Hvordan beskyttes virksomhedens data?

Data krypteres både under transmission og opbevaring, og adgang er begrænset til de systemer og funktioner, der er nødvendige for analysen. Platformen er bygget til at håndtere finansielle data med samme forsigtighed, som du selv ville anvende internt.

Hvordan forklares AI-modellens anbefalinger?

Hver anbefaling ledsages af en begrundelse, der viser, hvilke data og hvilke tærskelværdier der har udløst den. Modellen er ikke en "black box" — beslutningsgrundlaget kan altid gennemgås, før en anbefaling følges.

Hvor lang tid tager det at komme i gang?

Opstarten indebærer en indledende gennemgang af virksomhedens likviditetsbehov og risikorammer, hvorefter systemet konfigureres til at overvåge de relevante data. Tidsforbruget afhænger af kompleksiteten i virksomhedens økonomiske struktur og aftales konkret ved opstartssamtalen.

TrygantisApp platform for liquidity management and risk management for Danish companies

Data-driven decision support for the company's liquid capital

TrygantisApp analyzes market data and your liquidity continuously and converts patterns into concrete recommendations, so that capital does not stand still without a purpose and risk is not overlooked between quarterly reports.

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Example of continuous monitoring

Liquidity ratio Stable

Market volatility Moderate

Recommended Action Retain

Illustrative example of the platform display — not an actual customer sample.

The starting point

Unspent liquidity has a price, even when the account looks unchanged

Many Danish SMEs and private investors keep significant cash reserves as a safety buffer. It is an understandable decision, but it has a hidden cost: the purchasing power of the reserve gradually decreases while it remains passive.

At the same time, markets change faster than a human review every month or quarter can keep up with. Risk that occurs between two reviews often only becomes visible when it has already had an effect.

Inflation works continuously Even a stable cash balance loses real value month by month when the price level rises faster than the interest on the deposit.
Manual monitoring has gaps Quarterly or monthly reviews do not capture sudden swings in market conditions that occur between two check-ins.
Risk and return should be assessed together Decisions to move capital require an ongoing balancing of liquidity needs, time horizon and risk tolerance — not a one-time assessment.

The TrygantisApp team's approach to data analysis and risk modeling

About the platform

Built as a decision support, not as an automated trading robot

TrygantisApp is designed to support, not take over, the financial decision-making process. The platform collects and structures data, highlights relevant patterns and makes recommendations — the final decision remains with the business owner or investor.

The approach is deliberately conservative: the focus is on reducing unnecessary risk and improving the basis for decisions, rather than promising specific returns.

Three components that work together on risk management and optimization

The platform's engine is built around predictive analysis, systematic risk management and continuous optimization — each component solves a specific part of the task.

Predictive analytics

Patterns are identified before they become visible in the overall accounts

The models analyze historical and current market data to identify trends in interest rate development, liquidity needs and volatility, which are often not visible from a manual review of individual items.

Risk management

Automated capital protection based on defined limits

You determine the framework for risk tolerance and liquidity needs. The platform then monitors the market 24 hours a day and marks deviations that people typically only discover after the day's trade has ended.

Real-time optimization

Recommendations are updated when conditions change — not until the next meeting

When market conditions or liquidity needs change, the system recalculates the consequences and presents an updated recommendation that can be reviewed and approved without delay.

Method

This is how the process from data to recommendation works

Transparency in the method is a prerequisite for trust. Below are the three steps the system follows for each analysis.

Data collection

Relevant market data, interest rate trends and the company's own liquidity information are collected and structured continuously from approved sources.

AI analysis

The models assess data against your set limits for risk and liquidity needs and identify relevant patterns or deviations.

Actionable recommendations

The result is presented as concrete, explained recommendations — ready for review and approval, not as an automatically executed transaction.

Application

Three typical situations the platform is built to support

The scenarios below reflect typical challenges faced by Danish SMEs and private investors with significant cash reserves.

Liquidity management

A company with seasonal turnover must have access to cash at short notice, but at the same time does not want the entire reserve to go unused. The platform calculates how large a proportion can realistically be optimized without compromising operational reliability.

Protection against market volatility

In the event of sudden fluctuations in market conditions, the system identifies changes in the risk picture and highlights when a position should be reassessed, rather than waiting for the next scheduled review.

Strategic reinvestment

An investor with released capital from a previous investment receives a structured assessment of how the funds can be reinvested within the established risk framework, based on current market data.

Frequently asked questions

Answers to the technical questions we get most often

How is the company's data protected?

Data is encrypted both during transmission and storage, and access is limited to the systems and functions necessary for the analysis. The platform is built to handle financial data with the same care you would use internally.

How are the AI model recommendations explained?

Each recommendation is accompanied by a rationale showing which data and which thresholds triggered it. The model is not a "black box" — the decision basis can always be reviewed before a recommendation is followed.

How long does it take to get started?

The start-up involves an initial review of the company's liquidity needs and risk framework, after which the system is configured to monitor the relevant data. The time consumption depends on the complexity of the company's financial structure and is specifically agreed upon at the start-up meeting.

Get a concrete picture of how your liquidity can work more actively

A review is based on your actual liquidity situation and risk tolerance — not on general assumptions.

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All recommendations from TrygantisApp are based on historical and current data and do not constitute a guarantee of future returns. Capital deployment decisions remain the sole responsibility of the company or investor.