Machine Learning

Recognize patterns. Decide earlier.

Forecast demand, detect anomalies or plan maintenance more effectively: we build models for a clearly defined decision process. We measure whether they help by comparing them with your current process and a simple baseline, before extensive integration work begins.

What we take care of Discuss your project

Assessed use case with stop criteria · Planning and implementation by OTOKO®

Your brief for OTOKO®

What we take care of for you.

We first define which action follows from the result and which errors are costly. An overlooked fault has different consequences from an unnecessary maintenance alert. From this we derive target metrics and acceptable error rates. Training data is checked for completeness, timely availability and representativeness. Information that only becomes known after the event being forecast must not accidentally flow into training. A simple baseline shows whether a more complex model actually delivers additional benefit.

The possible scope of services

Assessment of use cases by benefit, data availability and risk
Prototype on real data with comparison to the current process
Model development with documented features, training data and quality metrics
Integration via interfaces into ERP, MES, CRM or payment systems
Explainability of results for business units, auditors and affected persons

We define the specific scope, your involvement and the acceptance criteria before the start.

Technology explained clearly

How we carry out the task.

Training and evaluating under realistic conditions

Time-dependent data is checked in a suitable chronological order. We look at individual sites, product groups or rare cases separately, so that a good overall figure does not conceal weak subareas. Features and model versions are documented in a traceable way. In the live process, we provide suitable indications of uncertainty and limits alongside the result. If a prediction falls outside the tested range, an expert review path can be provided. The calculation can be integrated as a batch run or an interface, depending on the required response time.

Production readiness needs more than a notebook test

Release approval covers data supply, response time, behavior when inputs are missing and a business assessment. We provide the documented comparison against the baseline as well as a plan for monitoring and updates. A prototype may also show that the available data is not sufficient for the desired prediction. In that case, data gaps and alternative approaches are identified before further development costs are incurred.

Meeting room at the OTOKO® Cologne office

A verifiable result

What you keep working with.

Assessed use case with stop criteria
Production-ready model with interface
Model card with quality, limits and explainability

The handover brings together implementation and documentation. Together, we review the agreed cases and record any remaining tasks.

Your project in detail

Measure predictions against the actual business problem.

Whether it is demand forecasting, anomaly detection or classification, we first examine which decision a model is meant to improve and which errors are acceptable in that context. Data preparation, model choice and evaluation follow only from that.

Create a solid baseline for comparison

A complex model has to hold its own against a simple rule or the previous way of working. We define such a baseline and separate training, validation and test data appropriately for the task. For time-dependent data, we specifically prevent information from the future from indirectly entering the training data.

Besides average accuracy, we examine relevant subgroups and rare cases. A good overall rate can conceal an unusable process if it is precisely the critical exceptions that are assessed incorrectly. False alarms, missed cases and the cost of manual review therefore feed into the business assessment.

Integrate the model into an accountable workflow

Predictions need a recipient and an action. We define when the model provides a recommendation, when a person decides and when the system should refrain from making a statement because the data is insufficient for a reliable one. Interfaces, response times and the required explanations are aligned with this workflow.

After launch, input data and result quality are monitored. Changed product offerings or ways of working can affect a model’s suitability. We agree on triggers for re-evaluation as well as a fallback to a previous version or a manual process. Automatic retraining only makes sense if review and approval are also organized.

Illustrative project scenario

How the service helps in everyday use.

Example: A service department wants to prioritize incoming cases. We train and test on historically assessed cases, review the cost of errors and route uncertain results to manual review. The benefit is measured by processing time and business quality, not by a model metric alone.

This example explains a possible process and is not a customer reference.

Before the first step

Your questions about Machine Learning.

Do you guarantee a specific hit rate?

A reliable target figure can only be set once the use case and representative data are known. We agree on measurement methods and stop criteria instead of a blanket success rate.

What happens if reality changes?

We monitor input data and available quality feedback. An update is carried out in a controlled manner and is checked against the existing version before it goes into production.

Your project

Which task would you like to solve?

Describe your situation and the desired result. The selected service will be included in the contact request.

Request this service

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Recognize patterns. Decide earlier.