Every Answer Comes With Its Reasoning
Neuro-symbolic inference you can audit: a neural layer proposes, and a symbolic layer checks every step against formal constraints.
The capability.
A language model generates plausible conclusions, and plausible is not the same as correct. A reasoning engine splits the work in two: the neural layer proposes candidate inferences, and an ontology layer verifies each one against formal constraints.
Only the inferences that pass are allowed to stand. Every answer carries the chain it came from, so a person can check how it was reached.
Our approach.
The constraints live in the ontology, so every reasoning engine we build sits on a knowledge graph. Proposal and verification stay separate mechanisms by design, because a model that checks its own proposals is grading its own work.
It is the same research line as our knowledge graphs: inference over a graph that keeps changing. That work is part of our ongoing PhD research.
In practice.
Any decision someone has to defend. When a regulator, a board, or a counterparty challenges an answer, the reasoning chain is what you show them.
It also makes an agentic system safer to leave running. An agent whose every inference is checked can take on longer tasks before a person reviews the result.
In Medjourney, decision graphs work like an automaton: each state allows only certain next steps, and the agent chooses among them. It is process automation with judgment inside fixed bounds, and every step leaves a trace.
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