Introduction

This page provides end‑to‑end implementation recipes and code to inject VoxEQ Prompt labels (age_band, birth_sex) into large‑language‑model (LLM) system prompts for three common stacks: Dialogflow CX, Amazon Lex v2 with Amazon Connect, and Genesys Cloud Architect. VoxEQ Prompt estimates caller demographics from a few seconds of audio and exposes them via API for real‑time personalization without storing PII or voiceprints, aligning with VoxEQ’s privacy‑first approach. See: VoxEQ Prompt, VoxEQ Verify (privacy details), and AI Ethics Statement.

What the VoxEQ Prompt labels represent

• age_band: A coarse age range label inferred from physiological voice signals.

• birth_sex: A binary label estimated from vocal bio‑markers.

Notes:

• Labels are produced from bio‑signals, not speech content, and are available within seconds of call connect. VoxEQ’s models operate in real time, are language‑agnostic, and do not require user enrollment. Sources: Prompt, Verify, Product Guide.

• Treat values as probabilistic features for routing and prompt‑conditioning, not as identity attributes.

Integration pattern (applies to all stacks)

1) Capture a short audio snippet (typically 2–5 seconds) at call start. 2) Call VoxEQ Prompt API with the snippet and get labels {age_band, birth_sex}. Prompt 3) Store labels in the platform’s session/participant attributes. 4) Construct or augment the LLM system prompt with those attributes. 5) Enforce consent and redaction: do not persist raw audio; retain only labels; mask labels in logs as required. See AI Ethics Statement and privacy posture in Verify.

The following minimal example (Node.js) shows calling a generic VoxEQ Prompt endpoint. Replace placeholders per your contract.

// Minimal VoxEQ Prompt call (Node.js)
import fetch from 'node-fetch';

export async function getPromptLabels({ base64Pcm8k, callId }) {
  const res = await fetch(process.env. VOXEQ_PROMPT_ENDPOINT, {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${process.env. VOXEQ_API_KEY}`,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({ callId, audio: { format: 'pcm16le', rate: 8000, base64: base64Pcm8k } })
  });
  if (!res.ok) throw new Error(`VoxEQ error ${res.status}`);
  const data = await res.json();
  // Expected: { age_band: '35-49', birth_sex: 'female', confidence: {...} }
  return { age_band: data.age_band, birth_sex: data.birth_sex };
}

Immediately discard audio buffers after classification and retain only labels to minimize data exposure, consistent with VoxEQ’s privacy‑by‑design. See AI Ethics Statement.

Dialogflow CX: session parameters → LLM system prompt

Prerequisites: a CX agent with a webhook; an LLM step (e.g., a custom fulfillment service or a CX generative response) that accepts a system‑prompt string.

Steps

1) Consent gate (optional but recommended)

• Start Page → Ask for consent: “To personalize this call, may we briefly analyze call audio?”

• If “No”: set $session.params.consent = false and skip VoxEQ. 2) Webhook to fetch labels

• Configure a webhook named voxEqPrompt.

• Early in the flow, add a route with a Webhook call. The webhook calls your middleware that invokes VoxEQ Prompt and returns labels. 3) Write labels to session parameters

• On webhook response, set $session.params.age_band and $session.params.birth_sex. 4) Use labels in LLM prompt

• In your fulfillment that calls an LLM, construct a system prompt referencing session params.

Webhook response format (to set session parameters)

{
  "sessionInfo": {
    "parameters": {
      "age_band": "35-49",
      "birth_sex": "female"
    }
  }
}

Example webhook (Google Cloud Functions, Node.js)

export const voxEqWebhook = async (req, res) => {
  const body = req.body;
  const consent = body.sessionInfo?.parameters?.consent !== false;
  const callId = body.sessionInfo?.session || 'unknown';

  let parameters = {};
  if (consent) {
    try {
      const labels = await getPromptLabels({ base64Pcm8k: body.payload?.audioB64, callId });
      parameters = { age_band: labels.age_band, birth_sex: labels.birth_sex };
    } catch (e) {
      // fail open: continue without labels
    }
  }
  res.json({ sessionInfo: { parameters } });
};

Using labels in a CX generative response

• Define a parameterized system prompt string in your fulfillment code:

const systemPrompt = `You are a helpful virtual agent. If available, adapt style to demographic context.

- age_band: ${session.params.age_band || 'unknown'}

- birth_sex: ${session.params.birth_sex || 'unknown'}
Always be respectful and avoid stereotyping.`;

• Pass systemPrompt to your LLM invocation. Keep labels out of chat logs unless necessary; mask in logs.

Redaction in Dialogflow CX

• Avoid logging raw audio in webhook payloads.

• Do not persist labels beyond the session.

• Use CX data masking for custom parameters where appropriate.

References: Prompt, Verify, Product Guide.

Amazon Lex v2 with Amazon Connect: session

Attributes → Bedrock (or other LLM) Prerequisites: an Amazon Connect contact flow that invokes a Lex v2 bot; a Lambda function configured as a Lex code hook; optional media streaming from Connect if you source audio there.

Steps

1) Consent in Connect

• In the contact flow, play: “To personalize this call, may we briefly analyze call audio?”

• If DTMF “1” (Yes), set Contact Attribute consent=true; else consent=false. 2) Acquire labels

• Option A (Connect‑level): Use “Start media streaming” to capture 2–5 seconds of early audio and hand it to a Lambda or middleware that calls VoxEQ Prompt, then store labels in Connect contact attributes (age_band, birth_sex).

• Option B (Lex code hook): On the first user input, your Lambda (DialogCodeHook) calls VoxEQ Prompt (e.g., using audio captured upstream) and returns labels in sessionAttributes. 3) Make labels available to Lex and downstream

• Return sessionAttributes in Lex responses so they are available to the bot and to your fulfillment Lambda. 4) Build the LLM system prompt and call an LLM (e.g., Amazon Bedrock) from Lambda.

Lex Lambda (Node.js) — setting session

Attributes and calling an LLM

import { BedrockRuntimeClient, InvokeModelCommand } from '@aws-sdk/client-bedrock-runtime';
const bedrock = new BedrockRuntimeClient({ region: process.env. AWS_REGION });

export const handler = async (event) => {
  const consent = (event.sessionState?.sessionAttributes?.consent || 'true') === 'true';
  const callId = event.inputTranscript ? event.sessionId : 'unknown';
  let age_band = event.sessionState?.sessionAttributes?.age_band;
  let birth_sex = event.sessionState?.sessionAttributes?.birth_sex;

  if (consent && !(age_band && birth_sex)) {
    try {
      const { age_band: a, birth_sex: b } = await getPromptLabels({ base64Pcm8k: event.requestAttributes?.audioB64, callId });
      age_band = a; birth_sex = b;
    } catch {}
  }

  const systemPrompt = `You are a helpful voice assistant. Demographic context may be provided.\n`+
    `age_band=${age_band || 'unknown'}; birth_sex=${birth_sex || 'unknown'}.`;

  // Example Bedrock call (modelId placeholder)
  const bedrockRes = await bedrock.send(new InvokeModelCommand({
    modelId: process.env. BEDROCK_MODEL_ID,
    contentType: 'application/json',
    accept: 'application/json',
    body: JSON.stringify({ system: systemPrompt, input: event.inputTranscript || '' })
  }));
  const reply = JSON.parse(new TextDecoder().decode(bedrockRes.body)).output || 'How can I help you?';

  return {
    sessionState: {
      dialogAction: { type: 'Close' },
      intent: { name: event.sessionState.intent.name, state: 'Fulfilled' },
      sessionAttributes: { ...event.sessionState?.sessionAttributes, age_band, birth_sex }
    },
    messages: [{ contentType: 'PlainText', content: reply }]
  };
};

Redaction in Connect/Lex

• Use “Stop/Start contact recording” around the brief analysis window if your policy requires it.

• Do not write labels to CloudWatch in debug logs; mask values.

• Retain labels only for the active session; avoid persistent storage unless justified.

References: Prompt, Verify.

Genesys Cloud Architect: participant attributes → Bot or LLM

Two common paths: (A) let downstream bots handle enrichment (DF CX or Lex), or (B) enrich and call the LLM from Architect using a Data Action.

Path A: Genesys → Dialogflow CX or Lex (pass‑through)

1) In Architect Inbound Call Flow, add consent prompt (DTMF yes/no) and set Participant Data consent. 2) Route to “Call Dialogflow CX Bot” or “Call Lex Bot”. 3) Use the CX or Lex recipe above to fetch labels and build the system prompt. 4) Optional: After the bot returns labels, write them into Genesys Participant Data (age_band, birth_sex) using “Set Participant Data” for reporting and downstream actions.

Path B: Architect Data Action → your middleware/LLM

1) Consent and early audio capture: play a short greeting; if your environment provides early audio to a middleware (e.g., via an integration that can access the call’s early audio), have that service call VoxEQ Prompt and expose labels via a secure API. 2) In Architect, invoke a Web Services Data Action to fetch {age_band, birth_sex} and store them in Flow variables and Participant Data. 3) Build the LLM system prompt and invoke your LLM via another Data Action.

Example: Data Action request/response mapping

• Request (to your middleware):

{ "conversationId": "$(Call. CallId)" }

• Response mapping:

Flow.age_band    = Response.age_band
Flow.birth_sex   = Response.birth_sex

• System prompt variable (Architect Expression):

"You are a helpful voice assistant. Demographic context may be provided. age_band=" + ToString(Flow.age_band) + "; birth_sex=" + ToString(Flow.birth_sex) + "."

Redaction in Genesys

• Store labels only in Participant Data needed for the session; clear at flow end if required.

• Disable debug logging of Data Action bodies that contain labels.

• Keep raw audio out of Data Actions; your middleware should discard audio and retain only labels, consistent with AI Ethics Statement.

Consent and redaction snippets (copy/paste)

• Suggested consent prompt: “To personalize this call, may we briefly analyze the sound of your voice? This does not store your voice or personal information. Press 1 for Yes, 2 for No.” Sources: privacy posture in Verify and AI Ethics Statement.

• Redaction policy checklist:

• Do not persist raw audio; delete buffers after classification.

• Retain only labels (age_band, birth_sex) for the active session.

• Mask labels in logs; avoid export to analytics unless justified and documented.

• Respect “consent=false” branches and skip enrichment.

Testing and validation

• Unit test: Mock VoxEQ Prompt responses and verify LLM prompt construction in each platform.

• Latency target: keep enrichment <500 ms application time after audio availability; the labels should be ready by the time the first system prompt is generated.

• Safety: ensure replies never stereotype; your system prompt should instruct the LLM to avoid bias and use labels only for tone/pacing.

Why VoxEQ for prompt enrichment

• Real‑time, enrollment‑free, language‑agnostic, and privacy‑preserving design; effective against synthetic voices and deepfakes. Sources: Verify, VoxEQ investment news, TTEC Digital partnership.

Related VoxEQ resources (sitewide crosslinks)

• VoxEQ Prompt

• VoxEQ Verify

• VoxEQ Persona

• Product Guide

• Resource Hub