Free
$0Free plan available.
Kastro is an AI-first customer support solution designed for startups and indie hackers. It uses ChatGPT to create an AI support agent that streamlines customer support, reduces expenses, and boosts revenue with 24/7 AI responses. Kastro offers an all-in-one customer support solution where AI and people work better together, focusing on handling repetitive questions and suggesting relevant articles from a help center.
Train your AI support operator by connecting it to your existing knowledge base. Kastro will then manage repetitive inquiries, recommend relevant articles, and organize conversations, allowing your team to prioritize complex issues. Additionally, you can utilize the AI-enhanced workspace to summarize conversations, expand replies, and rephrase responses.
Yes, we provide a free plan that allows you to explore Kastro's features before committing. You can learn more on our pricing page.
Each reply sent by Kastro up to 1,500 tokens counts as one message. Messages sent by your human operators to customers are not counted.
If your message credits are exhausted, customer inquiries will be automatically forwarded to an operator, and Kastro will stop responding.
We use gpt-3.5-turbo as the default for our chatbots, though GPT-4 is also available.
Yes, you can use GPT-4, though it consumes four times the message credits compared to the standard model.
Yes, Kastro supports websites in languages other than English.
If you require a custom integration or feature, you can join as an Elite customer, and we will work with you to develop a custom pricing plan tailored to your needs.
Free plan available.
Use these comparison pages to understand the trade-offs between the models most relevant to Kastro.
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.
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