> For the complete documentation index, see [llms.txt](https://docs.helvia.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.helvia.ai/resources/faqs.md).

# FAQs

Quick answers to the questions we hear most, from what Helvia.ai is to what to check when something is not working. 

{% hint style="info" %}
**Cannot find your answer?** See the [Support page](/resources/support.md) for ways to get help.
{% endhint %}

### About Helvia.ai

<details>

<summary><strong>What is Helvia.ai?</strong></summary>

Helvia.ai is an enterprise AI company redefining how businesses communicate and operate through agentic AI. Its proprietary platform\
delivers intelligent AI agents tailored to your organization, automating complex workflows and turning real-time insights into action.

</details>

<details>

<summary><strong>What is the Helvia.ai Agent Platform?</strong></summary>

The platform is an all-in-one, no-code environment for building, deploying, and monitoring AI agents that automate tasks and handle conversations with your users across channels.

</details>

<details>

<summary><strong>Who is the Helvia.ai Agent Platform for?</strong></summary>

Any team that wants to put an AI agent in front of its users, from customer support to internal operations. The visual builder works for both non-technical and technical users, so you do not need a developer to get started.

</details>

<details>

<summary><strong>What can I use the Helvia.ai Agent Platform for?</strong></summary>

Build an agent, ground it in your own knowledge, and deploy it to channels like your website, Microsoft Teams, or WhatsApp. You then monitor how it performs in Observatory, test it before real users see it, and hand conversations to your support team through LiveChat when a human is needed.

</details>

### Getting Started

<details>

<summary><strong>Do I need to know how to code to build an agent?</strong></summary>

No. You build agents visually by placing and connecting nodes on the canvas. Coding experience is an option for advanced cases, not a requirement to get started.

</details>

<details>

<summary><strong>How long does it take to get an agent live?</strong></summary>

A simple agent can go live the same day you start. How long a more advanced one takes depends on the knowledge, integrations, and testing it needs.

</details>

### Capabilities and Channels

<details>

<summary><strong>Where can users talk to my agent?</strong></summary>

Your agent can live on your website through the Webchat widget, in chat apps like Microsoft Teams, or connect through the API. The same agent can work in several places at once, each with its own settings.

</details>

<details>

<summary><strong>Can I ground an agent in my own content and data?</strong></summary>

Yes. Connect a knowledge base built from your files, written content, or external sources, and the agent retrieves from it to answer. This keeps responses based on your information rather than generic knowledge.

</details>

<details>

<summary><strong>Can I use my own LLM, or am I locked to one provider?</strong></summary>

You bring your own provider and keys. The platform supports the major LLM providers through integrations, and you can set a primary model with automatic fallbacks so replies keep working even if one provider has an outage.

</details>

<details>

<summary><strong>Can a single agent handle more than one language?</strong></summary>

Yes. An agent can detect the language a user writes in and respond accordingly, so one agent can serve users across 20+ languages.

</details>

### Accuracy and Trust

<details>

<summary><strong>How does Helvia.ai stop an agent from making things up?</strong></summary>

Answers are grounded in the content you connect, using retrieval-augmented generation (RAG) so the agent draws from your knowledge base instead of guessing. You can also set guardrails that keep responses on-topic and within policy.

</details>

<details>

<summary><strong>Can I see why an agent gave a particular answer?</strong></summary>

Yes. Every conversation is recorded as a session in Observatory, and you can open any reply to trace the exact workflow steps and data behind it.

</details>

<details>

<summary><strong>How do I test an agent before real users see it?</strong></summary>

You can run automated tests where a synthetic user talks to the agent and an evaluator scores the result against your criteria. This lets you catch problems and prevent regressions before you deploy.

</details>

### Human Handoff

<details>

<summary><strong>What happens when the AI cannot handle a request?</strong></summary>

You decide when a handoff happens by building it into your workflow, so the agent passes the conversation to a human through LiveChat at the points you choose. The user stays in the same channel, and your team sees the full history, so nobody has to start over.

</details>

<details>

<summary><strong>Can my support team join a live conversation?</strong></summary>

Yes. With LiveChat, your team manages handed-off conversations from a shared inbox, with conversation history, internal notes, and canned responses to reply faster.

</details>

### Security and Data

<details>

<summary><strong>Where is my data stored, and how long is it kept?</strong></summary>

Your data lives inside your Workspace, isolated from other Workspaces. You control how long conversation data is retained in the Workspace settings.

</details>

<details>

<summary><strong>Does Helvia.ai use my data to train its AI models?</strong></summary>

No. Helvia.ai does not use customer data to train its AI models. Your conversations, configurations, and content remain yours.

</details>

<details>

<summary><strong>How do I control who on my team can access what?</strong></summary>

Access is managed through role-based access control (RBAC), with roles at the Workspace and agent level. You can give someone broad access across the Workspace or limit them to specific agents.

</details>

<details>

<summary><strong>Can I track who changed what in my Workspace?</strong></summary>

Yes. Every administrative action, from role changes to agent edits and file uploads, is recorded automatically in an audit log. Admins can filter it by user, category, or date, and export the results to CSV for compliance reviews.

</details>

### Troubleshooting

<details>

<summary><strong>My agent is not responding to messages</strong></summary>

A common cause is the agent's LLM integration, so check that its credentials are valid, the provider is reachable and the plugin is enabled. You can also review the conversation in Observatory, which surfaces the errors that stopped the agent from replying.

</details>

<details>

<summary><strong>My Webchat widget is not loading on my site</strong></summary>

Check that the embed snippet is installed correctly and that your domain is listed in the deployment's allowed origins. A domain that is not permitted will block the widget from loading.

</details>

<details>

<summary><strong>My agent is not using my latest knowledge base content</strong></summary>

Changes to a knowledge base become available only after you update the agent's knowledge. Run the sync so the agent retrieves your newest content, since until then it answers from the previous version.

</details>

<details>

<summary><strong>My agent gives wrong or irrelevant answers</strong></summary>

Confirm a knowledge base is connected and that you have synced it after your latest changes. If answers are still off, open a session to see which content was retrieved, then refine or add content where there are gaps.

</details>

<details>

<summary><strong>A third-party channel or integration will not connect</strong></summary>

Check that the integration credentials are valid and that the integration is set up in Workspace before you activate the plugin that uses it. Expired, wrong or missing credentials are the most common cause.

</details>

<details>

<summary><strong>My agent replies in the wrong language</strong></summary>

The language an agent replies in is driven by the instructions it follows, so review those to make sure they account for the language the user is writing in. Enabling language detection also helps, so the agent can recognize the user's language and respond in it.

</details>

<details>

<summary><strong>My test keeps failing unexpectedly</strong></summary>

Open the test result to read the evaluator's reason for the verdict. A failure often means the success criteria are too strict, or the agent has changed since the test was written, so update whichever no longer matches your expectations.

</details>

### Still Stuck?&#x20;

Some questions need a person. The Support page shows you where to turn.

<a href="/pages/21r6MM6uyroIfX7xWHV5" class="button primary" data-icon="headset">Contact Support</a>


---

# Agent Instructions
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## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.helvia.ai/resources/faqs.md?ask=<question>&goal=<endgoal>
```

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`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

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Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.