Generative AI & LLM Integration

We integrate large language models — OpenAI's GPT-4/GPT-4o, Anthropic's Claude, Google Gemini, or open-source models like Llama and Mistral for cost- or data-sensitivity-driven cases — into products, internal tools, and workflows. Most engagements are built around retrieval-augmented generation (RAG): your documents, knowledge base, or database content is embedded into a vector store like Pinecone, Weaviate, or pgvector, so the model answers grounded in your actual data rather than its general training knowledge.

Where RAG isn't the right fit, we evaluate fine-tuning against a base model versus prompt engineering with few-shot examples, based on how specialized the task is and how much labeled data exists. Every integration includes structured prompt testing and hallucination/error-rate evaluation before launch, plus provider selection based on your data sensitivity, latency needs, and cost per call — not a default vendor choice.

• RAG pipelines using vector databases (Pinecone, Weaviate, pgvector)
• Integration with OpenAI, Anthropic, Google Gemini, or open-source LLM APIs
• Fine-tuning vs. prompt-engineering evaluation per use case
• Structured hallucination and output-quality testing before launch
• Embedding generation and knowledge-base ingestion pipelines