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.

Pilot firstvalidate before scaling
End to endfrom audit to support
In Ukrainelocal team and expertise

One data ecosystem

Your environment
01 / COLLECTData sources

CRM, ERP, documents, registries, sensors

02 / FOUNDATIONData warehouse

Cleaning, linking and quality control

03 / INTELLIGENCEAI agents

Search, analytics and automation

04 / ACTIONSolutions

Dashboards, services and operational decisions

SECURE · SCALABLE · MEASURABLE

We work with your infrastructure

Cloud platformsOn-premiseHybrid environmentsOpen standards

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.

What does the tool check?

Exact entries from the official State Service list: product names, manufacturers and listing details.

Is the search private?

Yes. Search terms stay in the browser and are not sent to DATAMEN or the government source.

What should you do after a match?

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.

01

AI-ready data

We audit data sources, clean, structure and connect the data, then establish a governed quality and access framework.

Data qualityETL / ELTData catalogs
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 →
01

Small and medium business

A fast start without excessive infrastructure: AI assistants, document automation, sales and finance analytics.

Less manual work
02

Enterprise

Corporate data platforms, systems integration, predictive models and a governed AI environment.

One source of truth
03

Communities and regions

Solutions for resources, citizen requests, recovery programmes, reporting and service quality.

Better public services
04

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.

01

Assessment

We define the challenge, available data, risks and success criteria.

02

Architecture

We design the solution, integrations, access model and implementation plan.

03

Pilot

We launch a working scenario on real data and measure its impact.

04

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 →
01

Privacy by design

Roles, least-privilege access, audit trails and deployment in an approved environment.

02

Human oversight

Critical actions have clear verification and approval rules.

03

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.

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.

Describe your starting point

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.

Complete the brief +38 067 352 17 75067 352 17 75Email usFirst conversation: challenge, data status and a realistic next step