AI you can actually trust for high-stakes decisions

ChatGPT gives you outputs. We give you governed outputs. Every recommendation passes through compliance gates, gets verified by a Gemini-led multi-model quorum, and produces a cryptographic audit trail. When ChatGPT hallucinates, you find out after damage is done. When our system would hallucinate, the output is blocked before it reaches you.

RAG solves a different problem. RAG helps the AI retrieve the right information — but it doesn't govern what the AI does with it. An AI with RAG can still hallucinate during synthesis, still make non-compliant recommendations, and still produce outputs with no audit trail. RAG is about getting the right inputs; AAGP is about ensuring safe outputs. You need both.

Because it's still the same model with the same biases and blind spots. Research shows models are sycophantic and struggle to genuinely challenge themselves. True adversarial verification requires models with different architectures trained on different data. They catch different errors because they fail differently. Roleplay is theater; multi-model verification is engineering.

Several major AI platforms have recently introduced multi-model approaches for research and analysis — validating the thesis that single-model AI is insufficient for important decisions. AAGP goes further. Where those tools surface multiple perspectives for a human to evaluate, AAGP enforces governance at the execution layer — compliance gates, deterministic audit trails, and cryptographic provenance. The difference is research-grade consensus vs. production-grade governance. We're not building a better research assistant. We're building the infrastructure that makes AI safe to execute autonomously in regulated environments.

Those tools generate content. We govern AI agents that take actions. There's a big difference between "write me ad copy" and "manage my marketing budget across channels." The latter requires safety controls, budget limits, compliance verification, and audit trails. Content generators don't provide that.

Two layers protect you. First, our pre-synthesis gates catch most mistakes before they happen — the bad output is never generated. Second, our circuit breaker monitors business metrics in real-time. If something goes wrong, the system auto-pauses, traces to the root cause, and can rollback — all before significant damage occurs.

Three reasons: (1) We know the domain — the founder has Digital Marketing Strategy certification and development partner clinics providing real-world validation. (2) Clear ROI metrics — marketing spend to new patients is easy to measure. (3) Healthcare-adjacent compliance — it's a good proving ground before expanding to clinical healthcare or financial services.

Early stage, moving fast. Architecture and design finalized — now beginning active development. Accepted into Google for Startups Cloud Program. Filed 12 provisional patents December 2025. Two dental clinic pilots confirmed. Team of 3 enterprise technologists plus a practicing dentist as product owner. Bootstrapped — no outside capital. Lead founder full-time as of March 2026, with active GCP development beginning mid-to-late March.

Gemini is core to our architecture — it serves as both the quorum leader and a participant in our multi-model governance. Vertex AI Agent Builder provides native multi-agent orchestration. Model Garden enables heterogeneous verification. And our dental vertical depends on Google Ads API. We're fully committed to GCP.

No — AiRadics is a complete web application. Dental practices log in and use it to manage their marketing. The governance is built into how the application's AI engine works internally. You don't integrate governance — you get a governed product out of the box.