Platform Features
Every module built to answer one question: what does the data actually show?
Flexyfoot is structured around a small set of deliberate capabilities. No bundled extras, no noise — each feature exists to support evidence-based decisions before capital moves.
Core Modules
What the platform actually does
Six working components, each addressing a distinct part of the research-to-decision process.
Structured Data Intake
Raw market and company data is normalised into a consistent format before any analysis runs, reducing the risk of comparing mismatched figures.
Scenario Modelling
Adjust assumptions — growth rates, cost pressures, time horizons — and see how outcomes shift, rather than relying on a single static projection.
Risk Flagging
Positions and assumptions are checked against defined thresholds, surfacing concentration or volatility concerns instead of leaving them buried.
Benchmark Comparison
Every output is placed alongside a relevant reference point, so a result is judged against something, not viewed in isolation.
Audit Trail
Each analytical step — inputs, adjustments, outputs — is logged in sequence, so a conclusion can be traced back to its origin.
Plain-Language Summaries
Technical outputs are accompanied by a short written explanation of what changed and why, aimed at reducing misinterpretation.
Why Structure Matters
Features designed to be checked, not just trusted
Most tools present a result and ask the user to take it on faith. Flexyfoot is built the other way round: every feature leaves a visible record of what it did, so a first-time investor — or anyone reviewing the account later — can confirm how a figure was reached.
This matters most in the early stages of investing, when the temptation is to accept a clean-looking number without asking what sits behind it. The features below exist specifically to keep that question answerable.
Design principle
If a feature can't show its working, it doesn't belong on the platform. That rule applies to every module listed on this page.
In Practice
A sample feature sequence
An illustration of how the modules interact in a single review — figures below are representative of the format, not live data.
What this sequence shows
No single step produces a final answer on its own. The value comes from the chain — each module checking or contextualising the one before it.
What it doesn't do
The sequence does not issue a buy or sell instruction, rank an outcome as "good," or substitute for independent judgement.
Getting Started
How the features are typically used
Load a position or scenario
Enter the figures or assumptions you want examined. The intake module standardises the format automatically.
Run and compare
Use scenario modelling and benchmarking together to see where an assumption holds up and where it doesn't.
Review the trail
Check the audit log and summary before treating any output as a basis for a decision.
See the feature set applied to a real scenario
Request access and work through a sample review using your own assumptions, with the audit trail visible at every step.
Request AccessNo commitment implied. Figures and examples on this page are illustrative of format and methodology only.