LLM Integration Experts

It depends on your specific needs. GPT-4 excels at complex reasoning and code generation. Claude is preferred for long-context tasks and safer outputs. Gemini is strong for multimodal applications. We benchmark all relevant models against your actual use case before recommending one — and often use multiple models in combination for best results.

Data privacy is configurable at multiple levels. Options include enterprise API tiers with zero data retention, private cloud deployments (Azure OpenAI, AWS Bedrock), and fully on-premise open-source models like Llama 3. We help you choose the right architecture based on your compliance requirements (GDPR, HIPAA, SOC 2).

Retrieval Augmented Generation (RAG) connects an LLM to your own knowledge base — documentation, databases, PDFs — so it can answer questions accurately using your proprietary data rather than just its training data. You need RAG when you want the AI to have up-to-date, domain-specific knowledge without the cost and complexity of full fine-tuning.

Costs vary widely by model and usage. GPT-4 Turbo runs around $0.01–$0.03 per 1K tokens, while GPT-3.5 and Claude Haiku are 10–20x cheaper. For a typical customer support bot handling 10K queries/day, monthly costs typically range from $200–$2,000 depending on context length. We help you optimize architecture to minimize costs without sacrificing quality.

Yes. Fine-tuning is ideal when you need consistent tone, specialized domain vocabulary, or structured output formats. We support fine-tuning on OpenAI models (GPT-3.5, GPT-4o) and open-source models (Llama 3, Mistral). The process typically takes 2–4 weeks and includes data preparation, training, evaluation, and deployment to your infrastructure.