Griffin AI vs Claude Citations: Advisory Work
Claude's citations feature makes the model say where its claims come from. Griffin AI uses it for advisory workflows where traceability is the entire point.
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
Claude's citations feature makes the model say where its claims come from. Griffin AI uses it for advisory workflows where traceability is the entire point.
Per-token pricing on the OpenAI API looks cheap on a single call and expensive on a year-long security workload. Griffin AI's pricing reflects the architecture.
Gemini on-device models are fast and cheap. For the developer-tool layer, they're useful. For the engine-plus-LLM layer, on-device is not the right fit.
The difference between grounded reasoning and hallucinated reasoning is not eloquence — it's citation. A look at how Griffin AI anchors every claim.
An auto-fix that closes a vulnerability and breaks the build is not a fix. Breaking-change awareness separates auto-PRs that ship from auto-PRs that get reverted.
An audit trail is only useful if you can answer questions from it. Quality is not about volume — it's about the ability to reconstruct decisions after the fact.
A vulnerability that passes through a working sanitizer is not a vulnerability. Detecting that sanitizer accurately is the difference between actionable findings and noise.
Your SBOMs come from a dozen vendors, three scanners, and two CI systems. Normalising them into one queryable graph is where SBOM programs actually succeed or fail.
A benchmark you can't reproduce is marketing. A benchmark you can rerun on your own infrastructure is evidence. The reproducibility gap is wide.
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