> ## 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.

# Scrapegraph Ai

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

ScrapeGraphAI is an AI-powered web scraping API that enables developers to extract structured data from any website using natural language prompts. Website [https://scrapegraphai.com](https://scrapegraphai.com).

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

## Where Scrapegraph Ai fits in a DialNexa workflow

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

  <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>
</CardGroup>

## What DialNexa should capture for Scrapegraph Ai

* 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 Scrapegraph Ai workflows

<AccordionGroup>
  <Accordion title="Summarize a long escalation">
    For this scenario, DialNexa should treat Scrapegraph Ai as an escalation destination. Send the impact, urgency, affected customer or object, owner, and transcript link so the right team can act before the issue gets colder.
  </Accordion>

  <Accordion title="Answer from approved knowledge">
    For this workflow, DialNexa should send Scrapegraph Ai 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 Scrapegraph Ai 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 Scrapegraph Ai 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 Scrapegraph Ai. 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 Scrapegraph Ai 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 search scraper">
    Use search scraper before answering, routing, or creating follow-up. DialNexa should verify the lookup result against the caller and send low-confidence matches to a human queue.
  </Accordion>

  <Accordion title="Use generate schema">
    Use generate schema 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>
</AccordionGroup>

## Workflows that pair Scrapegraph Ai with other integrations

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

## Implementation notes

* Use the DialNexa call ID as the idempotency key before running Scrapegraph Ai actions.
* Write a short operational summary into Scrapegraph Ai 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="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>

  <Accordion title="When should an AI answer fall back to a human?">
    When source context is missing, confidence is low, the caller disputes the answer, or the next step changes money, access, or legal commitments.
  </Accordion>

  <Accordion title="What should be measured over time?">
    Intent accuracy, extraction accuracy, fallback rate, review overrides, bad answers, and customer outcomes after AI-generated follow-up.
  </Accordion>

  <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>
</AccordionGroup>