We Build Custom AI Systems Tailored to Your Business

It is more than just setting up a model; it is a holistic package of strategy and implementation covering automation potential, data readiness, team workflows, and output measurement.

The starting point is never the technology; it is the business problem. The fastest impact usually comes from automating repetitive operational tasks or resolving CRM inefficiencies within the sales pipeline. In the right context, a chatbot can serve as a powerful lever for both sales and support.

Many projects fail or remain in the "pilot phase" because they start with an immediate reflex to "build a model" without addressing data, ownership, security, or process gaps. A readiness analysis prevents wasted investment by clarifying data sources, processes, risks, and success metrics.

Data security, the risk of hallucinations (false/made-up responses), authorization and access control, compliance (GDPR/KVKK), and operational sustainability are the most common concerns. Therefore, governance and monitoring design are as integral to our projects as the model itself.

For most organizations, a well-designed architecture—comprising the right data layer, governance, and use case—delivers powerful results using off-the-shelf models. The need for a custom model typically arises only when there are high requirements for data privacy, domain-specific expertise, or extreme performance.

A classic chatbot typically handles limited, pre-defined scenarios. A Corporate AI Assistant is a sophisticated system that is securely fed by your own documents, integrated into your business processes, managed through authorization and logging, and produces measurable business outcomes.

RAG becomes critical when corporate knowledge is fragmented and accurate answers require referencing specific documents, policies, procedures, or product data. This ensures responses are grounded in up-to-date sources and can be supported by citations.

AI provides significant gains in areas like lead scoring, intent analysis, meeting summaries, objection handling patterns, and accelerating proposals. In a well-built setup, the sales team spends less time on "dead leads" and focuses on high-probability opportunities.

SEO focuses on visibility within search engine results. LLMO involves optimizing content and structure so that AI systems like ChatGPT, Gemini, and Perplexity can better understand, accurately summarize, and reference a brand in the right context.

In addition to classic metrics (organic traffic, conversion, visibility), we track brand/product mention rates in AI responses, accurate contextual citations, the "answerability" performance of content, and coverage within specific query clusters.

It goes beyond simple translation to cover messaging, terminology, user expectations, interface adaptation, formatting, and quality assurance. We manage components like Translation + DTP + Voiceover + Software Testing + Turkish LLM Testing under a single unified strategy.

Test plans are built around criteria such as semantic accuracy in Turkish, tone, terminological consistency, hallucination tendency, sensitive content safety, and source grounding. The goal is to make the Turkish user experience measurable and accelerate model/workflow improvements.

This varies by scope; however, with a well-chosen use case, measurable first outputs can be achieved within 2–6 weeks. Larger transformations typically mature over 3–6 months through a roadmap and phased implementation.

We usually begin with a brief discovery and goal-setting phase, followed by roadmap development and implementation. We work alongside in-house teams to ensure the systems we deploy are fully adopted and sustainable in the long run.

If a CRM does not reflect the actual sales process, it turns into a mere record-keeping tool. When you establish roles, processes, metrics, and data discipline together, the CRM evolves into a governance system that actively drives sales performance.