DATA SCIENCE / FROM QUESTION TO EVIDENCE

Data Science.
Clearer decisions.

Bring order to data, explain your numbers and test forecasts. We develop a useful result around one concrete question, with defined scope, documented limits and a handover your team can work with.

Individual project services · Price on request · Documentation & team handover

Synthetic example

Plan with a range.

Weekly demand · illustrative units

Fictional data: six observed weeks, then a forecast with an illustrative uncertainty range. No project result or accuracy claim. A real range must be validated.
FOUR STARTING POINTS

What needs to become clearer?

Each service can be scoped on its own. We start where your data and your question stand today.

Which data can we trust for this question?

Data quality & foundations

Clarify sources, definitions and data quality, with repeatable checks and a practical plan for a usable foundation.

The agreed result

A documented dataset with validation rules, known limitations and prioritised next steps.

Scope & process

What is happening, and where should we act?

Dashboards & BI

Define measures, connect sources and build reports whose results can be traced back to the data.

The agreed result

An agreed report with traceable measures, validated data flows and a practical handover for your team.

Scope & process

Does a model improve on the way we plan today?

Forecasting & optimisation

Explore demand, capacity or planning with a baseline, held-out test data and explicit uncertainty.

The agreed result

An evaluated prototype, compared with the baseline, with documented limitations and a recommendation for the next step.

Scope & process

Who notices a failure, and who gets the process running again?

Data platforms & model operations

Turn data flows and suitable models into repeatable processes, with tests, monitoring and named owners.

The agreed result

A bounded pipeline or agreed model workflow, with tests, operating instructions and clear responsibilities.

Scope & process
A CHECKABLE PATH

An outcome at every stage.

We agree the next step based on what the evidence supports. A dashboard may be sufficient; a model needs its own validation.

  1. Define the decision

    One business question, an owner and an agreed measure of success.

  2. Understand the data

    Check sources, permissions, quality and whether the available data fits the question.

  3. Build & evaluate

    A bounded result, compared against reference values or a baseline.

  4. Hand over & decide

    Code, documentation and a walkthrough. Operations need their own agreed scope.

EXPLAINABLE & MAINTAINABLE

Your team stays in the picture.

We plan ownership from the start: who defines a measure, who approves access, and who responds when data stops arriving.

  • Agreed code and project files, with instructions for running or maintaining them.
  • Data sources, assumptions, checks and known limitations in writing.
  • A walkthrough for users and maintainers; access and usage rights are agreed, including third-party licences.
A SCOPED PROJECT / PRICE ON REQUEST

Start with one concrete question.

Describe the decision, your available sources and who will use the result. We clarify feasibility and propose deliverables, acceptance criteria, schedule and price. Each further stage is agreed explicitly.

A short description is enough for the first enquiry. Please do not send confidential datasets or access credentials through the contact form.

Request a project proposalView shop scope (German)

Project work and ongoing costs are shown separately. Hosting, licences, compute and support are included only when explicitly agreed.

A good place to start.

neurofunken knowledge assistant

Hello! What would you like to improve? I can explain our services and help you find a useful next step.

Replies from our editorial knowledge base.

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