5 More AI Voice Agent Use Cases for You and Your Business Right Now

Find use cases one through five in this article

An electrician, standing on a ladder with one hand on a breaker box, says:

“Agent, show wiring diagram step 4… The agent opens the correct schematic, reads a brief safety note, and waits. The electrician interrupts: “Scroll right… zoom 150%… okay, mark task complete.”

The voice agent overlays the correct schematic on a tablet/AR visor, reads the next safety step aloud, and logs the work-order note when the electrician confirms completion.

• Lower Mean-Time-to-Repair (MTTR): instant access to SOPs and parts lists; no pulling gloves off to scroll.
• Higher first-time-fix rate: guided checklists reduce re-work and truck rolls.
• Improved safety: eyes stay on the equipment; hands stay on tools.
• Automatic documentation: speech-to-log eliminates after-job admin time.

• Low-latency guidance: sub-second “next step” feedback keeps flow uninterrupted.
• Barge-in and turn-taking so tech can interrupt with updates (“Found burnt wire; skip step 3”).
• Domain-specific recognition (part numbers, fault codes) via Nova-3 keyterm prompting.
• Function calls to fetchSOP, lookupPart, logWork against the asset database/CMMS.
• Offline/spotty-network resilience with cached TTS prompts and local fallback.

1) Prompt with SOPs and Error Codes

Seed the system prompt with: (a) SOP index (by model/serial), (b) step granularity (≤10s spoken), (c) hazard callouts before high-risk actions, (d) error-code translations (“E42 → check neutral line continuity”), and (e) note-taking policy (“summarize steps and parts before logWork”):

You are a field-service voice assistant.
• Use fetchSOP(assetId, step) to retrieve instructions.
• Use lookupPart(partNo) to read stock & location.
• After "mark task complete", call logWork(jobId, notes).
Speak <8-second responses; confirm critical actions.

2) Tools: fetchSOP, lookupPart, logWork

• fetchSOP(assetId, stepText) → {instruction, diagramUrl}
• lookupPart(pn|name) → {pn, stock, bin, compatible}
• logWork(workOrderId, summary, minutes, parts[]) → {ok, recordId}

Return compact JSON so the agent can speak succinctly and update the CMMS.

3) Speech stack + offline cache

• Listen: nova-3 with jargon injection with kyeterm prompting ("vocabulary":["MCB","DIN-rail","AWG"]).
• Think: Deepgram-managed OpenAI or BYO model; set temperature:0.25 for concise instructions.
• Speak: aura-2-spencer-en (clear, neutral)
• Pre-cached prompts with SOP text and thumbnails on device; if the network drops, continue local read-outs and queue logWork for later sync.

Caller: “Where’s my shipment #1Z9-123-456?”

The agent quickly authenticates (name + ZIP), hits the Transportation Management System (TMS) to fetch real-time status, replies: “Your package is in Dallas and out for delivery tomorrow by noon.” Then offers proactive options: “Would you like SMS updates or reroute to a pickup locker?”

“Where-Is-My-Order” (WISMO) calls dominate post-purchase support. Deflecting these with a voice agent reduces queue load, cuts resolution time, and boosts net-promoter-score by offering proactive options (reroute, hold-at-hub, text notifications). Fewer manual lookups free agents for complex issues.

• Natural dialogue so customers can ask follow-ups (“Can you hold it till Friday?”) instead of typing tracking IDs.
• Real-time function calls (getShipmentStatus, offerOptions) integrate directly with TMS/Order Management System (OMS) APIs.
• Robust to accents and noisy channels to reduce misheard tracking numbers; multilingual replies via Nova-3.
• Turn-taking + barge-in keeps the flow snappy (“Actually, that’s 1Z A B C …”) while async API calls return, keeping the experience fluid.
• Low-latency STT→LLM→TTS loop so callers hear confirmations quickly and trust the result.

1) Prompt with status lexicon

Embed carrier status codes (“Label Created”, “In-Transit”, “Out for Delivery”), common reroute verbs (“hold”, “redirect”, “deliver to neighbor”), and a short escalation rule (handoff after two low-confidence parses).

You are a logistics assistant. Always:
1) Verify identity. Ask for name, last 4 of phone or order email, ZIP for auth.
2) Interpret tracking numbers from speech and confirm back.
3) Map carrier status from TMS to plain English; include ETA ± margin.
4) Offer valid options (hold, reroute, window change) when status == "In-Transit".

2) Tools: getShipmentStatus, offerOptions

• getShipmentStatus(trackingId|orderId) → {status, etaISO, lastScan, carrier, location, options:[...]}
• offerOptions(trackingId, action, params) → {ok, newEtaISO, ref} (e.g., reroute, signature waiver).

Return concise JSON so the agent can speak short confirmations, choose the clearest phrasing, and take next action.

3) Speech stack

Use bilingual Nova-3 for real-time STT (partials + barge-in) and Aura-2 for clear confirmations “Your package is out for delivery today between 2 and 5 PM.”).

Consider caching frequent carrier status phrases for <150 ms TTS start. Keep TTS responses ≤10 s; send partial acknowledgments while API calls resolve. Enable bilingual mode if your call mix demands it.

A guest calls and says, “Can I add late checkout, book a taxi to Heathrow for 6 AM, and leave a note for the driver in Spanish saying I have two suitcases?”

The agent updates the reservation, schedules a taxi through the local dispatch API, adds the Spanish note (“Recoger en el Lobby, 6:00 a. m., pasajero con dos maletas”), and confirms charges.

Voice agents create instant, 24/7 concierge coverage without hiring speakers for every language. They upsell add-ons (late check-out, spa, room upgrade), reduce front-desk load, and boost CSAT/NPS by giving guests quick, accurate service in their preferred language.

• Multilingual Nova-3 streams detect caller’s language, code-switches, and maintain context across languages.
• Turn-taking and barge-in let guests amend requests (“Actually, 7 AM pickup”).
• Function calls to booking/dispatch APIs (bookAmenity, orderTaxi, addNote) close the loop instantly.
• Aura-2 TTS speaks confirmations in English, Spanish, which is critical for guest trust.

1) Prompt: amenities and policies

Load a system prompt with: (a) amenity catalog (late checkout rules/fees, spa hours, upgrade policy), (b) confirmation style (“read back date, time, location, price”), (c) bilingual behavior (mirror caller language; if uncertain, ask preferred language once), and (d) PII handling (don’t read full card numbers aloud).

You are the multilingual concierge for Hotel Aurora.
1) Detect guest language on first utterance; mirror the caller’s language (EN/ES).
2) Always confirm: item, time, place, cost, and booking ID.
3) Offer relevant upsells once, politely, never twice.
4) Map amenity names to function calls; confirm monetary amounts.

2) Tools: bookAmenity, orderTaxi, addNote

• bookAmenity(roomId, amenityType, dateTime) → {amenityId, price}
• orderTaxi(roomId, pickupISO, pax, bags) → {bookingId, eta}
• addNote(bookingId, language, text) → {ok}

Return concise JSON; the agent reads back only key fields (time, cost, driver note) and include in the SMS.

3) Speech stack

Use Nova-3 (language_mode: "auto") for live STT. Keep responses short (≤10 s), use Aura-2 (aura-2-thalia-es or aura-2-thalia-es) for before committing. BYO LLM is optional. You can swap via agent.think.provider.model (see docs). Keep temperature:0.25 and tool schema strict for concise phrasing.

🤝 Customer Story: Abby Connect scales high-touch service and launches AI receptionist with Deepgram.

An automated outbound call begins: “Hi Taylor, this is Acme Energy with a friendly reminder. Your balance of $127.45 is due on June 14.” The agent verifies the last-4 of the customer’s phone number, offers a split-payment plan, securely takes card digits (or sends a pay-link via SMS), and—upon request—transfers to a live specialist with full context.

Collections conversations must follow strict scripts, capture promises-to-pay, and stay compliance-clean. A voice agent delivers perfectly consistent disclosures, boosts promise-to-pay (PTP) rate, lowers human agent load during peak cycles, and frees collectors to focus on high-risk accounts.

• Script fidelity and turn control ensure regulatory language (Mini-Miranda, PCI redaction) is never skipped.
• Function calls to billing/CRM gateway APIs (createPaymentPlan, takePayment) close the loop in-call.
• Real-time redaction and DTMF passthrough (masking card numbers, PII) inside the STT pipeline protects data and for robust recognition of amounts/dates.
• Clear numeric read-backs via TTS to confirm amounts/dates with low latency and minimal error.
• Barge-in/turn-taking so callers can interrupt (“I can pay Friday”) without derailing compliance flow.

1) Prompt with compliance script and escalation paths

Include: TCPA/consent language, required disclosures, ID verification order, hardship options, and handoff rules (“On request, or after 2 low-confidence turns, transfer to human and pass summary + balance.”). Keep numeric readouts slow and confirmed twice.

Always read amounts with dollars and cents; confirm the schedule before charging.
If caller requests a human at any point: immediately routeHuman(reason='agent_request') with summary.
After payment success, always read confirmation ID twice.

You are a compliant billing agent. Always:
1) Verify identity before discussing balance.
2) Present plan options from lowest-fee to fastest-clear.
3) Read back terms verbatim and provide a confirmation code.
4) If the caller requests a human at any time, route immediately with transcript summary.

2) Tools: verifyIdentity, getBalance, createPaymentPlan, takePayment, handoffHuman

• verifyIdentity(last4|dob) → {verified: bool}
• getBalance(accountId) → {amountDue, dueDate, pastDue}
• createPaymentPlan(accountId, terms[]) → {planId, schedule[]}
• takePayment(accountId, amount, methodToken|dtmf) → {receiptId}
• handoffHuman(reason, summary) → {transferId}

Return compact JSON so the agent can speak concise confirmations. Redact digits in transcripts ("redact": "pci") inside Settings; never echo raw PAN in logs.

3) Speech and privacy settings

Use Nova-3 for partials/turn events; enable redaction for PII where available and collect card digits via DTMF passthrough. Use Aura-2 with a calm, professional voice; set temperature: 0.1–0.2 to keep phrasing precise and avoid ad-libbing.

After resolving a Wi-Fi outage, the agent says,

“Let me read back what we’ve done.”

It summarizes the fix, next-step ticket, and SLA window, asks “Is that correct?”, accepts a quick correction, then pushes the case note and follow-up task into the CRM before ending the call.

Post-call paperwork—after-call work (ACW)—burns minutes and introduces inconsistency. Automating summaries and tasks cuts ACW, improves data quality/completeness, and ensures consistent follow-ups that protect SLA adherence and customer satisfaction.

• Live transcript stream captures the whole call with no need for manual re-type.
• Function calls post structured objects (createCaseNote, createTask) directly into CRM/ticketing the moment the call ends.
• TTS read-back confirms next steps clearly (dates, owners, deadlines) and lets the customer verify and correct the record to improve data quality.
• Turn-taking/barge-in so callers can say “Actually, my contact number changed.”

1) Prompt with Summary Schema

Embed the target JSON keys—who, issue, steps, outcome, next task, tags—and an instruction to keep read-back ≤15 s.

You are a wrap-up assistant. Produce:
{
  "who": "<name/id>",
  "issue": "<short>",
  "resolution": "<actions>",
  "nextTask": "<owner + due>",
  "tags": ["wifi","tier1"]
}
Then ask: “Does that look right?” Accept corrections, then call createCaseNote & createTask.

2) Tools: createCaseNote, createTask

• createCaseNote(caseId, summaryJson) → {noteId}
• createTask(caseId, taskText, dueISO) → {taskId}

Return IDs so the agent can confirm what was created (“Note N-1847; task T-9921 due Fri 17:00”). Return JSON; the agent updates if caller corrects details.

3) Speech stack

Use Nova-3 for streaming STT (partials + turn events). Have the LLM compile the structured summary, then Aura-2 reads it back. Allow barge-in to capture edits; re-read the corrected line; then call createCaseNote and createTask.

These are the core primitives you’ll reuse across all 10 use cases: turn detection & barge-in, function calling, latency budgeting, and multilingual setup.

Why it matters. Human conversations aren’t half-duplex. You need to:

• start speaking back as soon as intent is clear (partials),
• interrupt TTS the moment the caller talks (barge-in), and
• decide when a “turn” ends to trigger tool calls.

Minimal event loop (Node.js) (conceptual shape; adapt to your audio stack):

// Pseudocode for event-driven loop (WebSocket already open)
let playingTTS = false;

ws.on("message", (buf) => {
  const msg = JSON.parse(buf);

  // 1) Partial transcripts drive fast acks + tentative intent
  if (msg.type === "Transcription" && msg.is_partial) {
    // e.g., show "Got it, checking..." cue in UI
  }

  // 2) Caller begins speaking while we're talking -> barge-in
  if (msg.type === "TurnStart") {
    if (playingTTS) stopLocalTTSPlayback(); // cut audio locally
    playingTTS = false;
  }

  // 3) Turn ends -> trigger tool call / LLM step
  if (msg.type === "TurnEnd") {
    // decide next action: call tool, summarize, confirm, etc.
  }

  // 4) Agent audio to play out
  if (msg.type === "AgentAudio") {
    playPcmChunk(msg.audio); playingTTS = true;
  }

• Use the Settings handshake first; the agent streams partials and emits function calls/agent audio over the same socket.
• Barge-in is handled by reacting to live turn events/partials and stopping local playback immediately (no need to wait for server). For details on the agent lifecycle and messages, see Voice Agent docs.

You’ll call tools like getOrderStatus, bookAppointment, createCaseNote, etc. The agent issues a FunctionCallRequest and expects your FunctionCallResponse { id, name, content }`. Add retries and idempotency keys on your side.

Client handler (Node.js)

// Deduplicate by function-call id (idempotency)
const seen = new Set();

ws.on("message", async (buf) => {
  const msg = JSON.parse(buf);
  if (msg.type !== "FunctionCallRequest") return;

  for (const fn of msg.functions || []) {
    if (seen.has(fn.id)) continue; // idempotency
    seen.add(fn.id);

    const { name } = fn;
    const args = JSON.parse(fn.arguments || "{}");

    const exec = async () => {
      if (name === "getOrderStatus") return await tms.getStatus(args.orderId);
      if (name === "bookAppointment") return await cal.book(args.slotId, args.patientId);
      // ...more tools...
      return { ok:false, error:"unknown function" };
    };

    let content, attempts = 0;
    while (attempts++ < 3) {
      try { content = JSON.stringify(await exec()); break; }
      catch (e) { if (attempts >= 3) content = JSON.stringify({ ok:false, error:String(e) }); }
    }

    ws.send(JSON.stringify({
      type: "FunctionCallResponse",
      id: fn.id,
      name,
      content
    }));
  }
});

• Message flow + JSON shapes are documented in the Function Calling guides.

Target: keep end-to-end < ~1,000 ms from user audio → agent response start. Break it down:

• Mic buffering / Voice Activity Detection (VAD): 50–120 ms
• STT partials (Nova-3): ~100–250 ms to first meaningful token
• LLM + tool call: ~150–400 ms (depends on your infra/tool latency)
• TTS start (Aura-2): sub-~250–400 ms to first audio chunk

Deepgram’s TTS latency documentation walks through measuring TTFB and total synthesis; use it to tune your own measurements and buffer strategy.

Aura-2 materials emphasize low-latency startup suitable for real-time agents; measure in your environment and set SLOs.

Buffering Pattern

// Start speaking as soon as first TTS chunk arrives;
// keep a 100–150 ms jitter buffer to avoid underruns.
tts.on("chunk", (pcm) => buffer.enqueue(pcm));
setInterval(() => {
  if (buffer.sizeMs() > 120) audioOut.play(buffer.dequeueChunk());
}, 10);

Guardrails

• If tool call > 400 ms, send a short partial acknowledgement (“One moment while I check…”) then continue.
• If TTS start > 500 ms, switch to short acknowledgement and investigate network/back-end hops.
• Alert when end-to-end exceeds 1,200 ms for more than N% of turns.

Use when callers code-switch, or you have global callers. Nova-3 supports real-time multilingual transcription and code-switching; you can set a dedicated multilingual mode or use language-specific models per locale.

Settings snippet (multilingual/code-switching)

{
  "type": "Settings",
  "audio": {
    "input": { "encoding": "linear16", "sample_rate": 24000 },
    "output": { "encoding": "linear16", "sample_rate": 24000 }
  },
  "agent": {
    "language": "multi",                  // enable code-switching where supported
    "listen": { "provider": { "type": "deepgram", "model": "nova-3" } },
    "think":   { "provider": { "type": "open_ai", "model": "gpt-4o-mini" } },
    "speak":   { "provider": { "type": "deepgram", "model": "aura-2-thalia-en" } },
    "greeting":"Hola/Hello — ¿Cómo puedo ayudar? / How can I help?"
  }
}

• See the Settings reference and Getting Started guide for full configuration options (providers, prompts, tools).

You’ve got 10 patterns, code templates, and KPI targets. Here’s a pragmatic, staged checklist to take a Voice Agent from “cool demo” to reliable production.

Latency SLOs

• p95: mic → first partial ≤ 250 ms; partial → TTS start ≤ 600 ms; end-to-end round trip ≤ 1.0 s.
• Alert on breaches; surface per-stage timings in logs.

Error budgets and retries

• Define a monthly error budget (e.g., ≤ 0.5% failed interactions).
• Tool calls: timeouts, exponential backoff, and idempotency keys on create/update.

Graceful handoff to human

• One-shot clarify; if confidence stays low or user asks: transfer with summary + transcript + tool context.
• Track handoff rate and post-handoff resolution.

Rate limits and protection

• Apply per-caller and per-IP rate limits; circuit-breaker on upstreams.
• Backpressure: pause listening or respond with partial acks when tools are slow.

Analytics events

• Emit events for: turn times, barge-ins, tool-call outcomes, handoffs, KPI snapshots.
• Tie each session to trace IDs for call→tool→TTS correlation.

Canned fallback prompts

• Short, brand-safe replies for low-confidence cases.
• Include “clarify once → handoff” policy.

A/B Prompt Sets

• Version prompts (A/B/C) with guardrails; rotate weekly.
• Track containment, AHT, CSAT deltas per variant.

Vocabulary and Glossary Updates

• Maintain a domain glossary (SKUs, acronyms, provider names).
• Refresh monthly; validate with domain-term accuracy audits.

Multilingual rollout

• Detect language → mirror caller; ensure policy translations are approved.
• Add bilingual read-backs for safety-critical content (addresses, payments).

Channel expansion

• PSTN (SIP/CCaaS), WebRTC, mobile SDK.
• Normalize session analytics across channels; keep turn/latency SLOs identical.

Operational runbooks

• Incident playbooks (latency spike, tool outage), on-call rotations, rollback of prompts/functions, and weekly KPI reviews.

Enterprise or regulated industry? Talk to a voice AI expert → for deployment options (network isolation, redaction policies, on-prem/virtual private cloud).

Deepgram Voice Agent API brings real-time STT + LLM/tooling + TTS into one pipeline so your agents can listen, think, and act—with the speed and control production teams require.