Sidevellop applies predictive risk models to your freelance income, flagging when surplus capital is exposed and suggesting adjustments before a slow quarter becomes a cash-flow problem.
No card required for the trial period. Full data export at any time.
Sidevellop reads your income history alongside live market indicators to estimate how long your current contracted work will sustain you, and where surplus capital could be positioned if that runway shortens.
Most freelance income is irregular by design. Rather than treating each invoice in isolation, the model builds a rolling forecast of your likely earnings over the next quarter, comparing it against your historic spending baseline to identify when a gap is forming.
Income projections are adjusted for sector-specific variance, rather than assuming a flat average month.
When work slows, the platform proposes allocation changes sized to your actual liquidity, not generic model portfolios.
Each new invoice or contract update refines the forecast, so recommendations stay current rather than static.
Confidence bands widen with forecast horizon, which is reflected directly in the sizing of any recommendation, not hidden from the user.
Rather than publishing testimonials, Sidevellop maintains an open, aggregate log of community outcomes. Every figure below is anonymised and updated on a rolling basis, so prospective users can review method before committing capital.
| Reporting period | Strategy cohort | Risk-adjusted return | Mean variance score | Status |
|---|---|---|---|---|
| Q1 cohort, rolling 90 days | Conservative capital preservation | Published on log | Published on log | Verified |
| Q1 cohort, rolling 90 days | Balanced growth allocation | Published on log | Published on log | Verified |
| Q4 cohort, rolling 90 days | Volatility-managed surplus | Published on log | Published on log | In review |
Figures represent cohorts of at least 40 anonymised accounts, never an individual user's results.
Allocations are scored against a mean variance framework, balancing expected return against observed volatility for that cohort.
Headline figures are shown net of estimated downside risk, not gross performance, to avoid overstating outcomes.
The workflow is deliberately narrow: connect, analyse, recommend. Each stage is visible to you, with no black-box step in between.
Link invoicing, business banking, and contract records through read-only connections, so the model sees real cash flow rather than estimates.
The engine compares your income pattern against anonymised sector data to flag periods of likely slowdown or surplus ahead of time.
You get a short list of allocation options sized to your available surplus, each annotated with its expected risk-adjusted return.
For a freelancer, losing capital during a slow month carries more weight than missing an upside opportunity. The models are built with that order of priority: capital preservation is checked before any growth recommendation is surfaced.
When market volatility rises, recommendations automatically shift toward lower-variance allocations, and you are shown the reasoning behind that shift rather than a single opaque score.
Sidevellop was built around a simple observation: self-employed professionals handle both the operating and the investing side of their finances, usually without the tooling a small finance team would have. The platform brings a consistent, documented decision process to that gap, rather than leaving allocation choices to instinct alone.
Straightforward answers on data, methodology, and verification, with no marketing language attached.
Account connections are read-only, and raw transaction data is never sold or shared with third parties. Figures used in the Public Performance Log are aggregated and stripped of identifying details before publication.
Models are trained on anonymised, aggregated freelance income and market data, combined with publicly available market indicators. No individual client is identifiable within the training set, and the methodology document is available on request.
Rather than displaying testimonials, we publish rolling, cohort-level outcomes for each strategy type. These figures are recalculated on a fixed schedule and logged publicly, so results can be reviewed independently of marketing copy.
Yes. Full data export is available at any point during or after a subscription, and account closure removes your records from active analysis within the stated retention period.
Sidevellop provides decision-support analytics and recommendations. It does not execute trades on your behalf, and any allocation decision remains yours to make.
Join 5,000+ UK freelancers using AI to optimise their project intervals, backed by a public performance log you can review before you commit.