Custom Apps Built on
GPT-4, Claude & Open-Source LLMs

The best model depends on your use case, budget, and data privacy requirements. GPT-4o excels at general reasoning and code; Claude 3.5 Sonnet is outstanding for long document analysis and nuanced writing; Gemini Pro is strong for multimodal and Google Workspace integration. We assess your needs and recommend the right fit or run a comparison.

We implement Retrieval-Augmented Generation (RAG) which grounds every answer in your actual documents and data. The LLM is instructed to only answer from retrieved context and to say 'I don't know' when information isn't available. This dramatically reduces hallucination in enterprise use cases.

Yes. We integrate LLM features into existing products via API no need to rebuild your platform. We add AI layers on top of your current tech stack using REST APIs, webhooks, or direct database connections.

We can deploy fully private LLMs (Llama 3, Mistral, Qwen) on your own AWS, Azure, or on-premise servers. Your data never leaves your infrastructure. This is particularly common for legal, healthcare, and financial clients.

A basic prototype can be ready in 1-2 weeks. A production-ready app with authentication, database integration, and a polished UI typically takes 4-8 weeks depending on complexity. We always build a working prototype first so you can validate before full investment.

Usually not. Most business applications are best served by RAG (using your existing documents) rather than fine tuning, which requires significant labelled data. Fine tuning is only recommended for specific style or format requirements where RAG alone isn't sufficient.