01 — Services
AI that earns its place in daily operations—not a demo that stays in a slide deck.
We design and build AI features around a concrete process: a call that has to be understood while it is happening, a price that has to be estimated, a message that needs an answer. The model is one part. The data flow, the latency budget and the people who use the result matter just as much.
What you get
- 01
LLM integrations
Assistants, auto-reply and summarisation built on GPT and Claude models, grounded in your own data.
- 02
Speech & voice analytics
Speech-to-text, sentiment and voice analysis for calls—in real time or after the conversation.
- 03
Prediction engines
Price, demand and purchase-likelihood models that feed directly into operational decisions.
- 04
Computer vision
Image recognition and analysis pipelines for listings, catalogues and inspection data.
- 05
Workflow automation
Intent detection, routing and recommendations wired into CRM and messaging systems.
How an engagement runs
- 01
Discover
We start with the business problem, the constraints and the data and systems you already have.
- 02
Design
Architecture, scope and a delivery plan you can challenge before anything is built.
- 03
Build
Short iterations, working software early, and senior hands on the code.
- 04
Operate
Deployment, monitoring and handover, so the system keeps working after launch.
Questions we hear often
Do we need to train our own model?
Rarely. Most business cases are solved faster and more reliably by integrating existing models with your data and putting evaluation around them. Training from scratch makes sense for research or very specific constraints—we have done that too, with the open-source Turkish language model Toprak.
Can AI work in real time, during a conversation?
Yes. Our call-centre platform transcribes speech, analyses sentiment and produces recommendations for the agent while the call is still running. That only works when the architecture is designed around latency from day one.
How do you handle sensitive data?
We clarify at the start which data may leave your infrastructure, which models may process it and what has to be logged. Where personal data is involved, the design follows the rules that apply to you, such as GDPR or KVKK.
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