> ## 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.

# Semantic Scholar

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

Semantic Scholar is an AI-powered academic search engine that helps researchers discover and understand scientific literature.

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

## Where Semantic Scholar fits in a DialNexa workflow

Semantic Scholar 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 Semantic Scholar

* 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 Semantic Scholar workflows

<AccordionGroup>
  <Accordion title="Classify support reason after calls">
    For this workflow, DialNexa should send Semantic Scholar 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="Draft a CRM note or support reply">
    For this workflow, DialNexa should send Semantic Scholar 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="Extract appointment or address details">
    DialNexa should capture the preferred time, timezone, owner, promise made, and contact channel before updating Semantic Scholar. The receiving team should see exactly why the follow-up exists and what the caller expects next.
  </Accordion>

  <Accordion title="Summarize a long escalation">
    For this scenario, DialNexa should treat Semantic Scholar 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 Semantic Scholar 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 bulk papers">
    Use search bulk papers 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 get dataset release information">
    Use get dataset release information 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>
</AccordionGroup>

## Workflows that pair Semantic Scholar with other integrations

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

## Implementation notes

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

  <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>
</AccordionGroup>