Custom LLM development in Helsinki — private AI grounded in your business
We design and build production LLM systems for Finnish companies: private RAG assistants, model evaluation, structured workflows and secure integrations. We start with the smallest architecture that can meet the business requirement rather than training a model by default.
- Private answers grounded in your approved sources
- Model and architecture selected for the actual use case
- Evaluation against real questions before launch
- Access control, citations and audit logs
- EU-region deployment and documented handover
LLM systems we build
Each solution has a narrow job, an approved source set and measurable acceptance criteria.
Private knowledge assistants
Search company documents in natural language and return answers with citations and permission-aware retrieval.
Document extraction
Turn contracts, reports, orders and forms into validated structured data for existing systems.
Support copilots
Draft grounded replies for agents, surface the relevant policy and escalate uncertainty instead of inventing an answer.
Specialist search
Combine semantic retrieval, filters and reranking for technical, legal or operational document collections.
Structured AI workflows
Use models inside deterministic processes with schemas, approval gates, retries and complete decision logs.
LLM evaluation
Test answer quality, citations, safety, latency and cost on a repeatable set of real company questions.
Custom does not automatically mean training a new model
Most companies need a system that knows their current material, not a newly trained foundation model. Retrieval-augmented generation keeps facts in an updateable knowledge layer and gives every answer a source, while the underlying model can be changed as quality, price and data requirements evolve.
Fine-tuning is useful when repeated examples must teach a stable output format, classification rule or specialist tone. It is a poor substitute for a knowledge base because facts become expensive to update and difficult to trace. We compare prompting, RAG and fine-tuning against the same evaluation set before recommending an architecture.
Production quality comes from the surrounding system: ingestion, permissions, retrieval tests, structured validation, fallbacks, logging and cost controls. Those are delivered with the application so your team can operate it after the pilot.
How a project runs
- 01
Requirement and data review
We define the decision or task, inspect representative source material and agree what a correct answer means.
- 02
Architecture and evaluation set
We select a baseline model and create tests covering ordinary, difficult and out-of-scope questions.
- 03
Working pilot
A narrow system runs on your real material with citations, permissions and structured output where needed.
- 04
Production handover
Integrations, monitoring, documentation and cost controls are completed before ownership or support is agreed.
Technologies we work with
Models
- Commercial and open models
- Model routing
- Structured outputs
- Prompt versioning
Retrieval
- Embeddings
- Vector and hybrid search
- Reranking
- Source citations
Quality
- Golden test sets
- Answer evaluation
- Safety tests
- Cost and latency tracking
Delivery
- EU cloud regions
- APIs and integrations
- Access control
- Monitoring and audit logs
Related services
Frequently asked questions
Or email us directly at info@datakooshktech.tech.
Let's assess your LLM use case
Bring one workflow and representative source material — we will recommend the smallest viable architecture and a fixed pilot scope.