Freshness
Detect stale or time-sensitive sources before retrieval is used.
Verified context for AI agents.
enfer.ai is the truth layer between agents and the open web. Evaluate freshness, evidence, and contradiction risk before retrieved context becomes confident output.
For agent teams, research systems, and retrieval pipelines using live web sources.
Problem
Search is not ground truth.
Search gives agents more context, but not necessarily better context. A retrieved page can be relevant and still be old, biased, promotional, copied, or wrong. Once it enters a model window, an agent reasons on it like truth.
The layer
Context should be checked before it is trusted.
Provenance
Identify primary sources, derivative pages, and promotional content.
Claim support
Connect generated claims to inspectable evidence.
Contradictions
Surface higher-quality sources that disagree.
Early access
For teams whose context has consequences.
We are speaking with teams building agents, retrieval infrastructure, research tools, and AI workflows where source quality determines whether a system can be trusted.
Building agents on live web data?
We are inviting a small number of teams to test source evaluation in real retrieval workflows.