Neuro-symbolic root-cause analysis
A language model is good at generating plausible explanations and bad at being sure any of them are right. A symbolic system is exactly the reverse. Fibric's root-cause analysis uses each for what it is good at, so the answer to "why is this going wrong" is a chain you can check, not a confident sentence you have to believe.
The problem with asking a model "why"
When an operation breaks, the first question is always the same: why. An order is going to ship late. A wing of the hotel is running cold. A warehouse station has stalled. Ask a capable language model and it will give you a fluent, plausible explanation almost instantly. That is the problem. It is fluent and plausible whether or not it is correct, and in operations a confidently wrong root cause is worse than no answer, because it sends a real team to fix the wrong thing.
The failure mode here is well documented. Large language models are excellent at producing hypotheses that sound like reasoning but are not grounded in the actual state of the system. They hallucinate causes the way they hallucinate citations: convincingly. For a system that is about to take an action in the physical world, "convincingly" is not good enough.
What symbolic systems get right, and wrong
The classical alternative is symbolic: a model of the system, a set of rules, and a deterministic engine that infers cause from observed state. Symbolic root-cause analysis has a property the language model lacks. When it concludes that A caused B, it can show its work, and the conclusion follows from the facts and the rules with certainty. You can audit it. You can trust it.
What symbolic systems are bad at is the open world. They are brittle at the edges, they require someone to encode the rules in advance, and they fall silent the moment reality presents a situation the rules did not anticipate. The very rigidity that makes them trustworthy makes them narrow.
The division of labor
Fibric's approach follows a line of work, sometimes called neuro-symbolic AI, that pairs a neural model's flexibility with a symbolic engine's rigor. The two play distinct roles, and keeping the roles distinct is the whole point.
The neural side does what it is good at: generate hypotheses. Given the unified operational picture, the model proposes a ranked set of candidate root causes. It is allowed to be creative, even speculative, because nothing it proposes is trusted yet.
The symbolic side does what it is good at: verify against the facts. Each candidate cause is checked against the real, governed data and the known structure of the system. A hypothesis survives only if the evidence supports it deterministically. A hypothesis that cannot be grounded is discarded, no matter how plausible it sounded.
What comes out the other side is not a sentence. It is a chain: the cause Fibric believes, the evidence that supports it, and the candidates it ruled out and why. That chain is what makes the conclusion auditable. An operator does not have to take the answer on faith. They can read the reasoning the way they would read a colleague's note, and disagree with a step if they know something the data does not.
Why this matters before an action
Root-cause analysis is not an academic exercise on Fibric. It sits directly upstream of action. The platform's job is not to explain a problem and stop; it is to do something about it. That makes the grounding non-negotiable. An action is only as trustworthy as the reasoning that proposed it, and a hallucinated cause that drives a real action is precisely the failure the whole trust spine exists to prevent.
So the neuro-symbolic chain feeds the same governed flow as everything else: the model proposes a cause and a plan, the deterministic layer disposes, and any policy you set can veto the resulting action before it happens. The explanation and the action share one audit trail. When Fibric holds an order, you can follow the receipt all the way back to the grounded cause that justified it.
Where we are honest about the limits
This is a direction, not a solved problem. Grounding a hypothesis requires that the relevant facts are actually in the operational picture; a cause that depends on a signal no connector provides cannot be confirmed or denied, only flagged as unverifiable. We would rather surface "we cannot confirm this" than launder a guess into a certainty. The honest "unverifiable" is itself a useful output, because it tells you where your sensing has a gap.
Grounding
- Neuro-symbolic AI: the research program of combining neural learning with symbolic reasoning to get both flexibility and verifiable inference.
- Model-based diagnosis: the classical, symbolic tradition of inferring root cause from a model of system structure and observed state.
- Hypothesis-and-test: treating an LLM as a generator of candidate explanations that an external, deterministic checker must confirm before they are trusted.