Companies Modernizing Legacy Systems with AI in Poland (2027): How to Choose a Partner and When to Rebuild from Scratch

Most mid-sized and large companies in Poland run on systems built 10, 15 or 20 years ago: ERP, warehouse systems, sales platforms, applications written for a specific process. They work, but every change is expensive, documentation is incomplete, and the people who built them have left. There’s a new question on top of that: how do you add AI to such a system if it doesn’t even have a proper API?

This article clarifies three things: when to modernize and when to rebuild; how AI changes the modernization process; and which companies in Poland have documented experience working with legacy systems and AI.

Key takeaways

• Modernization doesn’t have to mean a rewrite. An AI layer on top of the existing system (API, agent, assistant) often delivers impact faster and cheaper than rebuilding from scratch.
• The “modernize or rebuild” decision depends on four factors: the uniqueness of your business logic, the state of your tech stack, the time horizon, and total cost of ownership (TCO) over 3–5 years.
• AI changes the modernization process: language models read old code, generate documentation and tests, and support refactoring. Engineers still make the calls.
• Taking over a system from another vendor is a distinct skill set. Ask partners about these projects, not just greenfield builds.
• The hidden costs of legacy aren’t licenses, but features you can’t ship, people you can’t hire, and vulnerabilities with no patches.

What legacy modernization with AI means

Legacy modernization is the transformation of existing software (often a monolith in older versions of Java, .NET, PHP, sometimes COBOL) into a solution that can be developed, maintained, and integrated. “With AI” means two different things worth separating:

• AI as a modernization tool: language models analyze code dependencies, generate functional descriptions and documentation, suggest refactors, write regression tests, and translate code across languages and frameworks.
• AI as a new system layer: an assistant or agent that uses the old system’s data and functions via an API, adds natural-language interaction, automates processes, and predicts. The system core stays.

Most companies need both, but in different proportions. A company with a working ERP that lacks reporting and query handling mostly needs the second. A company with a system nobody can change anymore needs the first one first.

Modernize or rebuild

In practice, the most common answer is “both, in sequence”: first add an API and an AI layer on top of the existing system (fast impact, measurable results), then replace modules using the strangler fig pattern—gradually substituting parts of the old system with new ones until the old is no longer needed.

Migration models

Several “R” models exist in the literature (Gartner: 5R, AWS: 6R/7R). In short:

• Rehost: move without changes (e.g., to the cloud).
• Replatform: small changes for a new platform.
• Refactor / Rearchitect: rebuild code or architecture while preserving logic.
• Replace: swap with an off-the-shelf product.
• Retire: decommission if unused.

AI adds two variants that are increasingly the first step:

• Augment: keep the core, add an AI layer on top (assistant, agent, natural-language interface).
• Re-engineer with an LLM: a language model reads the intent of old code and supports rewriting it to modern standards with tests, under engineer supervision.

How AI changes the modernization process

Reverse engineering with LLMs

The biggest legacy problem is missing documentation. Language models can read code and generate a functional description, dependency maps, flow diagrams, and a draft spec. This doesn’t replace analysis, but it shortens onboarding for a new team and helps decide what to keep and what to cut before anyone changes a single line.

AI-assisted refactoring

AI identifies recurring code issues and suggests modern equivalents, translating fragments across language and framework versions. The process isn’t automatic: every change goes through an engineer and tests, because in legacy systems a change in one place breaks something elsewhere.

Regression tests

This is where AI shines: generating test scenarios from existing code, including edge cases nobody documented. Test coverage before modernization is a prerequisite to changing the system without disrupting operations.

An AI layer on top of the system

Once the system is stable and has an API, you can add an assistant or agent: answer questions about system data, perform operations in natural language, automate processes across systems. Permissions (AI only sees what the user sees), logging, and the ability to swap the model without a rebuild all matter here.

Overview: companies modernizing legacy systems with AI in Poland

Order is not a quality ranking. Companies are grouped by partner type. The list includes Poland-based companies with a public offering or case studies in modernization and AI. We describe Startup House using the same scheme as the others.

1. Startup House (Warsaw)

A software house building software since 2016 for clients in Poland, the US and Europe, specializing in recent years in AI agents, assistants, and LLM integrations with existing systems. For us, legacy modernization comes in two forms: taking over a system from another vendor and developing it, and adding an AI layer to a running system without rewriting it.

• Case: Omnipack (e-commerce logistics, Poland). Took over the OmniSoft system (fulfillment, shipping, returns) and modernized it with a dedicated team (2 backend, 1 frontend, 1 QA). Result: 40% lower development costs. Tech: Java, Spring, Kafka, React. Engagement since 2023.
• Case: cybersecurity platform (USA, Fortune 500 customers). Data from a legacy system rebuilt into an AI orchestration layer: new AI agents spun up in minutes without engineer effort. Result: +150% revenue in a year, 400% growth in B2B customers. Tech: Anthropic models, LangGraph, MCP, OAuth 2.1, Google Cloud, integration with CrowdStrike Falcon. Engagement since 2020.
• Product: AI Interface Layer. A natural-language interaction layer on top of an existing platform, without rewriting its core.
• How we work: discovery workshops (what in the system drives cost, what can change without stopping operations), code takeover and regression tests, then incremental changes. AI solutions run on verified client data, in the client’s cloud, integrated with access policies; client data does not train public models. ISO 27001 compliant.
• Technology partners: Anthropic, OpenAI, Google Cloud, Microsoft Azure, ElevenLabs, Okta.
• Who it’s for: companies that want to add AI to a running system or take over and stabilize a system from a previous vendor without disrupting the business.

2. Software Mind

A company with 1 600+ specialists, offering dedicated modernization (Software Mind Shift), cloud migrations (including a case migrating a custom system to Google Cloud), AI/ML, generative AI, and data engineering. Industries: finance, telecom, life sciences, media, manufacturing, parcel delivery.

• Who it’s for: enterprises with heavy technical debt in critical systems that need a large team for a multi-year program.

3. Sii Polska (Warsaw, offices in many cities)

The largest IT services provider in Poland (Computerworld TOP200), combining system modernization and maintenance with an AI team (350+ specialists, offering everything from agentic AI to AI managed services).

• Who it’s for: enterprises that want to combine modernization, maintenance, and AI in a single contract with a major provider.

4. Netguru (Poznań)

A Poznań-based software house (800+ people) that, when modernizing digital products, emphasizes a UX overhaul and adding agents and RAG; ISO 27001.

• Who it’s for: companies modernizing a customer-facing digital product and looking to revamp UX at the same time.

5. STX Next (Poznań)

A Python software house; in the legacy context, strongest in rebuilding backends and data systems and in migrations with heavy data processing.

• Who it’s for: backend and data system rebuilds, migrations with large-scale data processing.

6. Ideo (Warsaw, Rzeszów)

A family-owned company (featured in Forbes’ family business ranking) offering process automation: LOGITO document workflow platform, Ideo Flow with AI agents for process coordination, e-commerce and B2B platforms, custom software, IT outsourcing. Clients include Lotto, DPD, Empik, Wolters Kluwer, Kronospan.

• Who it’s for: companies modernizing internal processes and document workflows, not just code.

7. Comarch (Kraków)

A software vendor; for companies whose own system isn’t worth modernizing, offers the “replace” option: a ready-made ERP with built-in AI agents and ChatERP.

• Who it’s for: companies choosing “replace” instead of modernizing their own code.

8. TTMS (Warsaw)

A platform integrator (Salesforce, Microsoft, Adobe); “replatform” variant: move processes from old custom systems to standard platforms with AI agents; ISO/IEC 42001.

• Who it’s for: companies moving processes from old custom systems to standard platforms.

Hidden legacy costs: where the ROI is

Legacy systems look cheap on paper because licenses are paid off and one person handles maintenance. The real costs are elsewhere:

• Features you can’t ship. Every integration (new sales channel, partner, AI) needs workarounds or is impossible.
• People. It’s hard to hire developers for unsupported technologies, and system knowledge lives in one or two heads.
• Security. For end-of-support components, vendors don’t issue patches; known vulnerabilities stay open.
• Data. The data is in the system, but you can’t use it for analytics or AI without manual exports.

ROI from modernization is not just project cost versus maintenance cost, but the value of what becomes possible after: process automation, customer service via an assistant, reporting without manual work. In the Omnipack project, the measurable effect was 40% lower development costs; in the cybersecurity platform project, new revenue lines thanks to AI agents launched without engineer effort.

How to choose a modernization partner with AI

• Experience taking over code. Ask about projects where the partner took over a system from another vendor, and what the first weeks looked like (audit, tests, documentation).
• Case studies with numbers. Development cost, handling time, number of processes automated. A description without numbers isn’t enough.
• Approach to AI in legacy. Does the partner propose an overlay first, or an immediate rewrite? The latter without analysis is a red flag.
• Work without disrupting operations. A plan for incremental changes, regression tests, parallel environments.
• Data security during the process. Where code and data go during LLM analysis; whether models run under business terms; who has access.
• Ownership and documentation. Code, tests, and documentation belong to the company after the project; the system can be handed over to the next team.
• Model interchangeability. The AI layer’s architecture allows changing the model provider without a rebuild.

Which path fits your company

• The system works but lacks AI and integrations: add an API and AI layer on top, then make incremental changes. An AI software house (Startup House) or a product team (Netguru, STX Next).
• The system is critical, large, and on an unsupported tech stack: a multi-year modernization program with a large provider (Software Mind, Sii).
• The core problem is processes and document workflow, not code: process automation (Ideo).
• Your own system isn’t worth maintaining: replace with a ready-made ERP (Comarch) or move processes to a platform (TTMS).

See also: Top 10 companies implementing AI in Poland · How to choose an AI implementation partner: 20-question checklist · AI Interface Layer – product page · Case study: Omnipack

FAQ

Is analyzing old code with AI safe for company data?

Yes—if models run under business terms (data doesn’t train models), in the customer’s cloud or a controlled environment, and access to code and data is limited to the project team. Make this one of your first questions to a partner.

Do we have to implement AI right away?

No. The first step is a system you can add AI to: organized data, APIs, tests. Add the AI layer later, often to a single process first to measure impact.

Which is cheaper: modernization or rebuilding from scratch?

In the short term, incremental modernization, because changes are spread out and the old system keeps running. A rebuild pays off when the stack is unsupported, the logic is standard, or the cost of maintaining the old system exceeds the cost of a new one over a 3–5 year horizon. Base the decision on a calculated TCO, not gut feel.

How does AI help document old systems?

Language models analyze code and generate a functional description, dependency maps, and flow diagrams, which an engineer verifies. This lowers the onboarding bar for a new team and helps decide what in the system is still used.

What are the most common modernization mistakes?

Refreshing the UI without touching the logic, rewriting everything at once without regression tests, skipping analysis of which features are actually used, and deploying AI on uncleaned data.

Can you modernize a system without disrupting business operations?

Yes—and that’s the standard: incremental changes, parallel environments, regression tests, and gradual module cutovers. That’s how the Omnipack takeover and modernization were executed.

Compiled by the Startup House team based on publicly available information (as of October 2026). If you represent a company on the list and want to update your description, contact us.