WYN / ARTIFICIAL INTELLIGENCE

EN — 2026

Artificial Intelligence

We turn repetitive decisions, information flows and the work teams do every day into measurable artificial intelligence systems.

Intelligence & communication

The need clears up and the right scope becomes visible.

In most organisations artificial intelligence stays a demo. The real need is for the right information to reach the right person, at the right moment, with human approval when required. WYN makes that flow visible before choosing a model.

KİME UYGUN?

  • Teams working with heavy information
  • Organisations that classify, summarise or answer similar content every day
  • Companies that want to bring artificial intelligence into their workflow safely and under control

DELIVERABLES

  • Use-case and process map
  • Model and data flow recommendation
  • Content search, classification or summarisation module
  • Permission, record and human approval flow
  • Measurement plan and launch documentation

TECHNOLOGY USED

Custom web applications, Python services, model providers, vector search, JSON-based APIs, role-based permissions and audit records.

TAHMİNİ KAPSAM

The first version usually focuses on a single critical workflow. Once data quality, user count, model choice and integration scope are clear it expands in modules.

SÜREÇ

  1. 01We extract the problem and the decision points.
  2. 02We define the secure data boundary and the success measure.
  3. 03We prototype a small but real workflow.
  4. 04We add human approval, error handling and a measurement layer.
  5. 05We grow the working system together with the team.

SIK SORULAR

What you need to know before deciding.

Do you build a custom model for every project?

No. First we measure whether existing models and in-house data are enough. If needed we evaluate a custom model or an on-premise option.

What happens if the AI gives a wrong answer?

We do not automate critical decisions. We manage the risk with confidence thresholds, human approval, source citation and record keeping.

Is our data used to train models?

We define clearly, inside the project scope, where data is stored, who can access it, and which provider it is never sent to.