Turing Standard

Adaptive Knowledge Engine

The model is never on the path to a fact.

AKE reads regulated documents into a bi-temporal knowledge graph. Every answer is deterministic, cited to its source page, and reproducible from the same input.

Greener by design

No model inference per ingested record. Compute scales with data volume, not GPU inference, at any corpus size.

EU AI Act

Source traceability by construction. Every answer grounded to its exact document and page. Audit-ready from day one.

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Deterministic

Same question, same answer, always. No probabilistic variance between runs.

Cited by construction

Every answer carries a source page citation, or the engine refuses to answer.

$0 marginal graph cost

No model call per ingested record. Ingest cost is compute, not per-document inference.

Bi-temporal

History is never deleted. The graph records what was true and when. Fully auditable.

Cited by Construction

Source page or it does not ship.

AKE answers carry a citation to the exact document, table, and page that grounds the fact. If the engine cannot locate a ground truth, it refuses rather than guessing.

LLM Responseno citation

“The maximum continuous discharge current for this cell type is approximately 10 amps under standard operating conditions. This may vary depending on temperature and application requirements.”

No source. Cannot be verified. Cannot be used in audit.

AKE Responsecited

Max. continuous discharge current: 10.0 A (at 25 °C)

LG Energy Solution INR21700 M50T·Table 3·p. 5

Exact source page. Reproducible. Audit-ready.

Query Types

Three classes of question AKE answers.

Specification lookup, Retrieval Augmented Generation (RAG), and Configuration Validation, each handled by the same deterministic engine with full citation.

Exact deterministic lookup of a property value

Query

What is the maximum continuous discharge current for cell model INR21700 M50T at 25 °C?

Answer

10.0 A

Sources
[LG Energy Solution INR21700 M50T Datasheet · Table 3 · p. 5]

Deterministic lookup. The same query returns the same answer from the same graph, always.

Architecture

From raw document to cited answer.

AKE ingests documents with deterministic code, builds a bi-temporal graph at zero marginal model cost, and serves cited answers through three query types. Click any stage to see what happens inside it.

Click any stage to learn what happens inside it.

Comparison

Five dimensions that matter to an auditor.

DimensionRAG over TextGraphRAGAKE
Model on the answeryesyesno, solver decides
Model on graph constructionn/ayesno, deterministic
Per-record model costembedding onlyper-record extraction$0
Reproducible from same inputnonoyes
Citation on every answeroften partialoften partialby construction

Deployment

Three modes, zero external model calls.

SaaS

Managed graph infrastructure. Your documents, your queries, your data isolated from other tenants.

Private Cloud

Deployed inside your VPC. No document or query leaves your perimeter.

Air-Gap

Full on-premises deployment with no external network calls. Supported for defence and classified environments.

AKE Whitepaper

Grounded by construction.

The AKE technical whitepaper covers the full architecture: why regulated documents defeat ordinary tools, how bi-temporal graph construction works without per-record model inference, and the formal proof that grounding is a structural guarantee, not a quality target.

Includes a worked comparison of RAG, GraphRAG, and AKE across the four dimensions that matter to an auditor: accuracy, traceability, reproducibility, and cost.

Architecture deep-dive with formal definitions
Comparison: RAG · GraphRAG · AKE
EU AI Act compliance mapping
Deployment guide including air-gap scenarios

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