Predictive Precision for Consistent Growth
Theyellow applies adaptive AI modelling to market data, translating complex signals into risk-calibrated supplemental income strategies you can act on with confidence, without requiring a trading desk's worth of time.
A continuously updated view of risk-adjusted recommendations, recalculated as market conditions and your own stated tolerance evolve.
Why Gig-Based Earnings Need a Different Risk Model
Supplemental income earners rarely have fixed hours to monitor markets. Decisions are often made quickly, between jobs, based on incomplete information or short-term market noise. This pattern tends to push risk tolerance higher than intended, and mistakes compound over time.
Theyellow acts as a risk-adaptive layer between raw market data and your decisions. It filters incoming signals against your stated tolerance, so the recommendations you see are already scaled to what you can reasonably absorb, rather than generic market averages.
Three Mechanisms That Do the Analytical Work
Each component addresses a distinct stage of the decision process, from data collection through to the final recommendation presented to you.
Latency-reduced market modelling
Market and pricing data is processed as it arrives, using latency-reduced modelling to keep the gap between an event occurring and it being reflected in your dashboard as narrow as practicable. For someone fitting investment decisions around a shift pattern, this means the information in front of you is current rather than several hours stale.
Dynamic risk-weighting tied to your profile
Rather than applying one risk model to every user, the engine adjusts its weighting dynamically based on your historical responses, stated preferences, and the proportion of income you have indicated you can allocate without disrupting your core earnings. The output shifts as your circumstances shift.
Automated insights, not automated decisions
Findings are compiled into concise, scheduled summaries rather than raw data dumps. The intention is to save the time a manual review would take, while leaving the interpretation and final call with you.
How a Recommendation Is Produced
The process is deliberately sequential, so each stage can be reviewed on its own terms if you want to understand how a figure was reached.
Data ingestion
Structured market feeds, pricing histories, and volatility indicators are ingested and normalised into a common format, removing inconsistencies that would otherwise distort later modelling.
Scenario modelling
The system simulates a range of plausible outcomes under different conditions, weighting each scenario by probability rather than presenting a single best-case projection.
Goal-based optimisation
Simulated outcomes are filtered against the supplemental income target and risk tolerance you have set, producing a narrower set of recommendations suited to your specific position.
Practical Scenarios Across Investor Profiles
These examples illustrate how the same underlying engine serves different objectives, depending on how it is configured.
Portfolio diversification
An investor holding a concentrated position in a single asset class uses the engine to identify correlated risks and suggest a broader allocation across instruments.
Outcome: reduced exposure to a single point of failure
Short-term yield optimisation
A gig worker with irregular but recurring free capital uses shorter modelling windows to identify yield opportunities suited to a limited holding period.
Outcome: capital deployed in line with available time horizon
Long-term risk mitigation
Someone building a supplemental retirement pot uses the adaptive risk engine to gradually lower volatility exposure as their target date approaches.
Outcome: risk profile aligned to a defined time horizon
Common Questions Before Getting Started
These answers are intended to clarify how the platform operates, rather than to persuade. If a question is not covered here, the full FAQ page has further detail.
How is my data handled and secured?
Account and financial data is encrypted in transit and at rest, and access is limited to the systems required to generate your analysis. No data is sold to third parties, and you can request removal of your account data at any time.
How transparent is the AI's reasoning?
Each recommendation is accompanied by a summary of the primary factors that influenced it, such as volatility trends or correlation with your existing holdings. The underlying models are probabilistic rather than deterministic, so figures are presented as ranges and confidence levels rather than single guaranteed outcomes.
Is there a minimum amount of capital required?
There is no fixed capital threshold built into the analysis itself; the engine scales its recommendations to the amount you indicate is available. You remain the final decision-maker on whether and how much to commit at any stage.