Europe’s LLM space feels a bit different from what you see in the US. There’s less noise, fewer bold claims, and more focus on how things actually work once they’re in production. A lot of teams here are building language models with specific use cases in mind – enterprise tools, multilingual systems, regulated environments – rather than chasing general-purpose hype.
This list looks at companies that are actively working with large language models across Europe. Some are building their own models, others are integrating and adapting them, but the common thread is pretty simple: they’re trying to make LLMs useful in real business settings, not just impressive in demos.
1. Oski Solutions
Oski Solutions works with companies that are trying to move from scattered tools and legacy systems to something more structured and scalable. Their focus sits somewhere between custom software development and practical AI adoption, including large language model integrations. In Europe, they tend to work with mid-size businesses and growing teams that already have some digital infrastructure in place but need to rethink how it all connects. A typical case might involve taking an internal system that barely talks to anything else and turning it into something that can actually support day-to-day operations without constant workarounds.
Their approach to LLMs is not about building standalone models from scratch, but about embedding them into real workflows. That could mean automating support tasks, improving internal search, or adding language-based features to existing platforms. Alongside that, Oski Solutions handle full-cycle development, from backend systems in .NET or Node.js to frontend interfaces in React or Vue. They also take on team augmentation and outsourcing projects, which makes sense for companies that need to scale development without hiring locally. Most of their work happens across Europe, with additional projects in North America and nearby markets.
Key Highlights:
- Provide LLM integration as part of broader software systems, not as isolated tools
- Work with companies in Europe, as well as North America and selected global markets
- Focus on business use cases like automation, internal tools, and data handling
- Experience with industries such as e-commerce, healthcare, fintech, and logistics
- Combine AI work with full-cycle development and system integration
- Often collaborate with teams that need long-term technical support rather than one-off delivery
Services:
- LLM integration into web and business applications
- Custom software development for web and enterprise systems
- Large language model integration and AI-driven features
- Backend development using .NET, Node.js, PHP, and C#
- Frontend development with React, Angular, and Vue.js
- Cloud and DevOps setup using Azure, AWS, Docker, and CI/CD
- CMS development with Umbraco and WordPress
Contact Information:
- Website: oski.site
- E-mail: [email protected]
- LinkedIn: www.linkedin.com/company/oski-solutions
- Address: Kaupmehe tn 7, 10114 Tallinn, Estonia
- Phone: +48571282759
2. InData Labs
InData Labs focuses on building and adapting large language models for business use, usually as part of broader data and AI systems. Their work tends to start with figuring out where an LLM actually makes sense – not every process needs one, and they seem to treat that as a practical constraint rather than something to work around. From there, they move into prototyping and integration, often using existing models like GPT or similar tools and shaping them around company-specific data.
What stands out is that InData Labs don’t treat LLMs as isolated tools. They connect them to data pipelines, cloud infrastructure, and existing applications, which is where most of the real work usually sits anyway. Their teams cover machine learning, data engineering, and NLP, so projects often involve a mix of model tuning, API integration, and backend work.
Key Highlights:
- Work with large language models as part of full data and AI systems
- Combine LLM development with data engineering and cloud setup
- Provide services in Europe and across international markets
- Focus on practical use cases like automation, analytics, and customer interaction
Services:
- LLM consulting and business case analysis
- Custom LLM development and integration
- Model fine-tuning using company-specific data
- NLP and machine learning development
- Data engineering and architecture setup
3. Aleph Alpha
Aleph Alpha takes a slightly different path compared to many LLM providers. Instead of building general-purpose tools and adapting them later, they focus on specialized language models designed for specific domains like legal, industrial, or administrative work. Their approach is tied closely to European infrastructure, with an emphasis on keeping data and processing within controlled environments.
What stands out is how Aleph Alpha works with organizations that already deal with complex, sensitive information. In practice, that includes government agencies or large industrial players where documents are messy, fragmented, and often hard to search through.
Key Highlights:
- Focus on specialized large language models tailored to specific domains
- Strong emphasis on data sovereignty and European infrastructure
- Works with public institutions, industrial companies, and regulated sectors
- Co-development approach where models are adjusted to internal workflows
Services:
- Domain-specific LLM development
- AI assistants for administrative and enterprise workflows
- Document analysis and knowledge retrieval systems
- Integration of LLMs into existing operational processes
4. Leobit
Leobit approaches large language models through what they call corporate LLMs, which are built around internal company data rather than general-purpose use. Their work often involves setting up systems that can handle internal documents, emails, or knowledge bases and turn that into something searchable or actionable.
They also spend time on architecture decisions, especially around how models are deployed – whether through APIs, customized services, or fully private setups. Leobit operates across Europe, with development centers in places like Poland and Estonia, and they combine this AI work with more traditional software development using technologies like .NET and Azure.
Key Highlights:
- Focus on corporate LLMs built around internal company data
- Work with both public and privately hosted models
- Provide services in Europe with multiple development centers
- Combine LLM systems with backend and cloud infrastructure
- Apply LLMs to internal workflows like HR, sales, and support
Services:
- Corporate LLM development and deployment
- LLM customization and fine-tuning
- AI agents for internal business processes
- Retrieval-augmented generation (RAG) systems
- Conversational AI and chatbots
5. 10Clouds
10Clouds works at the intersection of product development and applied AI, with a noticeable focus on fintech and digital platforms. Their LLM-related work is usually tied to improving existing products rather than building standalone AI tools. For example, instead of creating a separate chatbot product, they might integrate a language model into a banking app to handle customer queries or support internal decision-making.
Their teams combine machine learning, MLOps, and API integrations, often using tools like OpenAI or cloud-based services to keep things practical. A lot of their work revolves around automation – fraud detection, customer communication, or data analysis – especially in finance-related products.
Key Highlights:
- Work with LLMs as part of product development, especially in fintech
- Integrate external AI tools like OpenAI and cloud ML services
- Approach AI adoption with a focus on actual use cases, not assumptions
Services:
- LLM integration and generative AI implementation
- Machine learning development and consulting
- AI API integration and automation solutions
- MLOps and ML infrastructure setup
- Web and product development
6. Beetroot
Beetroot takes a slightly different angle on LLM development, leaning more into collaboration and long-term team setups rather than one-off delivery. Their LLM services are often tied to building dedicated teams or supporting internal engineering groups, which shows up in how they describe their work. That might mean helping a company organize messy datasets before even touching the model itself – something that tends to get overlooked but ends up taking most of the time.
They also seem comfortable working across very different domains, from life sciences to sustainability projects, where LLMs are used for things like document processing or research support. Beetroot includes consulting, development, fine-tuning, and even workshops, which suggests they often deal with teams that are still learning how to use these tools.
Key Highlights:
- Work with LLMs through dedicated teams and long-term collaboration
- Often involved in data preparation and structuring before model work
- Experience across healthcare, sustainability, fintech, and SaaS
- Provide both technical delivery and team training
Services:
- LLM strategy and consulting
- LLM application development
- Model fine-tuning and optimization
- Prompt engineering and workflow design
- Ongoing support and maintenance
7. Dev Centre House
Dev Centre House focuses on building custom LLM-based solutions for businesses that are either just starting with AI or trying to extend what they already have. Their work often centers around fairly clear use cases – chatbots, text analysis, and content-related automation – rather than more experimental applications.
They tend to offer end-to-end involvement, from development to deployment and ongoing support, which can be useful for companies that don’t have internal AI expertise. At the same time, their approach stays relatively straightforward, focusing on integrating LLMs into existing systems instead of redesigning everything from scratch.
Key Highlights:
- Focus on practical LLM use cases like chatbots and text processing
- Provide end-to-end development and support
- Work with companies at different stages of AI adoption
- Experience with real-time features like transcription and summarization
Services:
- Custom LLM development
- NLP solutions and text analysis
- AI chatbot development
- Enterprise AI integration
8. Azati
Azati works across custom software development and AI, with LLMs sitting as one part of a broader engineering setup. Their projects usually combine backend systems, data pipelines, and language models rather than treating LLMs as standalone tools. In practice, this often means building systems that process large volumes of text or voice data and turn it into something usable – like internal search, document analysis, or automated workflows.
They also put a lot of attention on how these systems are deployed and scaled. Azati works with both cloud and on-prem setups, depending on how sensitive the data is, which tends to matter in industries like healthcare or finance. Their teams cover everything from prompt design to infrastructure, so projects often move from early prototypes into full production systems without switching providers.
Key Highlights:
- Combine LLM development with full software engineering and data systems
- Work with text, voice, and structured data in one environment
- Experience with domain-specific model training and fine-tuning
- Support both cloud and on-premise deployments
Services:
- Custom LLM development and fine-tuning
- NLP and text analysis solutions
- LLM integration and API development
- AI-powered chatbots and assistants
- Speech-to-text and voice processing
- Prompt engineering and optimization
9. Square Root Solutions
Square Root Solutions focuses on building LLM systems that are meant to run inside enterprise environments, especially where data handling rules are strict. Their work leans toward structured implementations rather than experimentation, with clear steps from use-case definition to deployment. Instead of pushing one specific model, they work with a mix of open-source and commercial options, depending on what fits the project.
They also spend time on compliance and data ownership, which comes up often in industries like healthcare or legal services. Their projects usually include fine-tuning, API integration, and ongoing model management, so the systems don’t just sit idle after launch.
Key Highlights:
- Focus on enterprise LLM systems with structured implementation process
- Work with both open-source and proprietary models
- Experience with compliance-heavy industries like healthcare and legal
- Use retrieval-based architectures connected to internal data
Services:
- LLM consulting and use-case planning
- Custom LLM development
- Model fine-tuning and optimization
- LLM application development
10. Sapphire Software Solutions
Sapphire Software Solutions approaches LLMs mainly through integration work, where language models are added into existing business systems rather than built from scratch. Their projects often focus on improving how applications handle text, whether that’s through chat interfaces, document processing, or automated responses.
They also work across different types of applications, including enterprise software, mobile apps, and SaaS platforms, which shapes how their LLM work is applied. Instead of deep model customization, the focus is more on connecting APIs, setting up workflows, and making sure the system behaves consistently.
Key Highlights:
- Focus on LLM integration within existing systems
- Work across web, mobile, and enterprise platforms
- Use API-based models rather than building from scratch
Services:
- LLM integration and API setup
- Chatbot and conversational AI development
- Document processing and text automation
- AI feature integration for apps and platforms
11. AskGalore
AskGalore focuses on training and adapting large language models for specific business needs rather than keeping them generic. Their work usually starts with preparing data properly, which sounds obvious but is often where projects slow down. They handle both training from scratch and improving existing models like GPT or LLaMA, depending on what fits the situation.
They also stay involved after deployment, which is where things tend to get messy in real projects. Models need updates, prompts need adjusting, and performance drifts over time. AskGalore treats this as part of the normal lifecycle, not an afterthought. Their services cover web and mobile integrations, so LLM features are often built directly into apps rather than sitting as separate tools.
Key Highlights:
- Work with both custom-trained and pre-trained LLMs
- Focus on industry-specific use cases like healthcare, finance, and legal
- Handle full lifecycle from data preparation to deployment
- Support both cloud and on-premise model setups
Services:
- Custom LLM development and training
- Fine-tuning and prompt engineering
- LLM deployment and system integration
- AI chatbot and automation development
12. LeewayHertz
LeewayHertz approaches LLM development from a fairly structured, enterprise-oriented perspective. Their work often includes building models from the ground up or adapting existing ones to match internal business data. A lot of their projects revolve around improving how companies handle information – whether that’s analyzing documents, supporting decision-making, or automating communication tasks.
They also spend time on different training approaches like fine-tuning and few-shot learning, which suggests their projects go beyond simple API integrations. Their teams cover both AI and traditional software development, so LLMs are usually part of a larger system rather than the only focus. That includes chatbots, assistants, and data analysis tools built around language models.
Key Highlights:
- Work with both custom-built and pre-trained LLMs
- Apply different training approaches like fine-tuning and few-shot learning
- Experience with enterprise use cases like compliance and technical support
Services:
- LLM consulting and strategy
- Custom LLM development
- Model fine-tuning and optimization
- LLM-powered application development
- NLP and text analysis solutions
13. Addepto
Addepto focuses on using large language models as part of data-driven systems rather than treating them as standalone tools. Their work often connects LLMs with analytics, business intelligence, and internal knowledge systems. In practice, that might look like building a chatbot that pulls from company data or setting up a system that helps teams search and interpret large datasets without needing technical queries.
They work across industries like healthcare, finance, and manufacturing, where LLMs are used for tasks such as document processing, customer interaction, or internal communication. Instead of pushing one type of solution, they adapt models based on the use case, often combining them with data engineering and machine learning workflows.
Key Highlights:
- Combine LLMs with data analytics and business intelligence systems
- Work across industries like healthcare, finance, and manufacturing
- Focus on use cases like chatbots, knowledge search, and data analysis
- Integrate LLMs into existing business processes
Services:
- LLM-based solution development
- AI chatbot and virtual assistant development
- Data analysis and knowledge management systems
- LLM integration and customization
- Machine learning and data engineering
14. MindInventory
MindInventory works with large language models, using them within bigger AI systems. They often connect these systems to real operational issues, rather than just using them for experiments. Their projects generally start by properly defining the problem. While this might seem straightforward, in practice, it significantly influences how the entire system is built.
They also dedicate time to refining and controlling how these models behave, particularly addressing issues like ‘hallucination’ and ensuring response validity. This is evident in their use of methods such as retrieval-based systems and post-processing layers. MindInventory typically handles the complete lifecycle, from selecting the appropriate model to its deployment and subsequent updates.
Key Highlights:
- Work with LLMs as part of full AI systems, not standalone features
- Focus on real-world use cases like copilots and internal assistants
- Apply techniques to control model output and reduce errors
- Experience across healthcare, retail, construction, and education
- Involved in both development and post-deployment support
Services:
- LLM consulting and solution design
- Custom LLM development and fine-tuning
- LLM integration into applications and workflows
- Retrieval-augmented generation and prompt engineering
- Model testing, validation, and optimization
15. AleaIT Solutions
AleaIT Solutions approaches LLM development in a fairly direct way, focusing on automation and language-based features that can be added to business systems. Their projects often revolve around tasks that are repetitive or data-heavy – things like document processing, customer interaction, or basic analytics.
They also work across a wide mix of industries, which shows in the variety of use cases they describe – from finance and healthcare to retail and manufacturing. In many cases, the LLM is used as a supporting layer inside a larger application, not the core system itself.
Key Highlights:
- Focus on practical LLM use cases like automation and chatbots
- Work with pre-trained models and adapt them to business workflows
- Experience across industries including finance, healthcare, and retail
Services:
- Custom LLM development and fine-tuning
- AI-powered automation solutions
- Chatbot and conversational AI development
- LLM integration into existing systems
- Data processing and analysis tools
16. Geniusee
Geniusee takes a more engineering-heavy approach to LLM development, especially when it comes to custom models and data control. Their work often focuses on situations where off-the-shelf APIs are not enough – for example, when companies need consistent outputs or need to keep data fully private.
A lot of their projects involve retrieval-based systems and fine-tuning, especially for industries like fintech or healthcare where accuracy matters more than creativity. They also pay attention to how models behave over time, including prompt structure and evaluation workflows. Geniusee usually stay involved after deployment, which makes sense because these systems tend to drift or need adjustments once they’re used at scale.
Key Highlights:
- Focus on custom LLM systems with controlled data environments
- Use RAG and fine-tuning for domain-specific tasks
- Work with industries like fintech, healthcare, and legal
- Emphasize consistency and reproducibility of outputs
Services:
- LLM consulting and use-case definition
- Custom LLM development and training
- Fine-tuning and RAG implementation
- LLM integration into existing platforms
17. Mistral AI
Mistral AI operates closer to the core of model development, building foundation models and tools that other teams can adapt. Their positioning leans toward flexibility – giving companies the ability to train, fine-tune, or deploy models in different environments, including on-premise setups or edge systems. That flexibility shows up across their product stack, which includes tools for building agents, managing workflows, and running AI applications without relying entirely on external APIs.
There is also a noticeable focus on real-world deployment rather than just research output. Their models are used in industries like manufacturing and logistics, where the goal is less about generating text and more about supporting operational tasks. The idea of “autonomous work” appears often in their materials, which translates into things like enterprise search, workflow automation, or coding assistance.
Key Highlights:
- Builds and provides foundation models and open-weight systems
- Supports deployment across cloud, on-premise, and edge environments
- Focus on agent-based workflows and autonomous task execution
Services:
- Custom model training and fine-tuning
- AI agent and workflow development
- Enterprise search and content generation tools
- Application development platforms for AI systems
- Model deployment and infrastructure setup
18. LightOn
LightOn focuseses on a narrower but very specific problem – how to run AI on sensitive data without sending it outside the organization. Their platform is built around on-premise and hybrid setups, where both the data and the computation can stay within controlled environments.
A big part of their offering revolves around search and reasoning over internal data. Instead of building general chatbots, LightOn works on systems that can process large volumes of documents – including technical diagrams, reports, or even handwritten notes – and turn them into something searchable and usable. The idea is not just to retrieve information, but to combine it across sources and generate structured answers.
Key Highlights:
- Focus on on-premise and sovereign AI deployment
- Designed for environments with strict data security requirements
- Handles multimodal data including text, images, and technical documents
- Supports integration with existing enterprise data sources
- Strong emphasis on access control and data governance
Services:
- On-premise RAG platform for document search and reasoning
- API-based AI engine for integration into internal systems
- Ready-to-use chat and search interfaces for teams
- Data synchronization and indexing across multiple sources
19. InteliGems
InteliGems is focused on a fairly specific corner of the LLM space – regulated environments where outputs need to be traceable and explainable, not just plausible. Their work revolves around what they call governed AI agents, which are designed to follow predefined rules, policies, and approval flows. InteliGems builds systems that sit inside existing compliance processes, where every decision can be tracked back to a source or rule.
There is also a noticeable emphasis on audit readiness – for example, outputs can include inline citations or evidence trails, which is something that often gets overlooked until a compliance team asks for it. In practice, this shows up in use cases like healthcare classification or regulatory data extraction, where a vague answer is simply not acceptable.
Key Highlights:
- Focus on governed AI agents for regulated industries
- Strong emphasis on audit trails, traceability, and explainability
- Open-source platform with private deployment options
- Built-in guardrails, approval workflows, and control systems
Services:
- AI strategy and compliance-focused workshops
- Custom multi-agent system development
- Data curation and domain-specific fine-tuning
- AI validation with accuracy and groundedness checks
20. Pangeanic
Pangeanic approaches LLMs from a language and data perspective. Their work is closely tied to machine translation, where they combine traditional neural machine translation systems with LLM-based post-editing. The idea is not to replace existing translation pipelines, but to improve them by adding a layer that handles fluency, tone, and context more naturally.
One thing that stands out with Pangeanic is how much they rely on training data and domain adaptation. Their translation systems can be adjusted using company-specific terminology or past translations, which is useful in industries where consistency matters more than creativity. They also integrate with common CAT tools like Trados or MemoQ, which suggests their users are often translation teams rather than general AI buyers.
Key Highlights:
- Combines neural machine translation with LLM post-editing
- Strong focus on multilingual data and domain-specific datasets
- Integration with existing translation tools and workflows
Services:
- LLM-based translation and automatic post-editing
- Machine translation APIs and plugins
- Data annotation and multilingual dataset creation
- Named entity recognition and sentiment analysis
Conclusion
If you look across these companies, a pattern starts to show. Most of them are not trying to build the next general-purpose model. Instead, they’re taking what already exists and shaping it around specific business problems – internal tools, document-heavy workflows, customer support, things like that. It’s a bit less flashy than what you usually hear about LLMs, but probably closer to how the technology is actually being used day to day.
There’s also a noticeable difference in how teams approach implementation. Some lean toward full custom builds with tight control over data, others prefer faster integrations using existing APIs. Neither approach is “better” in general – it really depends on how sensitive the data is, how much control is needed, and honestly, how patient the company is willing to be. What matters more is whether the LLM fits into the system without creating extra complexity. That’s where most projects either start working… or quietly stall.
