Prompt-only defenses are not enough
Agents and retrieval systems ingest instructions from outside the user chat. That changes the security model.
Company overview
Built around one security boundary: external content before it reaches the model.
SecureFetch AI exists to make that boundary visible, inspectable, and enforceable for teams deploying agents and retrieval systems.
What the company is focused on
SecureFetch AI is a security company for AI agents and retrieval workflows. The company focus is narrow by design: inspect content from the outside world before it enters prompts, tools, memory, or downstream LLM context.
That includes web pages, linked documents, uploaded files, search results, and third-party source material that can influence system behavior long before a human notices.
Why this category matters
Retrieved content can shape behavior
External text can alter what an agent remembers, summarizes, cites, or does next.
Teams need policy outputs
Security controls are more useful when they return structured decisions that software can enforce.
How SecureFetch AI is presenting itself publicly
SecureFetch AI is publishing a clear company footprint through its website, documentation outline, and category definition. The intent is to give evaluators something concrete to review without overstating maturity or public adoption.
What is public here
- Company positioning
- Product boundary and use cases
- Documentation outline
- Frequently asked questions
What teams can evaluate
- Whether the fetch boundary is relevant to their architecture
- Whether structured inspection outputs fit their control plane
- Whether their agent and RAG workflows need source-aware policies