page · results · architectural targets · NCE v2.4

No promises.
Targets.

These figures are the NCE architectural targets — built on 6 months of development and real-sector testing. Not yet production results, because there is no production yet. Said clearly.

/ detail · 4 metrics

Four figures. Four commitments.

Defined from the outset — measured and refined on each pilot deployment.

12wks.
speed · time-to-value

From decision to first measurable results

Not 18 months of IT project. Twelve weeks to go from signed brief to first results on your real use case. Before/after measurement, executive committee presentation in week 12.

Method: pilot in 4 phases P0–P3 — defined deliverables at each stage
Scope: applicable from 500 documents · PDF, DOCX, XML, regulatory export formats
Commitment: GO/NO-GO presented at end of pilot · all deliverables retained whatever the decision
<5%
reliability · NCE target

Target error rate on regulatory corpus

RAG approaches plateau between 30 and 45% error on dense regulatory corpus. The KG + NCE architecture structurally targets the operational threshold of < 5% — through 14 reliability dimensions continuously measured.

Architecture: knowledge network + entity whitelist + fidelity validation
Protocol: sector golden tests · comparative evaluation vN vs vN−1
Reproducibility: every answer traces back to source paragraph · → see the NCE protocol
3days
deployment · go-live

From decision to a live NCE

No 18-month IT project before the first result. The NCE infrastructure — knowledge graph, vector engine, interface — is deployed in your infrastructure in 3 business days. Connection to your existing document sources included.

Deployed components: Neo4j (graph), Qdrant (vectors), NCE interface — 100% within your perimeter
Infrastructure modes: physical, virtualised or accredited enclave (air-gap possible)
After deployment: document ingestion started in Phase 0 — first results in week 4
0leak
security · exposure

Data exposed outside your infrastructure

100% on-premise. No call to an external service. No cloud dependency. The engine, the network, the interface and the models all live within your perimeter — physical, virtualised or accredited enclave.

Architecture: all modules (engine, network, interface, models) within your perimeter
Verifiable: network capture report delivered with the solution — for CISO/DPO validation
External dependencies: none — air-gap operation possible
NCE v2.4 · Neural Context Engine

14 reliability dimensions. A rigorous evaluation protocol.

The < 5% score is the structural target of the NCE architecture — not a marketing promise. It is carried by 14 reliability dimensions continuously measured, an entity whitelist, out-of-domain refusal, and fidelity validation of every answer.

See the NCE architecture →
/ technical metrics · DQE pipeline · NCE v2.4
automated tests 859+ 0 regression · v2.4
extraction confidence 83–87 % average score · real corpus
qualification stages 18 DQE pipeline · 2 phases
extraction duplicates < 8 % rate · fresh corpus

Measured on real phytosanitary corpus (2023–2024) · formal qualification in progress

/ methodology · measurement

How results are measured

Sector golden tests

Reference question sets with expected answers, replayed at each NCE version on the same corpus. No cherry-picking — same scope, same evaluators.

NCE vN vs vN−1

Every version is compared to the previous one on the same questions. KPIs: average score, pipeline confidence, refusal rate, contradictions detected. No figures published without this protocol.

Auditable protocols

All test sets are documented. Provided under NDA for clients in evaluation. The measurement methodology is part of the delivery contract.

/ your results

Bring your case.
We quantify together.

No generic promises. 30-minute diagnostic: we assess what KnowWeave can do on YOUR data.