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KNOWLEDGE BASE

Company-Specific AI Integration: LLM API or Custom Model Training?

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Table of Contents — English

Three AI Integration Approaches

LLM API integration (OpenAI GPT, Anthropic Claude, Google Gemini): API calls through a ready-made model. Fastest deployment, lowest starting cost. Disadvantage: no access to company-specific data. RAG (Retrieval Augmented Generation): The ready-made LLM model is enriched with company data. Before answering a query, the model looks at the company's document base. The most balanced approach. Custom model training (Fine-tuning): Re-training the model with company-specific data. Highest customization but highest cost and duration.

Which Use Case Requires Which Approach?

  • Customer support chatbot (FAQ answering): LLM API + RAG. Sufficient customization, fast deployment.
  • Internal document search assistant: RAG architecture. Generating correct answers from company documents.
  • Sales representative assistant (CRM-integrated): RAG + CRM API integration.
  • Industry-specific language model (legal, medical, financial): Fine-tuning. Ready-made models are insufficient in specialized fields.
  • Code generator or review assistant: GitHub Copilot-like structure, fine-tuning on the company's own codebase.

KVKK and Data Privacy: The Overlooked Dimension of AI Integration

KVKK and GDPR restrictions come into play when sending customer or employee data to AI models. Enterprise plans of providers like OpenAI and Anthropic guarantee that data won't be used to train their models. However, for critical data, on-premise models should be preferred or anonymization should be applied. PROJX Digital performs data privacy assessment upfront on all AI projects.

Technical Details of RAG Architecture

In RAG architecture, these components work together: document ingestion and chunking, an embedding model (converts company text to vectors), a vector database (Pinecone, Weaviate, or pgvector), a retrieval layer (finds the most relevant text for a query), and an LLM (generates a response using the retrieved text). PROJX Digital has experience deploying this stack with company-specific data.

Frequently Asked Questions

In Enterprise plans, the contract guarantees that data won't be used for training. On-premise alternatives are also available for critical data.

A basic RAG system can be deployed in 4-8 weeks. Projects with complex document bases and multilingual support can extend to 3-4 months.

REST API integration is the fastest way to add an AI feature without changing the existing software architecture. PROJX Digital designs this integration architecture as standard on all AI projects.

A mechanism to escalate to a human representative when the chatbot is uncertain should be designed. A confidence score threshold automatically triggers this escalation.

LLM API-based simple integration: 50,000-150,000 TRY, 4-8 weeks. Enterprise system with RAG architecture: 150,000-500,000 TRY, 3-5 months. Custom model with fine-tuning: 500,000+ TRY, 4-8 months.

Key Takeaways

  • RAG architecture offers the best cost/customization balance by enriching a ready-made LLM model with company-specific data.
  • KVKK and GDPR compliance must be designed from the start in every AI project involving customer data.
  • The on-premise model option is a critical alternative for enterprise projects requiring data sovereignty.
  • PROJX Digital's AI integration experience covers RAG architecture, LLM API, and fine-tuning projects.
Content Owner: Projx Digital
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