AI development: custom AI assistants and LLM integration
We adapt Gemini, OpenAI and Llama models to work with your business data. Intelligent systems that know your company and, built on a RAG architecture, ground their answers in your own sources.
AI applications in business
We customise general-purpose models and integrate them into your organisation's specific business processes.
AI is not a product in itself but a layer that makes existing products smarter. We integrate our solutions with your existing web platforms or mobile apps and run them on modern cloud infrastructure for scale and performance.
Natural Language Processing (NLP)
Assistant systems that automatically classify and summarise customer messages and draft replies.
Computer Vision
Quality control on the production line, camera-based counting and image-based process automation.
Predictive Analytics
ML models that produce sales, demand and operational forecasts from your historical data.
RAG Architecture and Fine-Tuning
AI systems enriched with your company's data that minimise the hallucination risk of general-purpose models.
- Data AnalysisYour company's documents, records and processes are analysed.
- Model SelectionThe right LLM (GPT, Gemini, Llama, Claude) and embedding model are chosen for your needs.
- RAG Set-upThe vector database, retriever and generator pipeline are set up.
- IntegrationThe system is integrated into your web, mobile or internal tools via API.
How does your AI project progress?
From pilot scope to production, in measurable steps.
Use Case Definition
Identifying where AI will create the most value in your business processes.
Data & Architecture
Data analysis, model selection and the RAG vs. fine-tuning decision.
PoC & Validation
A 2–4 week pilot, real-user testing and metrics analysis.
Production & Scale
Production deployment, monitoring and continuous improvement.
Frequently asked questions about AI development
For most enterprise use cases RAG (Retrieval-Augmented Generation) is sufficient and cost-effective. We store your company documents in a vector database and retrieve the relevant passages at query time. Fine-tuning is more expensive and is only preferred when a specific tone of voice or domain language is required.
Claude, GPT-4/5, Gemini, Llama 3 and Mistral — we choose based on the use case. For cost-sensitive projects we deploy Llama or Mistral locally; for work that demands the highest quality we prefer the Claude or GPT APIs.
The choice is yours. For KVKK (Türkiye's Personal Data Protection Law) and data residency, we can host Llama / Mistral on your own servers and run without any external API calls. If a cloud LLM is preferred for performance and cost, we use commercial APIs (Azure OpenAI, AWS Bedrock) that ensure customer data is not used for model training.
PoC (proof of concept): 2–4 weeks. Production release: 8–16 weeks. For AI projects to deliver real value, an iterative approach and real-user feedback are essential.
With RAG we give the model your company's data, and citing sources is mandatory. We set up evaluation pipelines that automatically cross-check model answers for accuracy. In high-risk domains (healthcare, finance) we add a human-in-the-loop approval layer.
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Let's integrate AI into your business
From pilot project to enterprise roll-out — we support your AI journey at every scale.