Data & AI consulting in Finland — from reporting to prediction
We build the data foundation first and the model second: one reliable data model, dashboards leadership trusts, and machine-learning models that predict demand, churn or capacity and land in the tools your team already uses.
- One data model instead of competing spreadsheets
- Dashboards built around real decisions
- Models delivered into your existing tools
- Honest assessment when ML is not the answer
- EU-hosted data, under a DPA
What we build
Data and machine-learning work that ends in a decision someone makes differently — not in a notebook.
Data modelling and consolidation
Sources are combined into one documented model with metrics defined once, so reports agree with each other.
Business intelligence dashboards
Dashboards built around the questions leadership actually asks, refreshed automatically instead of rebuilt monthly.
Demand and capacity forecasting
Models that estimate what is coming so purchasing, staffing and planning stop relying on gut feeling.
Churn and lead scoring
Scores that flag the accounts most likely to leave or convert, delivered into the CRM where sales works.
Anomaly detection
Automatic flags on unusual transactions, usage or process metrics, with thresholds tuned to your tolerance for false alarms.
ML engineering and deployment
Pipelines, versioning, monitoring and retraining, so a working model keeps working after the project ends.
Most prediction problems are data problems first
Companies usually arrive asking for a forecast and leave with something more useful: agreement on what a customer, an order and a month actually mean across their systems. Until those definitions are shared, every model and every dashboard is arguing with a different version of history.
So the first phase is unglamorous. We consolidate the sources, define the metrics once, and build reporting that leadership can act on without a manual reconciliation step. Very often that alone answers the original question — and it is the only way a later model gets trustworthy training data.
When prediction genuinely adds value, we keep the scope tight: one target variable, a baseline to beat, and a measurable business decision it feeds. A model that is 8% more accurate but never reaches the tool where the decision is made has changed nothing, so delivery and monitoring are part of the project, not an afterthought.
How a project runs
- 01
Data and goal review
We look at your sources and the decision you want to improve, and say honestly whether the data supports it yet.
- 02
Data model and baseline
Consolidated model, defined metrics and a simple baseline that any later model has to beat.
- 03
Model development
One target variable, evaluated against the baseline on your real history, with the trade-offs explained in business terms.
- 04
Deployment and monitoring
The output lands in your dashboard, CRM or workflow, with drift monitoring and a retraining plan.
Technologies we work with
Data
- Data engineering & pipelines
- Analytics modelling
- SQL & data warehousing
- Data quality checks
Machine learning
- Forecasting & regression
- Classification & scoring
- Anomaly detection
- Model evaluation
Analytics
- Business intelligence dashboards
- Power BI & Tableau
- Automated reporting
- Metric definitions
Cloud
- EU-region hosting
- Scheduled jobs
- CI/CD
- Monitoring & logging
Related services
Frequently asked questions
Or email us directly at info@datakooshktech.tech.
Let's look at your data
Tell us which decision you want to improve — you get an honest assessment and a fixed quote within 24 hours.