Results you can hand to a regulator, an insurer, or a referee.

The problemArticle 50 transparency obligations apply from 2 August 2026. The Article 11/12 logging obligations phase in by December 2027. Penalties reach €35M or 7% of global turnover, and insurers are already gating model-performance warranties on non-repudiable proof of behaviour.

What you getPaste an AI agent endpoint. We run 17 deterministic evidentiary checks across six EU AI Act articles — no LLM in the critical path — and return a scored readiness report you can hand to counsel.

Paste one log line and we name the specific failure, then fix it on your own data and hand you a verification command that needs no account. The scan and the fix are both free; Pro signs your live logs, with ML-DSA so the evidence outlives the 2030 deprecation and the 2035 cutoff — both of which fall inside Article 12's retention window.

The problemEvery vector database charges you a network round-trip before your function can do anything, and every one of them — Pinecone, Weaviate, Qdrant, Milvus, HNSW — returns approximate results. You tune recall against latency and never quite know what you missed.

What you getState travels in the request header as a packed hypervector, so a stateless function activates from bits already in hand. No connection, no query, nothing cold. And results are exact: a resonance shortlist followed by a full-precision rerank, so the top-k you get is the true top-k.

Measured at D=10,000 carrying 100 associations, against a warm co-located vector-DB query at 20–100 ms. The gain is structural rather than a faster scan: there is no round-trip to make, so it also works at the edge, offline, and with nothing provisioned. Capacity is bounded near √D and every read returns its own confidence margin, so an over-loaded key is detectable rather than silently wrong.

The problemClassical verification of quantum results runs out at roughly 48 qubits. Above that the available methods — Pauli and Majorana propagation, tensor networks — are truncated by construction, they disagree with each other, and there is no exact answer to say which one was right. IBM and Algorithmiq said exactly this in July 2026.

What you getFor Clifford, diagonal, product and structured circuits we return the exact expectation value — no shots, no sampling error, no truncation — at widths where every statevector engine is dead. Use it to measure what your approximate method actually cost you.

Scope is stated before you buy: exact for Clifford + diagonal + product + structured. Non-Clifford rotation with entangling hits a ceiling near 14 qubits, full-spectrum non-Clifford hits the same 2ⁿ wall everyone does, and physical noise is not modelled — deliberately, because an anchor should be the noiseless ideal.