ZTABS fine-tunes large language models for Helsinki enterprises — teaching foundation models to handle Finnish, one of the most challenging languages for AI. Healthcare text mining, gaming analytics, and Nordic enterprise automation built on EU-sovereign infrastructure.
Finnish breaks AI language models in ways that most vendors quietly ignore. With 15 grammatical cases, agglutinative morphology that can stack suffixes to create words like "juoksentelisinkohan" (I wonder if I should run around aimlessly), and a vocabulary where a single root word produces hundreds of inflected forms, Finnish is among the hardest languages for NLP systems to handle. Standard LLMs trained predominantly on English text treat Finnish as an afterthought — tokenizers fragment Finnish words into meaningless subword pieces, and generation models produce grammatically broken output that no Finnish speaker would accept. This matters commercially because Finland runs on Finnish. Patient records at HUS (Helsinki University Hospital) are in Finnish. Kela (Social Insurance Institution) processes claims in Finnish. Supercell's player support handles Finnish gamers in Finnish. Government digital services (suomi.fi) operate in Finnish. Fine-tuning teaches a foundation model to actually understand Finnish morphology and generate correct Finnish text — turning a model that "sort of" handles Finnish into one that works.
Helsinki's demand for Finnish-language LLM fine-tuning comes from sectors where language quality is non-negotiable. Healthcare is the largest driver: Finland's healthcare system generates vast quantities of clinical text in Finnish — diagnosis notes, referral letters, discharge summaries, and patient communications. The Kanta system (Finland's national health information archive) contains structured and unstructured data from every healthcare encounter. Researchers at THL (Finnish Institute for Health and Welfare) and FIMEA (Finnish Medicines Agency) need AI that can mine this text for epidemiological insights, adverse drug event detection, and clinical decision support — but the Act on the Secondary Use of Health and Social Data requires Findata permits and Finnish data residency. Gaming is the second driver: Rovio (Angry Birds), Supercell (Clash of Clans), and Remedy (Control, Alan Wake) generate player behavior data and community content that needs Finnish-language processing for localization, sentiment analysis, and content moderation. Public sector is the third: DVV (Digital and Population Data Services Agency) and Traficom need Finnish NLP for citizen service automation, document processing, and regulatory compliance monitoring across Finnish, Swedish, and Sámi.
Our Helsinki fine-tuning work starts from a premise unique to Finnish: the tokenizer matters more than the model. Standard BPE tokenizers trained on English-heavy corpora fragment Finnish words aggressively — "tietokoneohjelmointi" (computer programming) becomes 4-5 subword tokens instead of one semantic unit, wasting context window and degrading comprehension. We evaluate base models specifically for Finnish tokenizer efficiency: Silo AI's Poro and Viking models use Finnish-optimized tokenizers, while Llama 3's tokenizer handles Finnish better than its predecessors but still fragments long compounds. For healthcare fine-tuning, we work within Findata's secondary use framework — obtaining data permits, implementing required pseudonymization, and training on CSC infrastructure (Finland's national IT center for science). Training compute runs on LUMI (when research-grade scale is needed) or AWS eu-north-1 (Stockholm, the nearest AWS region) for production workloads. We maintain Finnish-language evaluation benchmarks covering clinical text, legal documents, customer service conversations, and technical documentation — because evaluating Finnish AI on translated English benchmarks is meaningless. Every fine-tuned model undergoes evaluation by native Finnish speakers who test morphological correctness, case usage, and register appropriateness (formal written Finnish vs. colloquial puhekieli).
Healthcare
A Finnish health research institute needed to analyze 2.8 million unstructured clinical notes from HUS patient records to identify patterns in type 2 diabetes progression. The notes were in Finnish, with medical terminology, abbreviations specific to Finnish clinical practice (e.g., "pkl" for "poliklinikka"), and inconsistent formatting across decades of records. Commercial NLP tools trained on English clinical text produced 40-50% accuracy on Finnish medical entity extraction. Findata permit requirements mandated all processing on Finnish infrastructure.
We fine-tuned Poro 34B on a Findata-approved dataset of 120,000 annotated Finnish clinical notes. The model learned Finnish medical entity extraction (diagnoses, medications, lab values, procedures), temporal relation identification (onset dates, treatment timelines), and clinical coding suggestion (ICD-10 mapping from Finnish free text). Training ran on CSC's computing environment using 8× NVIDIA A100 GPUs. The annotation was performed by a team of Finnish medical students supervised by clinicians from HUS.
Finnish medical entity extraction accuracy improved from 48% (base model) to 89.7% (fine-tuned). The model correctly handles Finnish clinical abbreviations and compound medical terms that zero-shot models misinterpret. Diabetes progression pattern analysis that would have required 18 months of manual chart review was completed in 6 weeks. All processing remained within CSC infrastructure — full Findata compliance maintained throughout.
We clean, deduplicate, and structure your training data into high-quality instruction-response pairs. Quality data is the single biggest factor in fine-tuning success.
Fine-tune GPT-4o Mini and GPT-3.5 Turbo through OpenAI's API with systematic hyperparameter optimization, validation splits, and automated evaluation.
Fine-tune Llama 3, Mistral, Phi, and other open-source models using LoRA, QLoRA, and full fine-tuning on cloud GPUs or your own infrastructure.
Rigorous evaluation against your specific tasks with automated benchmarks, human evaluation, and A/B testing against base models to quantify improvement.
Align model outputs with human preferences using DPO (Direct Preference Optimization) and RLHF techniques for better quality and safety.
Deploy fine-tuned models via OpenAI, vLLM, TGI, or Ollama with optimized inference, batching, and auto-scaling for production workloads.
Helsinki's LLM market is shaped by Silo AI's legacy (acquired by AMD for $665M in 2024). Silo AI built Poro and Viking — the best Finnish-language LLMs available — and their acquisition left a gap in Finnish enterprise AI services. FCAI (Finnish Center for AI) at Aalto and the University of Helsinki produces research talent but focuses on academic projects. Reaktor and Futurice have AI practices but are generalist digital consultancies. CSC provides compute infrastructure but not fine-tuning services. International vendors (OpenAI, Anthropic) offer Finnish support but with visibly lower quality than English — and no Findata-compliant training infrastructure. ZTABS brings production fine-tuning capability specifically designed for Finnish-language tasks, with experience navigating Findata permits and CSC infrastructure.
LLM fine-tuning in Helsinki ranges from €30,000 for targeted Finnish NLP tasks (classification, extraction) to €150,000+ for healthcare NLP platforms requiring Findata permits and clinical validation. Senior ML engineers with Finnish NLP experience are rare — perhaps 200-300 in the country — and command €130-€190/hr. ZTABS delivers at competitive rates with Finnish-language evaluation capability and Findata-compliant infrastructure experience.
Verified reviews from real client engagements — sourced from our public testimonial archive and Clutch profile.
My experience is throughout positive. Communication, service, the short response times and the flawless execution of a challenging topic was absolutely great. ZTABS is definitely my first choice again.
Christian Neff
Bank Software Advisory · Bank Software Advisory
Fintech
Fantastic Agency! I couldn't fault them even if I tried. They always go above and beyond to meet your expectations and always produces quality work. Thank you ZTABS.
Stephanie Kal
CEO · Beauty Finder Australia
Marketplace
It has been great working with ZTABS. They bounce off the ideas along the way. Amazing Experience.
Joel Rowe
CEO · Drill Quoter
Marketplace
We don't just contract — we ship and operate our own software. 17 products in production.
Common questions about LLM fine-tuning for Helsinki businesses
Finnish is agglutinative — words are formed by stacking suffixes onto root words. The word "talossanikin" (in my house too) contains a root and three suffixes. This creates an enormous vocabulary that standard tokenizers cannot efficiently encode. Finnish also has 15 grammatical cases, vowel harmony, and consonant gradation — all of which affect word forms in ways that models trained primarily on English struggle to learn without fine-tuning on Finnish data.
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