> ## Documentation Index
> Fetch the complete documentation index at: https://dialnexa.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Elevenreader

> Connect DialNexa calls to Elevenreader for model run, extraction result, generated answer, transcript, classification, media analysis, or tool call workflows.

ElevenReader is an AI-powered text-to-speech application by ElevenLabs that converts written content into natural-sounding audio.

<Note>
  Use Elevenreader with DialNexa when the call needs classification, extraction, generation, retrieval, summarization, visual understanding, or another AI-assisted step.
</Note>

## Where Elevenreader fits in a DialNexa workflow

Elevenreader should receive DialNexa output when the conversation affects a model run, extraction result, generated answer, transcript, classification, media analysis, or tool call. The handoff should explain what the caller asked for, what DialNexa learned, which record or object is affected, and who owns the next step.

<CardGroup cols={2}>
  <Card title="Classify call intent" icon="check-circle">
    Turn messy speech into intent, category, urgency, language, sentiment, and confidence fields.
  </Card>

  <Card title="Generate useful drafts" icon="check-circle">
    Create replies, notes, briefs, summaries, or tasks from the exact call outcome and approved context.
  </Card>

  <Card title="Extract structured details" icon="check-circle">
    Pull names, dates, products, addresses, invoice numbers, image labels, or requested actions into fields.
  </Card>

  <Card title="Keep humans in control" icon="check-circle">
    Send low-confidence, sensitive, or account-changing outputs to review instead of acting silently.
  </Card>
</CardGroup>

## What DialNexa should capture for Elevenreader

* Transcript, summary, language, speaker role, media or file link, intent, confidence, and sensitive-data flag
* Knowledge source, prompt version, model ID, tool call, allowed action, and fallback path
* Generated draft, extracted fields, recommended next step, review reason, and owner
* Transcript link, recording link, DialNexa call ID, CRM link, ticket link, and output record URL
* Risk flags for hallucination, missing source, private data, low confidence, or restricted action

## High-value Elevenreader workflows

<AccordionGroup>
  <Accordion title="Answer from approved knowledge">
    For this workflow, DialNexa should send Elevenreader a concise, action-ready handoff: matched caller, affected record, reason for the update, urgency, owner, next step, and links to call evidence.
  </Accordion>

  <Accordion title="Flag low-confidence AI output">
    For this workflow, DialNexa should send Elevenreader a concise, action-ready handoff: matched caller, affected record, reason for the update, urgency, owner, next step, and links to call evidence.
  </Accordion>

  <Accordion title="Analyze call sentiment and urgency">
    For this workflow, DialNexa should send Elevenreader a concise, action-ready handoff: matched caller, affected record, reason for the update, urgency, owner, next step, and links to call evidence.
  </Accordion>

  <Accordion title="Generate a follow-up brief for a human">
    DialNexa should capture the preferred time, timezone, owner, promise made, and contact channel before updating Elevenreader. The receiving team should see exactly why the follow-up exists and what the caller expects next.
  </Accordion>

  <Accordion title="Classify support reason after calls">
    For this workflow, DialNexa should send Elevenreader a concise, action-ready handoff: matched caller, affected record, reason for the update, urgency, owner, next step, and links to call evidence.
  </Accordion>

  <Accordion title="Use create file document">
    Use create file document only when DialNexa has a matched caller, a clear destination object, and enough call context to justify opening a new AI workflow result. If the caller is unclear, route to review instead of creating noise.
  </Accordion>

  <Accordion title="Use post agent avatar">
    Use post agent avatar when the call outcome maps clearly to that operation and the required fields, owner, review state, and evidence links are known.
  </Accordion>
</AccordionGroup>

## Workflows that pair Elevenreader with other integrations

* [Elevenreader](/integrations/elevenreader) + [Notion](/integrations/notion): Notion for knowledge updates.
* [Elevenreader](/integrations/elevenreader) + [Google Sheets](/integrations/googlesheets): Google Sheets for extraction QA.
* [Elevenreader](/integrations/elevenreader) + [Intercom](/integrations/intercom): Intercom for customer conversation context.
* [Elevenreader](/integrations/elevenreader) + [Google Drive](/integrations/googledrive): Google Drive for source files and recordings.
* [Elevenreader](/integrations/elevenreader) + [HubSpot](/integrations/hubspot): HubSpot for CRM notes and tasks.
* [Elevenreader](/integrations/elevenreader) + [Zendesk](/integrations/zendesk): Zendesk for support replies and ticket summaries.
* [Elevenreader](/integrations/elevenreader) + [Slack](/integrations/slack): Slack for review of risky outputs.
* [Elevenreader](/integrations/elevenreader) + [Google Docs](/integrations/googledocs): Google Docs for long-form call briefs.

## Implementation notes

* Use the DialNexa call ID as the idempotency key before running Elevenreader actions.
* Write a short operational summary into Elevenreader and link to the full transcript or recording for audit.
* Map required fields before launch: destination object, owner, status, urgency, next step, and record URL.
* Create review paths for low-confidence matches, sensitive requests, high-value customers, and actions that change money, access, legal terms, or customer commitments.

## FAQs

<AccordionGroup>
  <Accordion title="Should AI outputs act without review?">
    Only for low-risk workflows with clear confidence thresholds. Account changes, money, access, legal, and sensitive support cases need review.
  </Accordion>

  <Accordion title="What should be logged for AI decisions?">
    Prompt version, source context, model or workflow ID, confidence, output, call ID, and reviewer when applicable.
  </Accordion>

  <Accordion title="How do we reduce hallucinations?">
    Use approved knowledge sources, cite source records, set fallback behavior, and route low-confidence answers to humans.
  </Accordion>

  <Accordion title="Can DialNexa write follow-up messages with AI?">
    Yes, but drafts should use the actual call outcome, approved tone, and customer context. Sensitive messages should be reviewed.
  </Accordion>

  <Accordion title="What data should not be sent to AI tools?">
    Secrets, payment data, private HR or health details, and anything your policy forbids unless the tool and workflow are approved.
  </Accordion>

  <Accordion title="How should extraction errors be handled?">
    Store confidence and route missing or conflicting fields to review rather than silently updating downstream systems.
  </Accordion>
</AccordionGroup>