• Low-Code Workflow Builder — drag-and-drop canvas to visually compose multi-step agentic pipelines with LLM nodes, conditional logic, Python code blocks, RAG retrievals, and tool integrations; no deep ML engineering required.
• Multi-Model LLM Support — integrates natively with OpenAI, Anthropic Claude, Google Gemini, Meta Llama 2, Hugging Face, and Replicate; switch or combine models within a single workflow without rewriting logic.
• RAG Knowledge Bases — ingest PDFs, documents, and company data sources into vector databases in minutes; agents retrieve and cite your proprietary data rather than relying on generic LLM knowledge.
• Two-Click LLM Fine-Tuning — fine-tune open-source LLMs on your own datasets directly within the platform; models you build become your owned assets, not rented API endpoints.
• Guardrails & Output Validation — applies pre-built and custom validators to every LLM response to detect hallucinations, sensitive data exposure, PII leaks, and format violations before output reaches users.
• Observability & Evaluations — logs all agent interactions, tracks key performance metrics, runs large-scale LLM quality evaluations, and provides real-time debugging views so engineering teams can monitor production behavior precisely.
• On-Premise & VPC Deployment — runs entirely within your own corporate infrastructure, VPC, AWS, IBM Cloud, or IBM watsonx; satisfies strict regulatory requirements in finance, healthcare, and the public sector.
• Multi-Agent Orchestration — design and deploy networks of specialized LLM agents that collaborate on complex tasks, share context, and connect to internal APIs — all managed from a single visual interface.