AI-native GRC platform
Enterprise compliance automation for FedRAMP, NIST 800-53, SOC 2, CMMC, and ISO 27001. A multi-repo program spanning architecture, trust, governance, documentation, and operational readiness — built AI-native from day one.
- Problem: organizations waste months on manual compliance evidence gathering and framework mapping. Most GRC tools bolt AI on as an afterthought.
- Architecture: agentic evidence collection, automated control mapping across 35+ frameworks, continuous compliance monitoring, risk scoring, and audit preparation workflows.
- Security posture: trust boundaries, system boundary documentation, data flow diagrams, interconnection security agreements, and shared responsibility models.
- Why AI-native matters: purpose-built compliance automation delivers continuous assurance — not periodic snapshots that are stale by the time auditors arrive.
Agentic payments infrastructure
Local-first wallet infrastructure for OpenClaw nodes with operator guardrails, trust boundaries, and security controls for AI agent payment flows.
- Problem: AI agents handling real transactions need payment rails with trust boundaries and operator controls — not unconstrained cloud wallet access.
- Architecture: native wallet APIs, operator guardrails, trust model verification, deployment safety checks, and programmable value movement.
- Security surface: local-first design minimizes attack surface. Operator controls enforce transaction limits, approval flows, and audit trails.
- Why it matters: as agentic AI handles real value movement, the security and trust infrastructure becomes critical — this is where AI agent security meets financial controls.
Payment gateway infrastructure
Gateway API
HTTP 402-oriented payment gateway built to make wallet-backed agent interactions usable and secure in production applications.
- Problem: payment capability is useless if developers cannot integrate it cleanly with proper security controls.
- Architecture: gateway layer between applications and wallet infrastructure, with rate limiting, authentication, and audit logging.
- Why it matters: lowers integration friction for paid agent interactions while maintaining security boundaries.
Agent access and anti-abuse
Drip Faucet
Onboarding layer for mixed human/agent ecosystems that balances access with abuse resistance — without defaulting to human-only mechanisms like CAPTCHA.
- Problem: public access systems assume CAPTCHA, which excludes agents by design. But removing all controls invites abuse.
- Architecture: rate limiting, identity-based access tiers, and agent-compatible verification that doesn't rely on visual challenges.
- Why it matters: as AI agents become first-class participants in systems, access control must evolve beyond human-only assumptions.
