Question 1

What is a Verified Context Layer (VCL)?

Accepted Answer

A Verified Context Layer is middleware that intercepts AI-generated responses before delivery, extracts individual factual claims, cross-validates each claim against authenticated personal data sources in real time, and returns annotated responses with a verification confidence score. GroundSync is the first commercial implementation of VCL technology.

Question 2

What is the GroundSync Score?

Accepted Answer

The GroundSync Score is a 0-100 Verification Confidence metric that represents the percentage of AI-generated claims in a response that could be verified against authenticated personal data sources. A score of 90+ means nearly all claims are grounded in real data. A score of 0 means the AI response is entirely unverified.

Question 3

How does GroundSync prevent AI hallucinations?

Accepted Answer

GroundSync prevents AI hallucinations about personal context by verifying each AI-generated claim against the user's real connected data - their calendar, email, financial accounts, and health data. If an AI claims you are free on Thursday but your calendar shows a 3pm meeting, GroundSync flags the claim as NEEDS_INPUT and corrects it before you see the response.

Question 4

What is deterministic AI grounding?

Accepted Answer

Deterministic AI grounding is the process of anchoring AI responses to verified, authenticated data sources rather than allowing probabilistic or pattern-based responses. Unlike standard RAG which retrieves context without verifying claims, deterministic grounding validates each assertion and returns a pass/fail result per claim.

Question 5

What is agentic liability mitigation?

Accepted Answer

Agentic liability mitigation refers to technical systems that reduce the legal and operational risk of AI agents taking actions on behalf of users. GroundSync provides agentic liability mitigation by verifying every AI claim before an agent acts on it, creating an immutable audit trail of what was known vs assumed at the time of each action.

Question 6

How does GroundSync differ from RAG?

Accepted Answer

Retrieval-Augmented Generation (RAG) supplies context to an AI model to reduce hallucinations, but does not verify the output claims. GroundSync operates post-generation - it verifies the AI's output claims against authenticated sources regardless of how the model was prompted. GroundSync is the verification layer above RAG, not a replacement for it.

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