Kernshell provides GenAI strategy and use case assessment, custom LLM development and fine-tuning (GPT-4, Claude, LLaMA, Gemini), Retrieval-Augmented Generation (RAG) system development, AI copilot and virtual assistant development, multi-modal AI, prompt engineering, GenAI integration with CRM and ERP systems, and AI security and governance frameworks for regulated industries.

Kernshell selects LLMs based on client requirements. Models used include GPT-4o via Azure OpenAI Service, Anthropic Claude 3.5 via AWS Bedrock (preferred for long-context document analysis such as LexOps AI contract review), Meta LLaMA 3 deployed on-premises for maximum data control, Google Gemini 1.5 via Vertex AI for multimodal applications, and Mistral for cost-sensitive workloads.

Retrieval-Augmented Generation (RAG) connects an LLM to your organisation’s specific knowledge base — retrieving relevant documents before generating answers. This grounds responses in your actual company data, dramatically reducing hallucination risk and enabling source citations. Kernshell implements RAG for knowledge systems, contract review (LexOps AI), and clinical screening (ScreenX Health).

Yes. Kernshell builds GenAI for manufacturing including ETQ Reliance AI integration (surfacing quality insights from production data), predictive maintenance knowledge systems using RAG over equipment manuals and sensor data, and automated compliance report generation for ISO and FDA-regulated environments. Manufacturing clients include Jabil and Hitachi Energy.

Use RAG when your knowledge base changes frequently, you need source citations, or you have large diverse document sets. Use fine-tuning when you need consistent domain-specific writing style, vocabulary, or specialised reasoning patterns. Best practice: combine both — fine-tune for domain expertise, RAG for current knowledge access. They are complementary, not competing approaches