Artificial intelligence technologies
Systems that learn from data and support better decisions.
In artificial intelligence, VeriSet A.Ş. develops machine learning, AI-powered systems, data processing and intelligent automation technologies. The goal is to turn models that learn from data into measurable systems that work in the real world.
What is artificial intelligence?
Artificial intelligence (AI) is the general name for technologies that let computer systems perform tasks usually associated with human intelligence, such as learning, pattern recognition, inference and decision support.
Machine learning is the most widely used branch of AI: instead of writing every rule by hand, systems learn from example data. Deep learning is a machine learning approach that uses multi-layer neural networks.
- Area
- Artificial intelligenceArtificial intelligence and machine learning development
- Provider
- VeriSet A.Ş.Technology company based in Türkiye
- Contact
- [email protected]You can also write from the contact page
What does VeriSet do in this area?
Machine learning
Models that learn from patterns in data: classification, forecasting, clustering and anomaly detection.
AI-powered systems
End-to-end architectures that connect models to applications, services and workflows instead of leaving them on their own.
Data processing
Collecting, cleaning, labelling and preparing the data a model needs for training.
Intelligent automation
Workflows that automate repetitive work with rules and models together, while keeping people in control.
Where is artificial intelligence used?
The following are typical uses of this technology; every project is evaluated separately against its own data and goals.
- ForecastingPredicting values such as demand, load or failures from historical data.
- Anomaly detectionSpotting unusual behaviour, spikes and errors early.
- Text and document processingClassifying and summarising text and extracting information from it.
- Recommendation and personalisationSuggesting content or products based on user behaviour.
- Image and audio analysisTasks such as object recognition in images and speech or sound separation in audio.
Key concepts in AI
- Model
- A mathematical structure learned from data that produces predictions or decisions for new inputs.
- Training and inference
- Training is the stage in which a model learns from examples; inference is applying the trained model to new data.
- Overfitting
- When a model memorises its training data and performs badly on new data.
- Labelled data
- Examples whose correct answer is known. Used for a model to learn in supervised learning.
- Accuracy, precision, recall
- Metrics that measure model success from different angles. Which one matters depends on the cost of a wrong decision.
- Model drift
- A model's performance falling because real-world data changes over time.
How do we choose the right approach?
The same method does not fit every problem. The comparison below helps with the first decision.
| Approach | When it fits | What to watch for |
|---|---|---|
| Rule-based software | The rules are clear and rarely change, and every decision must be explainable. | Maintenance gets harder as rules multiply; it cannot capture ambiguous cases. |
| Classic machine learning | You have tabular data and need forecasting, classification or clustering. | Success depends heavily on data quality and on choosing the right features. |
| Deep learning | You have unstructured data such as images, audio or free text, and enough examples. | Needs more data and compute; decisions are harder to explain. |
| Ready-made model or service | You need quick results for a general task (e.g. text summarisation). | Data sharing, cost and customisation limits should be assessed up front. |
Common mistakes in AI projects
- Starting on the model without defining the problemFirst write down which decision will be improved and how success will be measured.
- Leaving data quality for laterMissing, mislabelled or skewed data makes even the best model fail.
- Looking only at training performanceA model should be tested on a separate test set of data it has not seen and, where possible, in real use.
- Not planning the move to productionRunning the model as a service, monitoring it and retraining it should be thought through from the start.
- Removing human oversightFor high-risk decisions, the model's suggestion and human approval should be designed together.
Checklist before you start
- Is the decision or process I want to improve clear?
- Do I know the metric I will judge success by?
- Do I have enough representative data?
- Are permissions and privacy requirements for using the data clear?
- Who will use the model's output, and how?
- If the model degrades over time, how will I notice?
How do we run AI projects?
Research
We understand the problem and the data, and question from the start whether AI is really needed.
Prototype
We try a quick model on a small dataset and agree on a measurable success criterion.
Build
We turn the model into a testable service and build the data flow and monitoring together.
Scale
As data and users grow, we retrain the model and keep monitoring its performance.
Frequently asked questions
What is the difference between AI and machine learning?
AI is a broad umbrella term. Machine learning is a branch of AI in which systems learn from data without explicit rules. Deep learning is a sub-field of machine learning that uses multi-layer neural networks.
What does VeriSet do in artificial intelligence?
VeriSet A.Ş. develops machine learning, AI-powered systems, data processing and intelligent automation technologies. Through R&D it researches new approaches and tries them out with prototypes.
How much data does an AI project need?
It depends on the problem. For some problems a small but representative dataset is enough. That is why we recommend building a quick prototype first and testing it against a measurable success criterion.
Should every problem be solved with AI?
No. Problems with clear, rarely changing rules are often solved more reliably and more cheaply with classic software. In the research step we assess whether AI really adds value.
How can I contact VeriSet about AI?
You can email [email protected] with a short description of the topic and the data you have.
Other areas of work
Data technologies
Big data, data infrastructure, data analysis and systems that turn data into meaningful knowledge.
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Modern and accessible mobile applications people can use in their daily lives.
Open pageWeb platforms
Modern web applications, services and high-performance digital platforms.
Open pageInfrastructure
Cloud systems, APIs, distributed architectures and reliable software infrastructure.
Open pageR&D
We research and experiment with new technologies and build solutions that can be used in the future.
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