AI Applications

The amount of data needed depends on the problem. In the first meeting we review the available data and clarify together the project's feasibility and which data needs to be collected.

Yes. We integrate the models we develop into your ERP, web and mobile applications via API.

Yes. In line with the scientific method, we recommend starting with a small-scope prototype and measurement, then scaling based on the results.

It depends on the scope. Discovery and prototyping usually take a few weeks; go-live and integration may take a few more weeks to months. We give a firm schedule after assessing the data.

Data security is set according to your needs; the model can run on your own infrastructure or in a controlled cloud environment. Confidentiality terms are clarified in the contract.

General-purpose services are sufficient for many tasks. If you have organization-specific data, confidentiality or accuracy requirements, a custom model or customization is the better choice; we evaluate both together in terms of cost and benefit.

Artificial intelligence is a broad field that aims to enable machines to perform tasks requiring intelligence. Machine learning, within this field, refers to models that learn from data instead of having rules written by hand. Most enterprise projects rely on machine learning methods such as classification, prediction and anomaly detection.

Success is measured by business indicators defined before the project starts, such as a reduction in error rate, time saved, forecast accuracy or cost reduction. Model performance (accuracy, precision, recall) and business impact are reported separately, so technical success isn't confused with real benefit.

Model performance can drop as the data distribution changes. That's why models in production are monitored, alerts are raised when performance drops, and they are retrained with new data when needed. The maintenance and retraining plan is shared in writing with the project delivery.

Yes. We develop solutions such as assistants that answer from your corporate knowledge base, document summarization and content classification. The most critical issues here are data confidentiality, grounding answers in sources and keeping incorrect answers under control. We clarify these at the start of the design.

A department lead who knows the business problem and a technical person who can provide access to the data are enough. We handle the data science and engineering. Decision makers attending interim demos is the most important factor in making sure the model fits the real need.

In projects involving personal data, we design KVKK compliance, data minimization and access authorization from the start. In decision support systems, we take care to make what the model bases its results on explainable. Critical decisions that require human approval are not left to automation.

We develop applications such as quality control, demand forecasting, document processing and customer analytics in fields like manufacturing, retail, logistics, services and the public sector. Since each industry's data and risks are different, we design the solution based on analysis results, not a ready-made package.