement for governing an AI system is understanding what it actually connects to. Topology extraction deploys invarosd 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
- Cryptographic receipt linking the observation to the host
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
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
- Helm chart configuration for isolated cluster deployment
Kubernetes Governance Integration
The InvarOS ValidatingAdmissionWebhook enforces governance boundaries
at pod admission time. Integration engagements configure the webhook handler,
define SLSA attestation requirements, establish hardware attestation node registries,
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
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 MCP tool surface in your environment, define trust boundaries, and prepare the architecture for future plugin-based protocol monitoring.
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
- Architectural recommendations for future MCP Gateway plugin deployment
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.
How engagements are structured
Discovery Sweep
Deploy invarosd in observe-only mode. Run topology extraction. Map undocumented agent flows and interface structures. Deliver authenticated TBoM artifact.
Architecture Audit
Evaluate network layout against governance requirements. Identify proxy bypass risks and trust boundary gaps. Construct dynamic TBoM maps and governance scope definition.
Deployment Design
Author canonical governance policies. Generate ZK verification schemas. Plan runtime and control plane deployment. Licence server and edge runtimes.
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