Use Test app with DialNexa when the call needs classification, extraction, generation, retrieval, summarization, visual understanding, or another AI-assisted step.
Where Test app fits in a DialNexa workflow
Test app 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.Classify call intent
Turn messy speech into intent, category, urgency, language, sentiment, and confidence fields.
Generate useful drafts
Create replies, notes, briefs, summaries, or tasks from the exact call outcome and approved context.
Extract structured details
Pull names, dates, products, addresses, invoice numbers, image labels, or requested actions into fields.
Keep humans in control
Send low-confidence, sensitive, or account-changing outputs to review instead of acting silently.
What DialNexa should capture for Test app
- 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 Test app workflows
Answer from approved knowledge
Answer from approved knowledge
For this workflow, DialNexa should send Test app a concise, action-ready handoff: matched caller, affected record, reason for the update, urgency, owner, next step, and links to call evidence.
Flag low-confidence AI output
Flag low-confidence AI output
For this workflow, DialNexa should send Test app a concise, action-ready handoff: matched caller, affected record, reason for the update, urgency, owner, next step, and links to call evidence.
Analyze call sentiment and urgency
Analyze call sentiment and urgency
For this workflow, DialNexa should send Test app a concise, action-ready handoff: matched caller, affected record, reason for the update, urgency, owner, next step, and links to call evidence.
Generate a follow-up brief for a human
Generate a follow-up brief for a human
DialNexa should capture the preferred time, timezone, owner, promise made, and contact channel before updating Test app. The receiving team should see exactly why the follow-up exists and what the caller expects next.
Classify support reason after calls
Classify support reason after calls
For this workflow, DialNexa should send Test app a concise, action-ready handoff: matched caller, affected record, reason for the update, urgency, owner, next step, and links to call evidence.
Use action one
Use action one
Use action one when the call outcome maps clearly to that operation and the required fields, owner, review state, and evidence links are known.
Use action two
Use action two
Use action two when the call outcome maps clearly to that operation and the required fields, owner, review state, and evidence links are known.
Workflows that pair Test app with other integrations
- Test app + Notion: Notion for knowledge updates.
- Test app + Google Sheets: Google Sheets for extraction QA.
- Test app + Intercom: Intercom for customer conversation context.
- Test app + Google Drive: Google Drive for source files and recordings.
- Test app + HubSpot: HubSpot for CRM notes and tasks.
- Test app + Zendesk: Zendesk for support replies and ticket summaries.
- Test app + Slack: Slack for review of risky outputs.
Implementation notes
- Use the DialNexa call ID as the idempotency key before running Test app actions.
- Write a short operational summary into Test app 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
What data should not be sent to AI tools?
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.
How should extraction errors be handled?
How should extraction errors be handled?
Store confidence and route missing or conflicting fields to review rather than silently updating downstream systems.
When should an AI answer fall back to a human?
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.
What should be measured over time?
What should be measured over time?
Intent accuracy, extraction accuracy, fallback rate, review overrides, bad answers, and customer outcomes after AI-generated follow-up.
Should AI outputs act without review?
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.
What should be logged for AI decisions?
What should be logged for AI decisions?
Prompt version, source context, model or workflow ID, confidence, output, call ID, and reviewer when applicable.