Product Announcement: Aegis Latent Core AI Governance Gateway
This is a project listing for an open-source, self-hosted AI governance and audit gateway providing request controls, PII redaction, and verifiable audit records for LLM traffic. It describes product features, not a user-reported problem.
Signal
Visibility
Sign in free to unlock the full scoring breakdown, root-cause analysis, and solution blueprint.
Sign up freeAlready have an account? Sign in
Deep Analysis
Root causes, cross-domain patterns, and opportunity mapping
Sign up free to read the full analysis — no credit card required.
Already have an account? Sign in
Solution Blueprint
Tech stack, MVP scope, go-to-market strategy, and competitive landscape
Sign up free to read the full analysis — no credit card required.
Already have an account? Sign in
Similar Problems
surfaced semanticallyManaging Multiple LLM Gateways Without Unified Keys, Budgets, or Audit
Teams running more than one LLM gateway end up with API keys, spend limits, routing rules and audit trails scattered across each tool separately. Platform and infrastructure engineers absorb the resulting operational overhead and lose a single view of cost attribution. This surfaced as a vendor launch post rather than a user complaint, so it carries no independent evidence of demand.
No Sandboxed Execution Boundary for Untrusted AI Agents
AI agents running locally have unrestricted access to host system resources, creating dual risks of accidental damage and data exfiltration. There is no standardized lightweight hypervisor layer that constrains agent execution without requiring full VM overhead. This gap becomes critical as agentic AI workflows expand into local environments.
AI Agent Compliance Auditing for EU AI Act
High-stakes B2B organizations need systematic frameworks to audit AI agents and LLMs for data leakage, hallucination, bias, and EU AI Act compliance before deployment.
AI Customer Answers Lack Auditable Evidence Trail for Compliance
Enterprises deploying AI in customer-facing roles cannot produce verifiable evidence of what criteria, sources, and execution contexts governed each AI response. Regulatory and legal requirements increasingly demand auditability of automated decisions. Internal logs are insufficient proof — external anchoring and tamper-evidence are absent from current AI deployment tooling.
CasesFly AI LLM Hallucination and Bias Detection Browser Extension
AI governance browser extension product launch for detecting LLM hallucinations. Not a problem statement.
Problem descriptions, scores, analysis, and solution blueprints may be updated as new community data becomes available.