Optimising financial decisions between projects
KlasterFin applies backtested AI models to your project-based income, helping you manage cash flow and weigh investment risk with evidence rather than guesswork.
KlasterFin applies backtested AI models to your project-based income, helping you manage cash flow and weigh investment risk with evidence rather than guesswork.
Freelance income rarely follows a steady curve. KlasterFin's engine is trained to read that irregularity rather than smooth it away.
The system tracks incoming and outgoing balances as they change, flagging shifts in your surplus so decisions are based on your current position, not last month's statement.
Before any allocation is suggested, the model estimates how much volatility your business can reasonably absorb during quieter trading periods.
Allocations adjust in small increments as your income pattern changes, rather than requiring a manual review each time a project ends.
Every recommendation passes through a backtesting stage before it reaches your dashboard.
The AI reviews decades of UK market data alongside anonymised freelance income cycles, looking for periods where similar cash flow patterns previously occurred.
Illustrative pattern matching across historical cycles
Each candidate strategy is run against verified historical returns to see how it would have performed, including during downturns, before it is ever presented as a recommendation.
Backtested performance across varied market conditions
As new income and expenditure data arrives, the model re-checks its assumptions and adjusts guidance accordingly, rather than relying on a single static forecast.
Ongoing recalibration as income data updates
An independent consultant typically earns the bulk of their annual income across a few concentrated quarters, followed by a slower stretch with fewer contracts. Using KlasterFin, they set aside project surplus during peak months and let the model identify low-risk, liquid growth opportunities that align with the length of the expected off-peak period, rather than locking funds away for longer than the gap requires.
KlasterFin does not optimise purely for growth. Every recommendation weighs how easily funds can be accessed against how much risk they carry, on the basis that a freelancer's surplus often needs to be available sooner than a typical long-term investor's.
Allocations are matched against your likely need to withdraw funds between contracts, not against a fixed investment term.
The model applies a ceiling on exposure to short-term market swings, based on your historical income variability.
Backtests draw on publicly available historical market records rather than simulated or synthetic price data.
The system does not suggest high-volatility instruments as a default, regardless of projected returns.
KlasterFin was built around a simple observation: most financial planning tools assume a steady salary. Freelancers and consultants do not have that luxury, and the gaps between contracts are exactly when financial decisions matter most.
The platform combines predictive analytics with a risk-first framework, so that the guidance you receive reflects both the opportunity in front of you and the reality of an irregular income.
Account and transaction data are processed to generate recommendations and are not sold or shared with third parties for marketing purposes. Data is used solely to run the analysis you request within your account.
The models are trained on publicly available historical market data and anonymised patterns of project-based income. No individual client data is used to train models for other users.
Backtesting shows how a strategy would have performed historically, including through past periods of volatility. It is a way of stress-testing an approach against real events, not a promise of future results, and current conditions are factored in through ongoing recalibration.
No fixed minimum is required. The model adjusts its suggestions in proportion to the surplus available, whether that is a modest buffer or a larger seasonal peak.
KlasterFin provides data-backed suggestions rather than personalised regulated advice. Many users treat it as a starting point for their own decisions or as a reference point when speaking with a professional adviser.
No commitment required to view initial backtested models.