1. Quickstart
Send fetched content to SecureFetch AI before it enters prompt assembly, memory, or tool execution.
Documentation overview
Product boundary first, implementation second.
These docs are structured to show where SecureFetch AI fits in an AI stack and what an integration would need to do.
Documentation map
2. System architecture
Place SecureFetch AI between retrieval and inference so external content can be inspected before model use.
3. Inspection outputs
Return structured risk signals such as injection risk, source trust, anomaly markers, and policy recommendations.
4. Policy integration
Use outputs to allow, warn, quarantine, route for review, or block downstream actions.
Example inspection flow
- Agent or pipeline fetches a page, file, or search result.
- Fetched content is normalized and sent for inspection.
- SecureFetch AI returns structured risk metadata.
- Application policy decides whether to pass, warn, quarantine, or block.
- Only approved content reaches model context or tool execution.
What this product is meant to complement
Application security
Traditional controls still matter. SecureFetch AI is focused on the content path into the model.
Prompt and model defenses
Model-layer protections remain useful. SecureFetch AI is intended to act earlier in the pipeline.
Retrieval quality systems
Ranking and relevance systems answer different questions than content trust and instruction risk.