AI assets
Models, agents, prompts, tools, vendors, data flows, and operational dependencies.
Coming soon · Private design partners
Continuous trust for critical AI systems.
Agenvera is building a new assurance layer for enterprises deploying artificial intelligence inside telecom networks, operational technology, and critical infrastructure.
TelcoAIFirst market wedge
TAAGAssurance graph core
EU-firstPrivacy-safe architecture
Regulatory evidence
NIS2, internal policies, AI governance, supplier controls, and audit records.
Risk testing
Prompt injection, unsafe tool use, data leakage, model drift, and excessive permissions.
Board reporting
Evidence-backed posture, owners, remediation actions, and executive summaries.
Not another dashboard. A living assurance model.
Agenvera is designed for high-trust environments where artificial intelligence cannot be treated as a black box or a one-time compliance exercise.
01
Discover AI in the enterprise
Build a structured inventory of AI systems, agentic workflows, models, prompts, vendors, tools, and critical data paths.
02
Validate risk continuously
Run targeted checks for unsafe tool use, prompt injection, leakage, excessive permissions, model drift, and supplier exposure.
03
Produce defensible evidence
Connect findings to control obligations, owners, remediation actions, and audit-ready reports for security, risk, and executive teams.
Core technical artifact
Telecom AI Assurance Graph
The first vertical models the relationships between telecom AI assets, operational systems, network functions, vendors, risks, controls, evidence, and owners.
AI assetsOSS/BSSRisk nodesControl obligationsEvidenceVendor dependencies
Built for regulated AI pressure, not hype.
The first release focuses on practical assurance needs that security, governance, and infrastructure leaders already understand.
Security evidence
Map AI-related risks to security measures, owners, supplier obligations, and operational evidence.
Controls that stay current
Connect policy, validation, testing, and remediation records as systems change.
Telecom first
Start with telecom network operations and expand to operational technology, mobility, energy, and water.
Assurance in plain language
Summarize posture, gaps, owners, and remediation actions for executives and auditors.
Privacy-safe by design from day one.
The pilot architecture is built to avoid unnecessary exposure of personal, subscriber, or sensitive operational data.
Metadata-first pilots
Agenvera can evaluate assets, model cards, test results, controls, and evidence without ingesting subscriber personal data, packet payloads, or call records by default.
- No training on customer data
- Private cloud and on-premise options
- Tenant isolation and explicit retention controls
For security leaders
Evidence-backed risk posture without creating a new data lake of sensitive telecom or infrastructure records.
For design partners
Start with synthetic or sanitized data, then move to private deployment only when the control boundaries are approved.
Focused wedge. Larger platform.
Telecommunications is the first proof point. The same assurance graph naturally expands into mobility, operational technology, energy, water, and smart-city systems.
TelcoAI Assurance
Inventory, risk validation, evidence mapping, and board reporting for AI-enabled telecom operations and vendor workflows.
Mobility and OT assurance
Extend the assurance model into connected mobility, operational technology, edge systems, and critical infrastructure dependencies.
Critical AI platform
Scale the graph and evidence model into broader regulated sectors with reusable controls and sector-specific ontologies.
Questions buyers ask first.
Short answers for security leaders, governance teams, and early design partners.
Is Agenvera a consulting company?
No. The company is being built as a software platform around an assurance graph, risk validation workflow, and evidence model. Advisory support may exist during pilots, but the core is productized software.
Why start with telecom?
Telecom combines artificial intelligence, critical infrastructure, operational complexity, supplier dependency, and strong security obligations. It is a focused wedge for a broader critical AI assurance platform.
What makes the assurance graph different?
It links AI assets, telecom systems, vendors, risks, controls, owners, and evidence in one model, so assurance can be continuously updated instead of recreated through manual assessment cycles.
Private pilots are opening soon.
We are speaking with telecom operators, cybersecurity consultancies, AI governance teams, and research partners interested in continuous assurance for high-trust AI systems.