Crebiliti transforms every AI statement into a measurable truth signal — detecting consensus, contradiction, and confidence in real time, and steering models toward verified trajectories.
Built for enterprises that need certainty — not speculation.
How Crebiliti Works (at a glance)
Raw sentences from any LLM.
Extracts Atomic Fact Units.
Consensus/contradiction → V‑Score.
Logit shaping with V‑Scores.
Enclave‑signed AR‑Bundle + V‑Badges.
A veracity layer that sits beside or inside LLMs: extract Atomic Fact Units, compute a V‑Score in a consensus lattice, and anchor decoding toward verified trajectories—while signing the reasoning path in hardware enclaves.
Parser + normalizer producing subject–predicate–object triples with qualifiers, time, and provenance.
Consensus/contradiction lattice with temporal decay, source independence, and a V‑Score per AFU.
Runtime logit shaping steers token selection using V‑Scores. Optional hard masks for high‑risk contradictions.
“MINI was first produced in 1959.” → V95/5
“MINI debuted in 1940.” → V12/88 (contradicted)
ARM TrustZone / NVIDIA Grace CCA attestation with code hash, model hash, V‑Graph snapshot, and monotonic counters.
Sub‑200ms V‑lookup with warm cache; graceful fallback to uncertainty surfacing in sparse domains.
Text → AFUs + V‑badges + evidence bundle.
Logits + AFU context → adjusted logits + audit token.
Targets → veracity‑regularizer weights and manifold pulls.
Test claims, inspect evidence, and see how anchoring changes token selection.
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