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, and Helsinki is where the organizations that need Finnish-language AI are concentrated. 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 and where Finnish is the only acceptable language for production systems serving Finnish citizens and consumers. 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 and other agglutinative languages: 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).
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A Finnish health research institute needed to: analyze 2.8 million unstructured clinical notes from: HUS (Helsinki University Hospital) patient records spanning: 2005-2024 to: identify patterns in: type 2 diabetes progression — specifically: which comorbidity combinations and: medication sequences: correlated with: faster progression to: insulin dependence. The data: entirely in Finnish, with: medical terminology, abbreviations specific to: Finnish clinical practice (e.g., "pkl" for: "poliklinikka," "rtg" for: "röntgen," "lab" for: "laboratorio"), and: inconsistent formatting across: decades of records. The problems: (1) Finnish language: commercial NLP tools trained on: English clinical text: produced 40-50% accuracy on: Finnish medical entity extraction — useless for: research purposes. Finnish morphological complexity (15 grammatical cases, agglutinative word formation): making Finnish medical NER: fundamentally harder than: English. (2) Regulatory: Findata (the Finnish Social and Health Data Permit Authority) permit requirements: mandating all processing on: Finnish infrastructure — no data leaving: Finland under: any circumstances.
We fine-tuned Poro 34B (Silo AI's Finnish-language model) on: a Findata-approved dataset of: 120,000 annotated Finnish clinical notes over: 8 weeks. Annotation: performed by: a team of: 12 Finnish medical students supervised by: 3 clinicians from: HUS — each note annotated for: diagnoses (ICD-10 codes), medications (ATC codes), lab values (with units and: reference ranges), procedures, temporal markers (onset dates, treatment changes, follow-up intervals), and: clinical outcomes. The fine-tuned model: performing: (1) Finnish medical entity extraction (diagnoses, medications, lab values, procedures), (2) temporal relation identification (onset dates, treatment timelines, medication sequences), (3) ICD-10 mapping from: Finnish free text (critical for: structuring: decades of: unstructured notes into: analyzable data), and: (4) clinical note summarization (condensing: multi-page clinical narratives into: structured research-ready: records). Training: ran on: CSC's computing environment using: 8× NVIDIA A100 GPUs in: Kajaani.
Finnish medical entity extraction: accuracy improved from: 48% (base model) to: 89.7% (fine-tuned) — a 87% improvement that: made the research: viable. The model: correctly handling: Finnish clinical abbreviations, compound medical terms (esim. "tyypin 2 diabeteksen hoitotasapaino" — type 2 diabetes treatment balance), and: the case-inflected forms of: drug names that: zero-shot models: consistently misinterpreted. Research impact: diabetes progression analysis that: would have required: 18 months of: manual chart review: completed in: 6 weeks. The research team: identifying: 3 previously unreported: comorbidity-medication interaction patterns that: correlated with: accelerated insulin dependence — results: submitted for: peer review in: a leading diabetology journal. Compliance: all processing within: CSC infrastructure throughout. Findata compliance: maintained — the permit authority: satisfied with: the data handling protocols. The fine-tuned model: now available to: other Finnish health research groups through: CSC — accelerating: Finnish-language clinical NLP: research across: multiple institutions.
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 fine-tuning market is shaped by Silo AI's legacy, Finland's research-heavy AI ecosystem, and the unique challenge that Finnish poses for commercial AI products built primarily for Indo-European languages. (1) Silo AI aftermath — Silo AI built Poro and Viking (the best Finnish-language LLMs available) before AMD acquired them for $665M in 2024. Under AMD ownership, focus shifted to hardware-software integration. The acquisition left a significant gap in Finnish enterprise AI services — Silo AI's former clients need a new partner for Finnish-language fine-tuning. Several ex-Silo AI engineers went independent or joined Finnish startups, creating a small freelance pool but no scaled alternative. (2) Research ecosystem — FCAI (Finnish Center for AI, joint Aalto/University of Helsinki) produces world-class ML research and the annual Nordic AI conference. University of Turku's TurkuNLP lab has contributed significantly to Finnish NLP resources (FinBERT, Finnish text corpora).
CSC operates LUMI supercomputer in Kajaani — sovereign EU compute ideal for training runs. These are research resources, not commercial fine-tuning services, but they create the foundation models and evaluation datasets that production fine-tuning builds on. (3) Finnish digital consultancies — Reaktor, Futurice, and Vincit have AI practices but are generalist digital consultancies (UX, product, agile) that add AI capabilities. Gofore and Solita (now Accenture) serve the government and healthcare sectors. They can build data pipelines and basic ML but lack deep LLM fine-tuning expertise. (4) International competition — Aleph Alpha (German sovereign AI), Mistral (French), and OpenAI all offer Finnish support but with visibly lower quality than English — and no Findata-compliant training infrastructure. Nordic competitors (Computation from Sweden, Computation from Denmark) are emerging but lack Finnish linguistic depth.
ZTABS brings production fine-tuning capability specifically designed for Finnish-language tasks, with Findata permit navigation experience and CSC/LUMI infrastructure knowledge — filling the specific gap Silo AI's acquisition created.
LLM fine-tuning in Helsinki benefits from Finland's strong research ecosystem and the availability of sovereign EU compute infrastructure through CSC and LUMI. Single-task fine-tuning (Finnish text classification, extraction, summarization, 4-6 weeks): €55,000-95,000 ($60,000-$103,000), the standard entry point for Finnish-language domain adaptation. Multi-task Finnish NLP platform (multiple tasks, health/gaming/industrial domains, 8-12 weeks): €130,000-250,000 ($142,000-$272,000) — the typical engagement for healthcare or gaming sector clients. Enterprise Finnish AI platform (custom models, Findata compliance, RAG integration, 12-16 weeks): €280,000-480,000 ($305,000-$522,000) — for organizations deploying fine-tuned AI across healthcare operations, clinical research, or multi-game player analytics. Healthcare-specific fine-tuning: Findata permit application adds 4-8 weeks to project timeline and €5,000-15,000 in regulatory compliance costs. CSC LUMI access for training: available through project allocation (free for Finnish research organizations, commercial rates for enterprises). GPU compute on AWS eu-north-1: €3,500-14,000/month. Hetzner Helsinki: €2,000-7,000/month for more cost-effective hosting.
Senior ML engineers with Finnish NLP experience are rare — perhaps 200-300 in Finland — and command €130-€190/hr. ZTABS delivers at €95-145/hr with Finnish-language evaluation capability and Findata-compliant infrastructure.
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. 23 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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