Question 1

How does Mendable handle data privacy and security for my documentation content?

Accepted Answer

Mendable processes your documentation by creating vector embeddings, which are numerical representations of your text, not storing the raw text itself in a directly readable format for query processing. Your original documentation content remains on your servers. We adhere to industry-standard security protocols for data in transit and at rest, ensuring that your proprietary information is used solely for powering your specific AI agent and is not shared or used to train other models.

Question 2

Can I integrate Mendable with private documentation or internal wikis that require authentication?

Accepted Answer

Yes, Mendable supports integration with private documentation sources. For web-based content, you can often provide credentials or use API keys for authenticated crawling. For file-based content from internal wikis or knowledge bases, you can upload files directly through the web interface or use our API for programmatic ingestion, ensuring the content remains within your controlled environment during the embedding process.

Question 3

What is the pricing model for Mendable, and how does it scale with usage?

Accepted Answer

Mendable's pricing is typically usage-based, primarily factoring in the volume of documentation ingested (e.g., number of pages or tokens), the number of queries processed by the AI agent, and potentially advanced features like custom LLM integrations or dedicated support. We offer tiered plans designed to scale from small projects to large enterprise documentation needs, with clear metrics for cost predictability.

Question 4

How can I measure the effectiveness and ROI of deploying an AI agent on my documentation site?

Accepted Answer

You can measure effectiveness through several key metrics available in the Mendable analytics dashboard: 'Deflection Rate' (percentage of queries answered by the AI without human intervention), 'Answer Accuracy' (user feedback on answer quality), 'Query Volume' (identifying common pain points), and 'Average Time to Resolution' for user queries. Comparing these metrics against pre-deployment support ticket volumes and user satisfaction scores provides a clear ROI.

Question 5

Is it possible to fine-tune the underlying LLM or use my own custom model with Mendable?

Accepted Answer

Mendable offers options for advanced users to influence the LLM's behavior. While direct fine-tuning of the base LLM might be an enterprise-tier feature, you can extensively customize the agent's responses through prompt engineering within the web interface. For specific use cases, Mendable may support integration with custom models or allow you to bring your own LLM via API, providing flexibility for highly specialized requirements.

Mendable Website Full Guide (2026)

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How to Use Mendable

Configure Knowledge Base Source

Mendable Alternatives

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About Mendable

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