Free
$0Free plan available.
Humley Studio is a powerful SaaS platform that empowers businesses and users to autonomously build, deploy, and manage cutting-edge AI-powered Assistants, delivering exceptional customer experiences while optimising valuable time and resources. It is a no-code platform for crafting self-serve experiences and managing AI outputs, making Conversational AI accessible and safe for all. Humley’s platform helps you to deliver operational efficiencies and exceptional customer experiences, catering to all needs – from simple chatbots to complex multi-channel integrated Assistants.
Users can create and run customer experiences in a few simple steps: create Q&As using generative AI, test and refine your experiences, engage with customers, and optimize and enhance your overall value.
You can build and launch your first conversational assistant in under an hour using the no-code Studio platform.
Building is free, allowing you to achieve immediate results.
Humley Studio securely integrates the latest AI models, including OpenAI LLMs.
Studio provides a robust toolkit that gives you control over AI outputs, ensuring secure and reliable conversational experiences for both you and your customers.
Free plan available.
Use these comparison pages to understand the trade-offs between the models most relevant to Humley.
Compare GPT 5.4 and GPT 5.4 Pro across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare GPT 5.5 and GPT 5.4 across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare GPT 5.5 and Claude 4.6 Opus across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare GPT 5.5 and Claude 4.6 Sonnet across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
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