Invisible Compliance Embedding
Hides your audit trail inside the response itself.
Like a medieval scribe embedding hidden marginalia, SCRIBE weaves chain-of-thought audit traces directly into the LLM output using steganographic encoding. EU AI Act Art. 14 requires explainability — SCRIBE delivers it without extra storage, extra API calls, or extra cost.
RAG Context → φ-threshold filter → Only relevant chunks sent to LLM
LLM Output → Stego-embed audit trail → Invisible to user, visible to auditor
High-risk AI systems must provide human-interpretable explanations. SCRIBE embeds the reasoning chain directly into the output — no separate logging system needed.
Auditors can extract the full chain-of-thought from any response using the SCRIBE extraction key. The original output is completely unmodified to end users.
Traditional explainability requires storing reasoning logs — petabytes at scale. SCRIBE encodes the explanation INTO the output. Storage cost: $0.
If anyone modifies the response, the steganographic embedding breaks. SCRIBE provides built-in tamper detection as a free side-effect of the embedding.
| Cost Category | Without SCRIBE | With SCRIBE | Impact |
|---|---|---|---|
| CoT logging storage | $300M+/yr (petabytes) | $0 (embedded) | 100% eliminated |
| RAG context tokens | 100% (all chunks sent) | 30-60% (pruned) | 40-70% savings |
| Compliance audit tools | $500K+/yr (3rd party) | Built-in extraction | Tool cost eliminated |
| Regulatory penalty risk | Up to 7% global turnover | Fully mitigated | Risk eliminated |
| At 1M req/month | $45,000/mo | $18,000/mo | $27,000/mo saved |
SCRIBE activates automatically through api.destill.ai/v1. No code changes. No extra storage.