Steel Rendvane predictive data modelling dashboard for automated dollar-cost averaging
Features

Structured tools built for irregular income

Steel Rendvane combines predictive data modelling with automated, rules-based investing so contributions keep moving even when your earnings don't follow a fixed schedule.

Designed around variable income, not against it

Most investing tools assume a predictable payday. Gig and freelance income rarely works that way. Steel Rendvane was built to read patterns in irregular cash flow and translate them into a structured, automated dollar-cost averaging plan — so contributions are consistent even when income isn't.

Core Technology

What Steel Rendvane actually does

Every feature below exists to solve one problem: keeping a disciplined contribution schedule running on top of unpredictable earnings.

Steel Rendvane interface showing predictive contribution scheduling

Predictive Data Modelling

Steel Rendvane analyses historical income patterns to forecast likely cash flow windows, helping set contribution amounts that are realistic rather than fixed and arbitrary.

Automated Dollar-Cost Averaging

Once a plan is set, contributions execute on a defined schedule without manual intervention, reducing the temptation to time entries or skip periods.

Adaptive Contribution Scheduling

Schedules can adjust as income data updates, so the plan reflects current earning patterns instead of a static assumption made months earlier.

Rules-Based Consistency

Contribution logic is defined upfront and applied uniformly, removing ad-hoc decision-making from the process of building a position over time.

How It Works

From income data to a running plan

A structured sequence, repeated automatically for as long as the plan is active.

Connect income data

Earnings history is used as the basis for the predictive model, giving it a realistic picture of cash flow rhythm rather than a single average figure.

Set contribution parameters

Define contribution frequency, minimums, and limits. These parameters form the rules the automated system follows going forward.

Let the schedule run

Contributions execute automatically according to the model and your parameters, with adjustments applied as new income data comes in.

Built for consistency over guesswork

Dollar-cost averaging works because it removes timing decisions from the equation. Steel Rendvane extends that principle to income that doesn't arrive on a fixed schedule.

Automated Execution

Contributions run on the schedule you set, without needing to manually initiate each transaction.

Data-Informed Timing

Contribution windows are informed by modelled income patterns rather than a single fixed date.

Adjustable Rules

Parameters can be revisited and updated as your income situation changes.

Transparent Logic

The rules driving each contribution are visible and defined by you, not hidden inside a black-box process.

Applied Use

Where these features fit

A look at how the same core system supports different income situations common in gig work.

Variable Weekly Income

Smoothing out week-to-week swings

For drivers, couriers, and platform workers whose weekly earnings fluctuate, predictive modelling helps set contribution amounts that flex with typical highs and lows instead of assuming a flat weekly figure.

Weekly Income Pattern
Seasonal Freelance Work

Accounting for busy and quiet periods

Freelancers with seasonal demand can rely on adaptive scheduling to scale contributions during higher-earning stretches and ease off automatically during slower months.

Seasonal Adjustment
Multiple Income Streams

Combining several irregular sources

Where income comes from more than one gig platform or client, the model works from combined data to build a single coherent contribution schedule rather than treating each source separately.

Combined Income Streams

See how a structured plan fits your income

Get started with Steel Rendvane and set up a contribution schedule built around your actual earning pattern, not a fixed assumption.

Get Started

Investment involves risk of loss. Predictive modelling is based on historical data and does not guarantee future outcomes or income accuracy. Please review your circumstances carefully before setting up an automated contribution plan.