Anomaly detection
Identify departures from the usual context, then examine whether they reflect a real change or a data issue. An anomaly begins a question; it is not a final judgment.
MADO / INTELLIGENCE
HOW THE SCOPE CONNECTS
From model output to a clear reason
- Context
Context before conclusions
Connect data to the question and detect anomalies
- Limits
Limits before confidence
Measure uncertainty and what the model cannot know
- Explain
A reviewable explanation
Understandable outputs for governed decisions
FIELDS OF WORK
Understandable outputs. Visible limits.
An analytical-use-case framework that keeps human responsibility explicit rather than replacing it with a model.
Uncertainty measurement
Show what the evidence supports and what remains unresolved. Confidence is not decorative; the conditions under which it is meaningful belong in the explanation.
Explainable outputs
Connect outputs to inputs, assumptions and relevant factors so a reviewer can discuss the reason rather than accept an opaque result.
Governed decision support
Place analysis inside a decision process with an accountable owner and review rules. Outputs support human judgment; they do not impose investment decisions.
ILLUSTRATIVE USE CASE
An alert needs explanation, not automatic acceptance.
- The question
- A model detects an unusual change and the team needs to understand whether it merits a decision review.
- What is examined?
- Data context, the alert’s basis, model validity and assumptions that may make the explanation insufficient.
- Intended output
- A discussable reason with knowledge limits and questions for the decision owner. This explains the purpose; it is not a live alert.
BEFORE ANY COMMITMENT
Clear answers. Explicit boundaries.
Does AI make the decision for me?
No. The purpose is to explain outputs and support review, not delegate independent investment decisions to a model. The decision owner and authority remain explicit.
Does confidence guarantee a correct result?
No. Confidence and uncertainty depend on data, assumptions and validity conditions. There is no blanket accuracy, profit or infallible market-prediction guarantee here.
How do I discuss a use case?
Describe the question, inputs, intended use and reviewer. You can request a scoped Intelligence engagement today for model-output review, explanation or data-quality assessment. Delivery terms come first; this is not an instant AI SaaS activation or a bundled Risk feature.
START NOW
Start with the question, not the model’s name.
Explain the decision to support and what would make the output understandable and useful to its reviewer.
START AN ENGAGEMENT
Scoped project enquiries open today
Request a scoped data, model-review or integration project now. We confirm deliverables, timeline, cost and limitations before an agreement; no instant AI or live-trading platform is activated.
What helps us understand your requirements?
- The analytical question and available input types.
- How the output is used and who reviews it.
- Acceptable error limits and explanation-evaluation criteria.
Send a non-sensitive description only; no passwords, API keys, login details or private client information.