Every feature exists to make one decision clearer
WEETU combines structured data intake, model-driven analysis, and a transparent performance log so that each recommendation can be traced, questioned, and verified.
No black-box scoring. No unexplained outputs.
A system built around four principles
Rather than a single "AI score," WEETU is organised around distinct capabilities that work together — each one designed to reduce a specific type of decision risk.
Structured Data Ingestion
Financial statements, market data, and operational inputs are normalised into a consistent schema before analysis, reducing errors caused by inconsistent formatting or missing fields.
Multi-Model Analysis
Multiple analytical models process the same input independently. Where they disagree, the disagreement is surfaced rather than averaged away silently.
Confidence-Weighted Output
Every recommendation is paired with a confidence range rather than a single definitive figure, reflecting the underlying uncertainty in the data and models used.
Assumption Log
Each output is accompanied by the assumptions and data sources that fed into it, so the reasoning path can be reviewed rather than taken on faith.
Scenario Comparison
Decisions can be tested against alternative assumptions side by side, making it easier to see how sensitive an outcome is to a given variable.
Public Performance Log
Past recommendations and their outcomes are recorded and kept visible, so the system's track record can be checked against reality over time.
Why we show the working, not just the answer
Most analysis tools present a conclusion and ask you to trust it. WEETU is built the other way round: the conclusion is the least interesting part. What matters is the path that produced it.
Each feature below is designed to expose a specific layer of that path — the data that went in, the models that processed it, and the confidence attached to what came out — so that a decision-maker can agree, disagree, or ask a better question.
Feature deep dive
A closer look at how each capability behaves in practice.
Structured intake before analysis begins
Raw inputs — filings, spreadsheets, market feeds — are parsed and mapped to a fixed internal schema before any model touches them. This step catches formatting inconsistencies and flags missing or conflicting fields early, rather than letting them silently distort a later output.
Benefit: fewer downstream errors caused by unclean or mismatched source data.
Disagreement is information, not noise
Instead of collapsing multiple analytical approaches into one blended figure, WEETU keeps each model's output visible. When models converge, confidence is higher. When they diverge, that divergence is reported explicitly rather than hidden inside an average.
Benefit: you can see where the analysis is stable and where it is genuinely uncertain.
Every output carries its own footnotes
A recommendation is only as useful as the assumptions behind it. Each output in WEETU links back to the specific data points, time period, and modelling assumptions that produced it, so it can be checked rather than simply accepted.
Benefit: faster, more informed review before a decision is acted on.
Stress-test a decision before committing to it
Key assumptions — growth rate, cost inputs, timing — can be adjusted and compared side by side, showing how sensitive the recommendation is to each one. This turns a single-point answer into a range of plausible outcomes.
Benefit: a clearer sense of how much a conclusion depends on any one assumption holding true.
The log is a feature, not an appendix
Every recommendation issued through WEETU is timestamped and recorded alongside its stated confidence level. As real-world outcomes come in, they are added to the same record.
This does not guarantee any future result — but it means the system's history is available for scrutiny rather than described in marketing terms alone.
Illustrative figures shown above represent the layout of the log, not live data.
See how each feature behaves against real decisions
Review the assumption structure, model outputs, and logged history before relying on WEETU for a decision of your own.
Get in TouchAll outputs are decision support, not financial advice.