Qualetics

Teams that want to use AI today often end up stitching together separate tools for chat, workflow orchestration, model access, and app hosting, then rebuilding the same integrations again for each new use case. Experimenting with AI on ad hoc tasks is easy, but turning a proven prompt or workflow into something the rest of the organization can run reliably, with access controls, an audit trail, and a real user interface, is a separate and harder problem. The page frames this as needing to avoid 'stitching five vendors together' to go from a one-off chat answer to a durable production application.

Nautilus lets users describe a task in chat and either get an answer directly or have that conversation turned into a hosted app, complete with forms, a scoring or review step, role-aware access, and an audit trail, as shown in the sample 'customer risk desk' conversation. Under the hood, workflows are built as 'Data Machines': visual, multi-step pipelines with triggers, tool calls, reasoning steps, and human-in-the-loop approvals that can be scheduled or triggered by events. A model catalog lets builders mix language, vision, speech, and embedding models from providers like OpenAI, Anthropic, Google, Azure, AWS, Hugging Face, Cohere, and Mistral, routing by quality, cost, or latency within a single workflow. Connectors link the platform to CRMs, warehouses, files, APIs, and SaaS tools, and the platform can be deployed on Qualetics's cloud, AWS, Azure, GCP, private cloud, or on-premise.