Artificial intelligence can process enormous volumes of financial information, identify patterns, calculate ratios and generate investment assessments within seconds. But speed and analytical power do not eliminate a fundamental problem: an investor ultimately has to understand why a conclusion is justified. Data2Space applies immersive 3D visualization to bridge precisely this gap. Instead of leaving the interpretation entirely to AI, it makes observations, investment criteria and shareholder demands spatially visible and therefore intuitively understandable.
The starting point is not an AI opinion, but the underlying information. Annual reports, financial statements, operational KPIs, ownership structures, governance information and other relevant data can be transformed into a spatial environment. The investor can then explore the relationships between these elements rather than merely reading them sequentially in tables and text. The objective is not to replace financial analysis with attractive graphics. It is to make the analytical structure visible.
Making Observations Visible
A financial observation often appears trivial when expressed as a sentence: revenue increased, EBITDA declined, cash increased, customer acquisition costs rose, or the company remains loss-making. The difficulty is understanding how these observations relate to one another.
The Smartbroker Holding example illustrates the point. Between 2024 and 2025, revenue increased by 30.1%, customers by 37.8%, assets under custody by 47.0% and trades by 78.2%. At the same time, operational EBITDA after customer-acquisition costs fell from €9.0 million to −€0.3 million, while customer-acquisition costs increased from €1.7 million to €9.8 million.
In a conventional report, the investor has to hold these numbers in working memory and construct the relationship mentally. Data2Space can turn them into spatial structures: growth can be represented as expanding volumes, costs as counterweights, and profitability as a layer that can be inspected separately. The investor does not merely receive the statement that growth is expensive; the economic relationship becomes something that can be seen.
This is particularly powerful when headline figures are potentially misleading. The 2025 EBITDA after customer-acquisition costs looks dramatically worse than in 2024. Yet EBITDA before customer-acquisition costs was €9.5 million in 2025 versus €10.7 million in 2024. The spatial representation can therefore separate underlying operating performance from the deliberate investment in customer acquisition.
Turning Investment Criteria into Objects
Investment criteria are often expressed as abstract concepts: economic moat, balance-sheet strength, earnings quality, management quality, capital allocation, margin of safety or return on capital.
The Data2Space method translates these abstractions into objects and relationships that can be explored spatially.
A strong balance sheet, for example, can be represented as a structure whose dimensions reflect equity, cash, debt and intangible assets. In the Smartbroker case, €19.9 million of cash, €2.9 million of bank debt and a 73% equity ratio create a very different visual structure from a highly leveraged company. At the same time, the €15 million of goodwill can be explicitly distinguished from more tangible financial resources.
The same principle applies to the concept of an economic moat. Smartbroker’s media business has structural advantages based on reach, brand and the connection between financial information and brokerage. The brokerage business, however, faces intense competition and comparatively low customer switching costs.
Instead of reducing this conclusion to a single AI-generated score such as “Moat: 6/10”, Data2Space can expose the individual components. The investor can see where the moat exists, where it is weak, and which assumptions support the assessment.
Making Demands Testable
The method becomes even more valuable when investment analysis does not stop at describing a company but formulates concrete demands.
An investor might require:
- sustainable positive free cash flow;
- transparent reporting of one-off effects;
- ROE or ROCE targets;
- shareholder-friendly capital allocation;
- an independent supervisory board;
- stronger audit quality;
- protection against unnecessary dilution;
- better capital-market communication.
These demands can become spatial objects connected to the underlying evidence.
The Smartbroker implementation review provides a practical example. Of 14 previously formulated demands, three were fulfilled, one largely fulfilled, four partially fulfilled and six not fulfilled.
In a spatial environment, this does not have to remain a list of fourteen statements. Each demand can become a node whose status is directly connected to the evidence supporting it. A fulfilled demand can be distinguished from a partially fulfilled or unresolved one. The investor can move from the overall picture to the individual evidence and back again.
This creates an important methodological difference: the visualization does not tell the investor what to believe; it shows the investor why a particular conclusion might be reasonable.
AI as Analyst, Not as Final Judge
AI is exceptionally useful for preparing such an environment. It can extract information from reports, identify relationships, calculate ratios, compare reporting periods and organize large amounts of evidence. But an AI-generated conclusion remains an interpretation. The Data2Space approach therefore places an additional layer between raw information and investment decision-making: human-readable spatial evidence.
AI might identify that a company has a weak governance structure. Data2Space can make the underlying relationships visible: ownership concentration, board composition, potential dependencies, compensation structures and audit arrangements can be positioned within the same corporate structure.
In the Smartbroker analysis, for example, the founder holds 59% of the shares, while the chair of the supervisory board is employed by the founder’s family office. The 2025 review concludes that the governance issue remained unresolved. An AI system can summarize this relationship. A spatial representation allows the investor to inspect it. That distinction matters because investment decisions are ultimately judgments under uncertainty. The investor should be able to challenge an interpretation, change a weighting, investigate an underlying figure or reach a different conclusion.
Spatial Visualization as a Cognitive Check
The deeper purpose of 3D visualization is therefore not simply faster information consumption. It is a form of cognitive control. When information is presented only as an AI-generated narrative, the investor is exposed to the AI’s selection and framing of the evidence. When the underlying relationships are spatially accessible, the investor can independently inspect the evidence and construct an alternative interpretation.
This is particularly important for investment criteria that cannot be reduced to a single number. “Good management”, for example, is not simply a KPI. “Margin of safety” is not merely a formula. “Economic moat” is not a database field. These are judgments derived from multiple observations. Data2Space can bring these observations together in a spatial context, allowing the investor to move between the individual facts and the higher-level investment thesis.
From Reading Reports to Exploring Investment Cases
Traditional financial analysis is predominantly linear: page after page, table after table, note after note.
Data2Space changes the interaction model. The investor can enter a spatial representation of the company, examine its financial structure, move through ownership and governance relationships, compare periods, inspect individual figures and evaluate predefined investment criteria. This turns the annual report from a document that has to be read into an environment that can be explored.
The approach is particularly consistent with the principles used in the Smartbroker analysis: understandable business model, competitive advantage, earnings quality, balance-sheet strength, management quality, capital allocation and valuation.
The investor does not have to accept a final “buy” or “sell” verdict generated by an AI. Instead, the investor can ask: What do I actually see? Which evidence supports the thesis? Which observations contradict it? Which demands have been met? And what remains uncertain?
The Investor Remains Responsible
This is the central idea behind the Data2Space method. AI can accelerate analysis. Spatial computing can accelerate understanding. But neither should eliminate human judgment.
The ideal workflow is therefore not:
Data → AI → Investment Decision
but:
Data → AI-assisted analysis → spatial visualization → human inspection → human judgment
The role of AI is to help structure complexity. The role of Data2Space is to make that structure intuitively accessible. The role of the investor is to decide what the evidence means. That creates a different philosophy of AI-assisted investing: AI does not become the investor. It becomes a powerful analytical instrument through which the investor can see more clearly.
The ultimate objective is therefore not an AI-generated opinion, but a better-founded human opinion — one that is faster to reach, easier to challenge and more directly connected to the underlying evidence.