Best enterprise AI companies in Central and Eastern Europe

Choosing a technology partner to implement artificial intelligence in the enterprise segment requires going beyond the standard software house criteria. In CEE (Central and Eastern Europe), there are players that combine global scale with deep engineering expertise, offering agentic systems and RAG knowledge bases aligned with the AI Act and DORA. This article presents a vetted list of companies with documented AI deployments for the corporate sector.

Key takeaways

• Startup House (Warsaw) cuts onboarding time on cybersecurity platforms by 95% thanks to intelligent assistants.
• N-iX reduced pull request (PR) cycle time from 12.4 h to 7.2 h using advanced Claude Code capabilities.
• EPAM automated processes for PostNL, shrinking tasks that took over a week to just 40 minutes using more than 20 agent types.
• WeAreFuture achieved a 50% lead time reduction at SMAY as an OpenAI Select Partner.
• SoftServe optimized industrial simulations for Krones, cutting time from 4 hours to under 30 minutes.

The best enterprise AI companies in Central and Eastern Europe are specialized in designing, implementing, and scaling solutions based on large language models (LLMs), machine learning, and multi-agent systems within complex corporate infrastructures.

Working with these organizations enables secure modernization of legacy systems and integration of company data with AI models without risking public model training on clients’ confidential information.

Company profiles with engineering teams in Central and Eastern Europe

The following list presents vetted players operating in the CEE region. The order does not constitute a quality ranking; the roundup is based on publicly available facts and documented enterprise project successes.

Startup House (Poland)

Startup House (Warsaw) is a software house that has been building software since 2016 for clients in Poland, the US, and Europe. They implement AI in the products and processes of mid-sized and large firms: AI agents and multi-agent systems, AI assistants, RAG knowledge bases, and integrations of language models with existing systems, including legacy, without rewriting them from scratch. 50-person team, 100+ delivered products, clients on 5 continents, NPS 75.

• Specialization: AI Agents (LangGraph, MCP), KnowHub (RAG), Legacy systems modernization.
• Industries: food manufacturers, fintech, healthcare, cybersecurity, travel, logistics.
• Example project: AI orchestration layer for a US cybersecurity platform (Fortune 500) — 150% revenue growth in one year.
• Ideal client: Mid-sized and large companies seeking a strategic partner to rapidly deliver secure AI solutions (MVP in as little as a week).

EPAM (Global centers in CEE)

EPAM is an engineering giant with a massive footprint in CEE, employing over 62,850 people in 55 countries. Their enterprise AI approach is built on proprietary accelerators such as the DIAL platform and AI.Run, enabling rapid deployment of models from multiple providers in a controlled environment.

• Specialization: Agentic QA, LLM model orchestration, AI.Run.
• Industries: High-tech, retail, financial services.
• Example project: PostNL — reduced operational tasks from over a week to 40 minutes using 20+ agent types.
• Ideal client: The largest global organizations requiring massive scale and standardized delivery processes.

SoftServe (Wrocław, Gliwice, Austin)

A company with Ukrainian roots, HQ in the US, and a strong presence in Poland. As an NVIDIA Elite Partner and Anthropic Select Partner, SoftServe focuses on advanced multimodal solutions and industrial applications of AI.

• Specialization: Agents4Everything, multimodal RAG, Agentic MVP Sprint.
• Industries: Industry 4.0, healthcare, energy.
• Example project: Krones — optimized simulations from 2–4 h to under 30 minutes.
• Ideal client: Manufacturing and tech companies needing deep AI integration with hardware or complex visual data.

N-iX (Centers in CEE, HQ Malta)

With a team of over 2,400 engineers, N‑iX is one of the fastest-growing players in the region. They have delivered 50+ AI projects and are part of the Claude Partner Network, which positions them strongly in Anthropic model deployments.

• Specialization: AI agent development, RAG systems, data analytics.
• Industries: Logistics, finance, retail.
• Example project: WorkWave — 80% workflow time reduction thanks to AI.
• Ideal client: Enterprises looking to reduce tech debt through agentic automation of engineering processes.

WeAreFuture (Warsaw)

A technology boutique that, since July 2026, has held OpenAI Select Partner status. They specialize in transforming business processes through direct access to the latest models and support from the creators of ChatGPT.

• Specialization: AI transformation, strategic GenAI advisory.
• Industries: Manufacturing, pharma, services.
• Example project: AbbVie — increased daily AI adoption across the organization from 12% to 90%.
• Ideal client: Companies needing close collaboration with the OpenAI ecosystem to drive a cultural shift in how work is done.

Sii Polska (Warsaw and 14 cities)

Poland’s largest IT integrator, repeatedly awarded in Computerworld TOP200 rankings. They have a team of over 350 AI specialists and a rich portfolio of partnerships with the largest cloud providers.

• Specialization: Scaling AI systems, integration with Azure/AWS/GCP, Databricks.
• Industries: Banking, insurance, public sector.
• Example project: 70+ AI deployments for corporate clients across Europe.
• Ideal client: Polish and international corporations seeking a stable partner for long-term outsourcing contracts.

Selection criteria for an enterprise AI partner

The process of choosing a company to deliver an AI project must be structured. In the enterprise segment, an architectural mistake can cost hundreds of thousands of euros through inefficient token spend or data leaks.

Below is a standard vendor due diligence process:

• Security verification (Governance): Does the vendor guarantee that client data is not used to train public models? Is the architecture mapped to requirements such as DORA, HIPAA, or SOC 2?
• Experience with legacy systems: Most enterprise projects don’t happen in a vacuum. The ability to integrate an AI assistant with systems like SAP, Comarch ERP, or databases without rewriting them is crucial.
• Cost transparency: A professional partner should offer cost control per agent (e.g., via Langfuse) and real-time monitoring of resource usage.
• Approach to hallucinations: Avoid firms promising “zero hallucinations.” Look for partners who implement RAG, where AI answers strictly based on company data with source attribution.
• Location and legal compliance: In CEE, understanding the AI Act and GDPR is key. Regional firms such as Startup House or Sii operate under the same legal frameworks as their EU clients.

Startup House products and case studies

At Startup House, we not only advise—we deliver ready-made components that accelerate implementation. Our solutions are designed for enterprise-grade security and can run in the client’s cloud.

Selected AI products

• KnowHub: An AI assistant powered by a RAG knowledge base that unifies distributed company documents and answers based on them 24/7. Integration takes around 2 weeks, and access is role-based.
• AI Interface Layer: A conversational AI layer on top of an existing platform, allowing users to query data from legacy systems in natural language without rebuilding them.
• AI Demand Planner: A solution for food manufacturers integrating with SAP and Comarch ERP to forecast demand and validate against historical data.

Case Study: Client portal with embedded AI for a cybersecurity platform

For our US client serving Fortune 500 companies, we designed and implemented a portal with a built-in AI assistant. The goal was to eliminate onboarding bottlenecks and improve retention.

Implementation details: We used the Claude model and the LangGraph library to orchestrate conversation flows. We leveraged the Model Context Protocol (MCP) for secure data access and OAuth 2.1 for authorization.

Measurable results:

• Reduced onboarding time by 95% (from 45 minutes to under 2 minutes).
• Increased user frequency from quarterly to daily usage.
• Created a single, unified dashboard for three key roles: the board, CFO, and CISO.

AI deployment risks in large organizations

Even the best enterprise AI companies in Central and Eastern Europe must contend with challenges that can slow projects or threaten their security:

• Unauthorized data access: AI models can “leak” information if the RAG layer doesn’t respect user permissions defined in Active Directory or other IAM systems.
• Uncontrolled token costs: Complex agent loops without limit monitoring can generate high bills from model providers (OpenAI, Anthropic).
• Lack of audit logs: Under the AI Act, high-risk systems must maintain complete logs of AI decision-making processes, which is often overlooked at the PoC stage.
• Vendor lock-in: Building a solution 100% on one cloud provider’s proprietary features without an abstraction layer can hinder later migration.

Which company is right for your project?

The decision depends on scale, budget, and technical specifics. If your organization needs global support in 50 countries and hundreds of specialists on a single project, EPAM or SoftServe are natural choices given their vast resources.

If speed-to-market, direct collaboration with solution architects, and modernizing legacy systems without corporate inertia are your priorities, Startup House offers a more agile, founder-to-founder approach.

For companies in Poland seeking local support with significant staffing capacity, Sii Polska remains a solid foundation. Meanwhile, boutiques like deepsense.ai are indispensable for projects requiring unique expertise in Computer Vision or deep learning beyond standard LLMs.

Frequently asked questions (FAQ)

Does Startup House guarantee data security in AI solutions?

Startup House designs architecture so that client data is not used to train public AI models. All processes comply with ISO 27001, and access to information is role-based (RBAC) with full audit logs, supporting compliance with the AI Act and GDPR. Solutions can run in the client’s cloud or a dedicated environment.

What AI experience does N-iX have?

N‑iX has delivered over 50 artificial intelligence and machine learning projects and is part of the Claude Partner Network (Select). Their engineers specialize in building AI agents, confirmed by a deployment for WorkWave where pull request (PR) cycle time was reduced from 12.4 h to 7.2 h. The company has delivery centers in 10 countries, including in CEE.

What does WeAreFuture specialize in for enterprise?

WeAreFuture is an OpenAI Select Partner, giving them unique access to the vendor’s latest tools and models. They focus on transforming business processes, exemplified by a rollout at SMAY (50% lead time reduction) and at AbbVie, where they significantly increased employee adoption of AI tools. They operate primarily as a boutique strategic advisor.

What benefits does working with EPAM bring for large AI projects?

EPAM offers global scale (over 62,000 employees) and proprietary platforms such as DIAL and AI.Run, which streamline orchestration of multiple AI models in enterprises. Their effectiveness is confirmed by the PostNL project, where a system of over 20 AI agents cut week-long processes to just 40 minutes. They are partners of Microsoft, AWS, and Google Cloud.

Do Polish AI companies have experience in the banking sector?

Yes. For example, 10Clouds delivered a PoC for a commercial bank in Poland, accelerating application analysis by 79% (from 90 to 19 minutes). Startup House also works with the financial sector—for instance, SimplyLease for Siemens Financial Services, where an automatic leasing decision is made within 7 minutes and the process is available 24/7.

Last updated: October 26, 2026.

We invite the companies listed to send data updates to the editorial team.