Artificial Knowledge
An AI that distinguishes what is true from what is believed
// What it is
A new generation of systems that, unlike generalist models, maintains the distinction between facts, opinions, norms and hypotheses — designed for historical archives, cultural heritage, scientific research and the legal domain, where these differences are essential.
// Where we are
// Technical details · for industry insiders +
Technical problem solved
LLMs treat every token equally. Crucial distinctions (what is true, what is believed, what is normative, what is hypothesis, what is verified) collapse into a single probability distribution. For historical archives, cultural heritage, scientific knowledge and law, an infrastructure is needed that preserves these distinctions.
Technical positioning
Epistemic infrastructure · stratified graph + formal verification
Architecture / approach
- 01 Level 1 — Empirical Manifold · typed primary observations
- 02 Level 2 — Dynamic Ontology · classes that evolve with the domain
- 03 Level 3 — Neurosymbolic Bridge · LLM ↔ symbolic anchoring
- 04 Level 4 — Epistemic Judgment · EAL Engine for modal judgment
- 05 Level 5 — Architectonic · meta-level of global coherence
- 06 Wittgensteinian tripartite ontology — separated fact/value/grammar
Technology stack
TRL and development status
Reference standards
// Related services
The expertise behind Artificial Knowledge directly powers some of our project-based services:
Approach to intellectual property
Why we patent and what it means for clients