The Custom ChatGPT Chatbot is a tool that allows users to create and embed a chatbot on their website. The chatbot is generated by scraping the website and creating a custom Machine Learning model based on its content.
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AINIRO is a no-code AI platform that enables businesses to build and deploy custom AI-powered chatbots, agents, and workflow automations by connecting large language models to their own data sources databases, websites, CRMs, APIs, and cloud storage through a visual configuration interface that requires no programming knowledge.
Users define what data the AI should have access to, what tasks it should be able to perform, and what rules should govern its behavior, and AINIRO handles the underlying infrastructure: retrieval, orchestration, API connections, and response generation.
The result is a fully functional AI application that reflects the business's specific knowledge and processes rather than generic AI capabilities.
The platform goes beyond simple chatbot functionality to support autonomous AI agents capable of taking multi-step actions querying a database, drafting a document, sending a notification, updating a record in response to user inputs or scheduled triggers.
This agentic capability positions AINIRO for use cases that extend well beyond customer-facing chat, including internal knowledge workers that help employees navigate complex information, automated reporting agents that pull and summarize data on a schedule, and process automation flows that connect previously siloed systems through an AI coordination layer.
AINIRO is available as a cloud-hosted service and as a self-hosted installation for organizations with strict data sovereignty requirements.
Its API-first architecture means that AI applications built on the platform can be embedded into existing websites, portals, and enterprise systems through standard integration patterns.
The platform includes monitoring tools that track AI response quality, usage patterns, and error rates, giving administrators the operational visibility needed to maintain reliable AI deployments in production environments.
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