Future-proof data strategy

Your data is worth more
than you think.
Take back control.

Today your data assets are buried deep inside IT: in systems, in employees' heads, in report request queues. We put them in the hands of the business. On an intelligent data platform, data grounds decisions, feeds AI and opens the way to retiring legacy systems.

Request a data asset auditSee how we work
15 yearsenterprise experience
100+data products
3+multinational rollouts

The macro context

Why now, and why waiting is not worth it.

Three parallel pressures are hitting Hungary's institutional and enterprise sector at the same time. All three point to the same place: managing data assets is no longer an IT question but a board-level decision.

Pressure 01

AI competitive pressure that will not wait

AI adoption is under way in every sector. Financial institutions automate fraud detection, the public sector builds decision support, industry rolls out predictive maintenance. And the first agentic pilots have appeared everywhere: digital coworkers that do not just answer, they run processes.

Whoever starts the AI project before solving the data problem gets stuck. The technology is not the issue. The foundation is missing.

“The AI race is won by whoever gets their data in order first. Not by whoever has the most expensive model.”

Pressure 02

Regulation and compliance pressure

The financial sector faces DORA, the Basel IV implementation and the Hungarian central bank's (MNB) tightening data reporting expectations all at once. In the public sector, EU interoperability frameworks create obligations. In industry, ESG and supply chain transparency effectively turn data management into regulation.

“Is your data reliable? Can you trace it back? Is every step auditable?”

Pressure 03

The peak of legacy system replacement

On the Hungarian market, ERP systems are heading into a generational change. Support for early SAP systems is coming to an end. Over a decade, core systems have accumulated customizations that no one fully documents anymore.

If you build the platform first, the switch is not a leap into the deep but a gradual crossing. And along the way it turns out you need to buy less software than you thought.

“Both paths start with a data strategy, and the replacement path leads through the data platform.”

Not our claim: the numbers come from independent analyst firms

60%

The share of AI projects that will be abandoned by 2026 because there is no prepared data behind them.

Gartner, 2025
63%

The share of organizations where AI-supporting data management practices are missing or uncertain.

Gartner, 2024
80%

The share of companies naming data limitations as the barrier to broader AI adoption.

McKinsey, 2026
40%

The share of AI agent projects that will be cancelled by the end of 2027, typically due to unclear business value and inadequate controls.

Gartner, 2025

They all point the same way: the technology is rarely the problem, while the meaning and stewardship of data very often is. MIT researchers described the same thing from the other side: deployments typically stall not on model capability but on learning and workflow gaps (MIT Project NANDA, 2025).

The three entry points

No matter where you start, you arrive at the same place.

The journey starts from three different pains, and all three lead to the same destination. Pick the situation closest to yours.

The intelligent data platform

Data and logic move to the center.

For decades the application was the center and data was its by-product. The intelligent data platform inverts that relationship, and becomes the layer that makes every other system replaceable.

The old world

The application is the center

Data lives inside the ERP, logic hides in its depths. Every new need means another integration, another license, another dependency. Data is a by-product.

The inversion

Data and logic are the center

Explicit, documented, auditable, owned by the organization. Applications become replaceable consumers: the old ERP, the new module, the BI tool, the AI model and the digital coworker all work from a single source of truth.

01

The nervous system

Real-time data flow

Every system emits events instead of copying data. Every transaction is available within milliseconds, with no point-to-point integration and no batch jobs.

02

The intellect

Processing and business logic

The data stream gains meaning: aggregation, pattern recognition, alerting. The rules that used to hide deep inside legacy systems become visible and verifiable here. This is where data meaning is born.

03

The memory

Unified, open data assets

The organization's complete memory, in an open format, queryable back to any moment. A single source of truth: your data stays yours, now and ten years from now.

04

The immune system

Governance, quality, audit

Who can see what, precisely defined. Every access logged and traceable. Multi-tenant isolation, on-premise and private cloud compatibility.

Data meaning: the company's shared language

Meaning travels with the data.

Every important business concept comes with a definition, an owner and a quality standard: in machine-readable form, inside the running systems, not in documents nobody opens. The industry term for this is enterprise ontology; we call it data meaning, because that is exactly what it provides: the meaning of your data.

Reporting, BI and AI all connect here. If two screens ever disagree, the shared concept layer is the authoritative source. And the key stays with the business: IT stores and moves the data, but the business owns its meaning.

Meaning, recorded

The same concept and metric definitions apply in the glossary, in reports and in AI applications. Meaning no longer lives in one colleague's head.

Agreements, enforced

The agreement between the party supplying the data and the party receiving it exists in machine-readable form: what it contains, at what quality, by when. The system checks it, not a person.

Oversight, provable

You can trace where data came from, who has access to it, and which AI solution uses it. Demonstrable in audits and regulatory reviews alike.

Under the hood, mature, open-source, enterprise-grade components do the work: the same building blocks the world's leading banks and tech companies build on.

The only thing that is not free here: knowing how to put it together right.

See item by item what we work on: more than sixty individual capabilities

AI capabilities

The platform is not the goal. What you build on it is what counts.

The capabilities built on the platform are not a distant roadmap. They are rungs of a ladder your organization climbs at its own pace. And every rung delivers value immediately.

Step 1

Real-time decision layer

Dashboards that show the live reality instead of yesterday's export. Alerts that fire while intervening still makes sense. The Monday meeting debate ends: there is one number, and it is the same for everyone.

Step 2

Predictive capabilities

Historical depth and real-time freshness together make it possible: fraud detection on behavioral patterns, predictive maintenance, demand forecasting, liquidity forecasting. You stop explaining problems after the fact and start seeing them coming.

Step 3

Language intelligence

LLM-based assistants that work on your organization's own governed data assets, not on the noise of the internet. An executive asks in plain language, and the answer comes from the actual numbers, with sources cited.

Step 4

Autonomous agents and multi-agent processes

Digital coworkers that do more than answer: they run processes. They reconcile orders, assemble reports, investigate anomalies, on the platform's data, under the platform's explicit rules, with a full audit trail.

And this is where the two threads meet: what replaces a retired legacy system is not always software. Your next “ERP system” may well be a team of digital coworkers.

What AI demands, and what the platform delivers

An AI system, whether a predictive model, a language model or an autonomous agent, imposes four data requirements at once. That is why AI cannot be put ahead of the data. And that is why nothing has to be rebuilt when the next model generation arrives.

01

Freshness

AI works on current reality, not on yesterday's snapshot

That is what the real-time streaming layer is for.

02

Depth

historical context: patterns, trends and anomalies only show up over time

That is what the data asset layer's time travel is for.

03

Reliability

AI cannot doubt: if the data is wrong, it returns the error in a confident voice

That is what the governance layer is for.

04

Openness

AI tools evolve fast: a closed format turns every model switch into a migration project

That is what the open data asset format is for.

Governed AI: AI governance

In an institutional setting, autonomy is only acceptable with oversight.

That is why we build the oversight layer right alongside the capability ladder: covering the expectations of NIST, ISO 42001 and the EU AI Act in a single system, in a form you can prove in audits and regulatory reviews alike.

01

AI inventory

A register of every AI solution running in the organization, including vendor tools and embedded features. With an owner, the data domains used and the business purpose.

02

Risk classification

Every AI solution classified along the logic of the EU AI Act, because the obligations follow from the classification, with high-risk systems clearly flagged.

03

Human approval

Recorded rules on where human approval is mandatory, what must be logged, and how model performance is measured, as live control points.

04

Provable operation

You can demonstrate after the fact which data, which model and what moment produced an AI decision. Agents never query the raw database; they work on an access-controlled, logged layer.

Data & Integration Product

Data is not infrastructure. It is a product.

In the data product mindset, every data product has an owner, an SLA, documentation, an internal customer and a data contract. Try it: pick data products and see what they build.

Data Product Explorer

Demo data

Data product catalog

Dashboard built from the selected products

Daily revenue

+6.4%

HUF 48.2M

Source: Sales transactions · SLA: real-time

Inventory turnover

-1.8 days

11.4 days

Source: Inventory and logistics · SLA: 15 min

Ask your data

HUF 46M

In two warehouses, slow-moving stock has been tying up HUF 46M for 38 days. That is 2.1 points of gross margin: without connecting the two data products, this number would show up nowhere.

Sources: Sales transactions · Inventory and logistics

Data contract

The agreement does not live on paper. The system enforces it.

Behind every data product stands a data contract: the agreement between the team supplying the data and the team consuming it, in machine-readable form. It is checked not by a person but by the ingestion process: on every run, automatically. When something deviates, the consumer does not discover the error in a report; the owner gets an alert.

What it containsa fixed structure and meaning: the consumer knows what to expect
At what qualitymeasurable rules: freshness, completeness, validity
By whena committed availability the consumer can plan on

The figures above are illustrative. In production, all of this runs on your own platform, from your own data products, under governance, with a full audit trail.

The timing

In multinational environments, the transition has already begun. Now it is becoming available to everyone. This is the first-mover window.

2005

The first real-time banking data platforms

Custom code, specialized teams, year-long projects. The technology existed, but it was not affordable.

2020

The maturity of the open source ecosystem

Data technologies reach enterprise production grade, on open source. The license fee is zero.

2026

The moment of general availability

The full platform can be assembled from open components. Move now and you gain a 5-year head start. Wait, and you fall behind.

References

Where we have already delivered: in live environments, with real stakes.

MBH Bank (formerly Magyar Bankholding)

Strategy

Real-time data platform strategy

For Hungary's second-largest bank, we created a real-time data platform strategy: we designed a unified, reliable data layer on top of the heterogeneous systems inherited from the bank merger, without replacing the source systems.

Erste Bank Hungary Zrt.

In production

Banking system development and data integration

We have been building and integrating core banking systems for years: in a strictly regulated environment, in live day-to-day banking operations.

AdvisoryK&HRaiffeisenCIBVilati Zrt.

Reference materials on request, under NDA: write to us.

The methodology

How we work: what you get and when.

Three phases. Each comes with a measurable acceptance criterion fixed up front, and until a phase clears its own gate, we do not move on.

Liberation

Freeing data from closed systems

What we do

A data asset audit: we map where data originates, who is responsible for it and where it breaks. We also uncover the hidden business logic: the triggers, stored procedures and batch jobs where operational knowledge hides. We put a price on what until now you could only feel: what data chaos costs you every day.

What you get

  • Data asset map: source systems, data flows, responsibilities
  • Business logic inventory: where operational knowledge lives and how much it is at risk
  • Business impact analysis: the quantified cost of unmanaged data
  • Prioritized roadmap and architecture proposal: the target state
Acceptance gate

The cost of data chaos quantified, and the first step of the roadmap backed by a business case, in a form ready for a board decision.

Order

Governance, quality, a single source of truth

What we do

Building the intelligent data platform, from the real-time streaming layer to the open data asset layer. Lifting business logic out of the depths of legacy systems into explicit, versioned, auditable platform rules. And in parallel, the organizational side: appointing data owners and running joint business glossary workshops with the business teams, because we do not invent the definitions, we uncover them with you.

What you get

  • A working platform foundation: your data starts moving, in real time
  • A single source of truth: in open formats, owned by you
  • Explicit business logic: the hidden rules captured in the platform, documented
  • A governance framework and data product catalog: with SLAs and an audit trail
  • Clear accountability: appointed, trained data owners and a shared business glossary
Acceptance gate

The platform answers 10–20 of your real business questions on real data, evaluated together, not demoed on sample data.

Value creation

Putting data to work

What we do

On the newly ordered data layer we create business value, moving up the capability ladder: real-time dashboards, predictive analytics, LLM-based assistants and, when you are ready, the digital coworkers of Agentversum. In parallel, if you so decide, the scheduled phase-out of legacy systems, module by module. This is also where knowledge transfer happens: role-specific training on live tasks, playbooks, and preparing internal trainers.

What you get

  • A real-time decision layer: dashboards that are always current
  • Working AI use cases: from predictive models to autonomous agents
  • A legacy phase-out roadmap: which system can be replaced, when and with what
  • Measurable ROI: reducing the cost quantified in the audit
  • Knowledge transfer: a trained in-house team, playbooks, independent continuation
Acceptance gate

Every use case proves its impact against the baseline recorded in the first phase: not in status reports, but in numbers.

In every phase, at every step: the software is open source and stays yours. No vendor lock-in. No supplier dependency. What we bring: the knowledge, the experience and the guarantee that it works the first time.

Segments

Every sector enters through a different door, into the same room.

Hungarian state and public institutions

The next wave of government digitalization is data-driven governance: policy decisions grounded in real-time, reliable data.

The pain

Data across ministries and subordinate agencies lives in isolation. Decision preparation relies on manual data collection: slow, error-prone and impossible to audit.

What Dataversum offers

A multi-tenant intelligent data platform: every agency keeps its own data sovereignty on a shared, permission-controlled layer. On-premise and private cloud compatible. Full audit trail. An AI-ready foundation from decision support to automation.

Why us

Architecture delivered in live production environments, where a data handling error is not a business loss but an incident affecting citizens.

Frequently asked questions

Before you pick up the phone: the questions we hear most.

Still have a question? Get in touch

Start here

The first step is a number.

The audit shows what data chaos costs you per day, and pinpoints the one spot where you can gain the most with the least risk. That is the number that turns an IT project into a board decision.

We use your details solely to get in touch with you.

30-minute intro callNDANo commitment

A tangible start

Putting one data domain in order

We take one important data domain end to end: definition, owner, quality rules, data contract, catalog entry. Visible results within a few weeks, as a template for the rest.

Discuss a pilot

Banking reference

MBH Bank case study

A walkthrough of the bank's real-time data platform strategy material, on request, under NDA.

Request the reference material

Executive summary

The business cost of data chaos

A one-page executive summary you can explain to top management: why now and why it is a priority.

Request the summary