CRM, ERP, documents, registries, sensors
Data → intelligence → impact
AI agents and solutions
built on your data.
We build a reliable foundation of data, AI agents and warehouses — so businesses and public institutions can make better decisions faster.
One data ecosystem
Your environmentCleaning, linking and quality control
Search, analytics and automation
Dashboards, services and operational decisions
SECURE · SCALABLE · MEASURABLE
We work with your infrastructure
New practical tool
Is your software in Ukraine's official restricted list?
Search 2,020 official entries by product or manufacturer. The query runs locally in your browser, and every match points to a relevant migration path.
Exact entries from the official State Service list: product names, manufacturers and listing details.
Yes. Search terms stay in the browser and are not sent to DATAMEN or the government source.
Confirm the exact product, map dependencies and plan a controlled migration with data reconciliation.
What we do
From raw data
to a working AI solution
One accountable team combining data engineering, AI development and a practical understanding of operations.
AI-ready data
We audit data sources, clean, structure and connect the data, then establish a governed quality and access framework.
Learn more ↗AI agents for operations
We build secure assistants that work with your documents, knowledge bases and business systems.
Learn more ↗Warehouses and analytics
We design data warehouses, lakehouses and BI solutions that create one reliable source of truth.
Learn more ↗End-to-end AI solutions
From hypothesis and prototype to integration, team training, monitoring and continuous improvement.
Learn more ↗Legacy and restricted software migration
We move data from 1C, BAS, Bitrix24 and unsupported systems with reconciliation, history preservation and no target-vendor lock-in.
Learn more ↗Who we work with
Solutions aligned with the scale of your responsibility
From a focused business process to the digital infrastructure of an entire region.
Explore industry use cases →Small and medium business
A fast start without excessive infrastructure: AI assistants, document automation, sales and finance analytics.
Less manual work ↗Enterprise
Corporate data platforms, systems integration, predictive models and a governed AI environment.
One source of truth ↗Communities and regions
Solutions for resources, citizen requests, recovery programmes, reporting and service quality.
Better public services ↗Government institutions
Secure work with registries, documents and analytics, including roles, audit trails and regulatory requirements.
Transparent operations ↗How we start
A controlled path
from idea to scale
We begin with value, prove it through a pilot and only then scale the solution.
Assessment
We define the challenge, available data, risks and success criteria.
Architecture
We design the solution, integrations, access model and implementation plan.
Pilot
We launch a working scenario on real data and measure its impact.
Scale
We integrate, train the team and continuously improve the solution.
Responsible AI
Your data remains yours. So does control.
We build around security, transparency and measurable value — not around whichever technology is trending.
Read our methodology →Privacy by design
Roles, least-privilege access, audit trails and deployment in an approved environment.
Human oversight
Critical actions have clear verification and approval rules.
No technology lock-in
Modular architecture, documented integrations and knowledge transfer to your team.
Choose the right first step
Start with the decision you need to make
Each path produces a concrete next-step artefact rather than a generic technology presentation.
Understand readiness
Use a private five-question assessment to identify whether data, access or governance should come first.
Start the assessment →02Frame an AI pilot
Turn one business process into a bounded pilot with agreed data, controls and success criteria.
Plan a discovery →03Reduce technology risk
Inventory unsupported or restricted software, map dependencies and define controlled replacement waves.
Explore an EOL audit →Clear answers before technology
Questions leaders ask before starting
Short answers for choosing a responsible first step with data and AI.
Where should an organisation start an AI project?
Start with one business process or management decision. Define the user, required sources, data owner, verification method and boundaries of human oversight before selecting a model or platform.
Must data be moved to an external cloud?
Not necessarily. The environment can be cloud, on-premise or hybrid. The choice depends on data categories, security requirements, available integrations, performance and organisational rules.
Can an AI agent work with documents and databases?
Yes, when permitted sources, roles, information freshness and agent actions are defined. Critical answers require source references, logging, test queries and human approval.
How is an AI pilot evaluated?
Before launch, agree a test set, quality measures, a process metric, unacceptable errors and decision criteria. After the pilot, record observed facts, assumptions, risks and conditions for scaling separately.
Are these solutions suitable for communities and government institutions?
Yes, for document search, request classification, analytical preparation and data-quality controls. AI should not replace statutory authority or the final administrative decision.
How does legacy migration prepare data for AI?
A controlled migration documents sources, cleans reference data, resolves duplicates, aligns access and records integrations. This creates a stronger foundation for analytics, enterprise search and AI agents.
The first step
You have the data.
Let's turn it into action.
In our first conversation, we will define the challenge, data readiness and a realistic pilot format.