Fugore Ruzeli Dashboard for AI-supported liquidity analysis for Swiss SMEs

AI-supported liquidity optimization for Swiss SMEs

Maximize the return on your dormant capital through predictive analytics. Available immediately, without commitment periods.

Liquidity status — example

Available capital CHF 420,000
Forecast 30 days +0.34%
Payout duration Immediately
mechanism

How Fugore Ruzeli creates capital efficiency

Three components form the basis: continuous data analysis, unrestricted availability and a control system according to Swiss standards.

01

Real-time data analysis

The system continuously processes global market data and derives predictive recommendations for action from it. Each recommendation is based on current market conditions, not rigid models.

02

Liquidity without a minimum term

Capital remains available at any time. There is no notice period and no minimum holding period - a key difference to classic fixed-term deposit products.

03

Risk control according to Swiss standards

Data storage and processes follow Swiss compliance requirements. Automated checking mechanisms limit the exposure per position.

Model output

How the model identifies return opportunities

The following table shows an exemplary snapshot: market segments, identified trends and recommendations derived from them.

Market segments · Snapshot

Illustrative data, no real-time connection in this view

Market segment Trend 7T AI recommendation Expected return p.a. Response time
CHF money market funds +0.18% Hold 1.20% < 1 minute
CH corporate bonds +0.41% Increase 1.85% < 1 minute
Short-term deposits EUR −0.09% Reduce 0.95% < 1 minute
Repo market CH +0.27% Hold 1.40% < 1 minute
Representation based on historical market patterns. No guarantee of future returns.
Methodology

Three steps from market date to recommendation

The process is documented and traceable. Every recommendation can be traced back to the underlying data.

01

Aggregation of global market data

Price, interest and liquidity data from relevant markets are continuously recorded and standardized.

02

Pattern recognition by neural networks

Trained models identify recurring patterns and deviations in the collected data series.

03

Automated decision suggestions

The recognized patterns result in concrete, prioritized suggestions for action for capital allocation.

About Fugore Ruzeli

Data-based decisions instead of gut feeling

Fugore Ruzeli develops analysis systems for companies that do not want to leave liquid assets unused but rely on access. The team combines quantitative modeling with requirements from Swiss financial practice.

The focus is on traceability: Each recommendation is linked to the underlying data and prepared in a way that is understandable for decision-makers without a technical background.

More about Fugore Ruzeli
Fugore Ruzeli team analyzing market data
Questions & Answers

Technical and financial details

Direct answers about data storage, payout and model accuracy.

Where is our data stored?

All data is processed and stored exclusively on servers in Switzerland. There is no data transfer abroad.

How does the instant withdrawal work?

Capital is not tied up in illiquid positions, but allocated to instruments that are available at short notice. A retrieval request triggers the return to the reference account without a waiting period.

How reliable are the AI ​​recommendations?

The model is continually validated based on new market data. Recommendations are probabilistic statements based on historical patterns, not guarantees for future developments.

Use capital more efficiently

Request access and receive an assessment of the optimization potential of your dormant capital. The analysis is based on your real liquidity data.