KitKat Audit SDK
Verify RAG answers with grounding and citation checks.
- Status
- Generally available
- Last checked
- Unverified
- License
- Proprietary
- Pricing
- Free tier
What it is
The KitKat Audit SDK verifies retrieval-augmented generation (RAG) answers by checking claims supported by context and references to sources, providing a trust score and approval status for generated content.
KitKat Audit SDK stands out by offering a specialized solution for verifying RAG answers, focusing on grounding and citation checks which are crucial for maintaining trust in AI-generated content.
When it fits — and when it doesn’t
A good fit for
Developers building RAG systems who need to ensure their generated content is grounded in context and properly cited.
Data scientists working on AI models that generate text, requiring a tool for post-generation auditing.
Look elsewhere if
Projects where real-time verification is critical as the SDK operates locally and may introduce latency.
Teams with limited technical expertise to integrate an additional library into their workflow.
Known weaknesses
Each one links to the public issue, changelog or docs page it came from, where we have one.
- 01
Limited language support beyond Python and TypeScript
Primary SDK is in Python, with only community-supported TypeScript version available
- 02
Expensive at scale due to reliance on external services for grounding verification
Cost increases significantly as the volume of content to be verified grows
What it does well
Grounding verification for RAG answers
Citation checking to ensure source references are accurate
Trust score generation for generated content
APPROVE/REJECT status based on audit results
Pricing
- Free tier
- Yes
- Paid from
- —
- Model
- Flat rate
- Enterprise plan
- No
Pricing not independently confirmed — check the vendor’s page.
Performance benchmarks
How Fast Is It?
Set up KitKat Audit SDK
Setup steps, common gotchas and what to check after 30 days.