Quilla Fondorio — financial analysis dashboard with yield curve on green background

Artificial intelligence for business treasury

Put idle capital to work with historically validated strategies

Quilla Fondorio converts market data into concrete liquidity allocation recommendations, supported by simulations of real scenarios from the latest economic cycles.

The cost of not deciding

Immobile liquidity also has a price

Holding unallocated capital is not a neutral decision. When inflation exceeds the performance of traditional accounts, each month of inaction represents a real loss of purchasing power, known as opportunity cost.

The problem is not the lack of data, but the time to process it. An SME owner reviews interest rates, market reports and cash projections among dozens of operational tasks, and volatility does not wait for the day to end.

Quilla Fondorio transfers this analysis to models that process market information continuously, without replacing the entrepreneur's judgment, but freeing him from the burden of constant surveillance.

  • 1
    Inflation above performance. Cash in checking accounts loses real value month after month without it being immediately noticeable.
  • 2
    Insufficient manual analysis. Spreadsheets do not react to the speed of the markets or incorporate thousands of historical scenarios.
  • 3
    Limited manager time. Whoever runs an SME rarely has hours dedicated to market surveillance.
  • 4
    Risk of reactive decisions. Without an analytical framework, treasury decisions tend to be made late and under pressure.

Methodology, not black box

How we get from data to a recommendation

Each stage of the process is documented and can be explained in concrete terms, without resorting to abstract promises about artificial intelligence.

1

Data integration

We connect market sources and, if applicable, client treasury information under previously defined security and privacy protocols. No data is used outside the agreed scope.

2

Predictive modeling

Candidate strategies are backtested: they are simulated against historical data from different market cycles to measure their behavior before being proposed.

3

Decision support

The system translates the results into tailored recommendations, with their logic and historical performance visible, so that the final decision remains with the employer.

Main features

Three capabilities that support each recommendation

Predictive analysis

Models trained on long historical series

Predictive models identify historical performance patterns across asset classes and market conditions, and estimate how a strategy might perform in comparable future scenarios. These are not absolute predictions, but probabilities calculated on verifiable data.

Risk scoring

A risk score designed to protect the principal

Each strategy receives a score based on its historical volatility and its performance during periods of market decline. The goal is not to maximize performance at any cost, but rather to preserve capital before seeking growth.

Real-time dashboards

Continuous visibility into every active decision

The dashboards show the current exposure, cumulative performance and volatility indicators for each ongoing strategy. The information is continually updated so that sovereignty over financial decisions remains in the hands of the entrepreneur.

The proof is in the process

The rigor behind each recommendation

We do not offer narrated success stories, but rather the method with which each suggestion is constructed. Any recommendation comes from back testing, not intuition or promises of future performance.

Systematic backtesting

Each strategy is evaluated against multiple historical market cycles, including periods of contraction, before being deemed suitable for presentation to a client.

Data scale

The models are trained and validated on large volumes of market information, which makes it possible to detect patterns that a manual analysis would be difficult to process in the same time.

Safety standards

Each client's information is treated under principles of data minimization and restricted access, aligned with Spanish and European data protection regulations.

A partner, not a black box

Technical analysis explained in verifiable terms

Quilla Fondorio was born from the idea that artificial intelligence applied to finance must be able to be explained, not just shown. Each recommendation is accompanied by its calculation logic, its simulated historical performance and its associated risk level.

We work with SME owners and private investors who are looking for informed decisions about their liquidity, not bets based on intuition. The balance between technology and human judgment remains at the center of how we design the product.

Quilla Fondorio — team reviewing financial analysis models

Your capital, powered by artificial intelligence

Every treasury decision remains yours. Quilla Fondorio provides data analysis, historical validation and continuous monitoring so that that decision is made with complete information, not with time pressure.

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