Human-Verified Semantic Integrity & Grounding

Your RAG Pipeline is Only as Safe as Its Last Retrieval.

The 3 Vectors of RAG Failure

Unauthorized Data Exfiltration

Your bot pulls from a shared knowledge base. Without strict semantic boundaries, a retail customer's query can inadvertently trigger the retrieval of a restricted internal PDF or a sensitive HR document. We audit the "Privacy Guardrails" of your retrieval logic.

Grounding & Source Decay

LMs are confident even when the retrieved data is conflicting or obsolete. If your vector DB contains three versions of a refund policy, the AI will "hallucinate a hybrid" that doesn't exist. We verify the factual grounding of the output against the specific source document.

Semantic Injection & Noise

Competitors or malicious actors can "poison" public-facing data that your RAG system ingests. We test your system's ability to distinguish between authoritative internal documentation and "noise" that could lead to biased or dangerous outputs.

The Methodology: Dissecting the Bridge

Source-to-Output Mapping

We don't just look at the chat. We analyze the specific document chunks retrieved from your vector database to see exactly where the logic failed.

Access Control Stress-Testing

We simulate "Privilege Escalation" prompts to see if we can force your RAG system to retrieve data it shouldn't have access to (e.g., PHI or trade secrets).

Semantic Boundary Analysis:

We test the "Narrowness" of your retrieval. Is your bot pulling too much irrelevant data? Is it ignoring critical context?

Remediation Blueprint

We provide your engineers with specific metadata tagging and prompt-weighting adjustments to tighten the retrieval-to-generation loop.

The "Source vs. Reality" Autopsy

We don't just tell you something is wrong. We cite the regulation and show you how to fix the prompt.

SOURCE DOCUMENT (PDF)

Section 4.2: Policy Terms

"No refunds shall be issued after 30 days under any circumstances."

Semantic Drift

AI misinterpreting policy hierarchy and ignoring strict grounding constraints.

FAILURE DETECTED

CRITICAL INTEGRITY FAILURE

FINDING

AI hallucinated "Exceptions for VIPs" despite no such grounding in the source document.

RISK

Financial Liability / Significant Policy Breach

COMPLIANCE GAP

SOC2 / GDPR Article 5 (Accuracy).

Regional Compliance Frameworks

REGION

REGULATORY FRAMEWORK FOR RAG

United States

NIST AI RMF / HIPAA Security Rule (Ensuring AI doesn't retrieve PHI improperly).

Canada

PIPEDA / Bill C-27 (AIDA) (Accountability in automated decision-making systems).

European Union

EU AI Act / GDPR Article 32 (Security of processing and automated accuracy).

Is your Knowledge Base leaking? Let's verify it.

Send us 10 examples of complex RAG-based interactions. We will map the output back to your source documents and tell you if your AI is staying within its semantic boundaries or drifting into liability territory.

Start Your 10-Point RAG Integrity Audit