The reasoning and inference system that orchestrates flows of specialised AI Agents, combining logic and deterministic reasoning over the knowledge graph with predictive capabilities from deep learning.
| LLMs/RAG | Logos | |
|---|---|---|
| Reasoning type | Statistical correlations | Formal logical inference |
| Correctness guarantee | Probabilistic (not guaranteed) | Mathematical (demonstrable) |
| Explainability | Post-hoc approximations | Complete deductive chain |
| Traceability | Limited to source documents | Complete back to original axioms |
| Inconsistency detection | Non-systematic | Automatic and formal |
| Knowledge updates | Costly retraining | Immediate and incremental |
| Regulatory compliance | Difficult to certify | Built in by design |
Formal correctness
Every inference produced by Logos is a valid logical consequence of the knowledge base and the ontology. There are no spurious conclusions. The guarantees are not empirical: they are mathematically demonstrable properties of the reasoning algorithms employed.
Computational completeness
Every valid logical consequence derivable from your axioms will be found by the reasoner. No valid inferences are missed due to algorithmic limitations. If something follows logically from your knowledge, Logos will find it.
Complete reasoning traceability
Every conclusion includes the complete deductive chain that underpins it: ontological axioms applied, facts consulted, inference rules executed. The justification is not post-hoc: it is the exact record of the reasoning process.
Built-in regulatory audit
The architecture satisfies the explainability and transparency requirements of the EU AI Act by design. Every decision can be inspected, every step validated, every conclusion traced back to its origin. Regulatory compliance is not a bolt-on: it is inherent.
Automatic inconsistency detection
The reasoner formally identifies logical contradictions and constraint violations before they propagate errors. If you attempt to assert something that contradicts your axioms or existing data, the system detects it immediately and pinpoints the source of the conflict.
Decidability
Every query terminates in finite time with a definitive answer. There are no cases where the system enters infinite loops or produces unpredictable behaviour. The complexity is formally characterised and the algorithms optimised for practical use cases.
Controlled expressivity
OWL DL offers the perfect balance between expressive power and computational tractability. Expressive enough to model complex domain constraints, restrictive enough to guarantee decidability. The optimal middle ground between databases and full first-order logic.
Evolution without retraining
Adding new knowledge requires neither rebuilding indices nor retraining models. The reasoner integrates incremental information and recalculates only the affected inferences. Your reasoning system evolves continuously alongside your organisational knowledge.
Logos, deterministic reasoning for GNOSS
Semantic AI Platform
Argos
Lokut
