Sales call intelligence

const baseUrl = "https://api.assemblyai.com";
const headers = {
  authorization: "YOUR_API_KEY",
  "Content-Type": "application/json",
};

const audioUrl = "https://assembly.ai/wildfires.mp3";

// Step 1: Transcribe with speaker labels + sentiment analysis
let res = await fetch(`${baseUrl}/v2/transcript`, {
  method: "POST",
  headers,
  body: JSON.stringify({
    audio_url: audioUrl,
    speech_models: ["universal-3-pro"],
    speaker_labels: true,
    sentiment_analysis: true,
  }),
});
if (!res.ok) throw new Error(`Error: ${res.status}`);
const { id: transcriptId } = await res.json();

let result;
while (true) {
  res = await fetch(`${baseUrl}/v2/transcript/${transcriptId}`, { headers });
  result = await res.json();
  if (result.status === "completed") break;
  if (result.status === "error")
    throw new Error(`Transcription failed: ${result.error}`);
  await new Promise((r) => setTimeout(r, 3000));
}

// Step 2: Identify speakers by role
const understandingRes = await fetch(
  "https://llm-gateway.assemblyai.com/v1/understanding",
  {
    method: "POST",
    headers,
    body: JSON.stringify({
      transcript_id: transcriptId,
      speech_understanding: {
        request: {
          speaker_identification: {
            speaker_type: "role",
            known_values: ["Sales Rep", "Customer"],
          },
        },
      },
    }),
  }
);
if (!understandingRes.ok) throw new Error(`Error: ${understandingRes.status}`);
const identified = await understandingRes.json();

// Step 3: Calculate talk/listen ratio per speaker
const speakerDurations = {};
for (const u of identified.utterances) {
  speakerDurations[u.speaker] =
    (speakerDurations[u.speaker] || 0) + (u.end - u.start);
}
const totalMs = Object.values(speakerDurations).reduce((a, b) => a + b, 0);
const talkRatios = {};
for (const [speaker, dur] of Object.entries(speakerDurations)) {
  talkRatios[speaker] = ((dur / totalMs) * 100).toFixed(1) + "%";
}

// Step 4: Summarize sentiment shifts
const sentimentBySpeaker = {};
for (const s of result.sentiment_analysis_results) {
  const speaker = s.speaker || "Unknown";
  if (!sentimentBySpeaker[speaker]) sentimentBySpeaker[speaker] = [];
  sentimentBySpeaker[speaker].push(s.sentiment);
}

let sentimentSummary = "";
for (const [speaker, sentiments] of Object.entries(sentimentBySpeaker)) {
  const counts = { POSITIVE: 0, NEUTRAL: 0, NEGATIVE: 0 };
  sentiments.forEach((s) => counts[s]++);
  sentimentSummary += `${speaker}: ${counts.POSITIVE} positive, ${counts.NEUTRAL} neutral, ${counts.NEGATIVE} negative\n`;
}

// Step 5: Format transcript and generate coaching scorecard
const speakerTranscript = identified.utterances
  .map((u) => `${u.speaker}: ${u.text}`)
  .join("\n");

res = await fetch("https://llm-gateway.assemblyai.com/v1/chat/completions", {
  method: "POST",
  headers,
  body: JSON.stringify({
    model: "claude-sonnet-4-5-20250929",
    messages: [
      {
        role: "user",
        content:
          "You are a sales coaching assistant. Analyze this sales call and produce a scorecard.\n\n" +
          `Talk/listen ratios: ${JSON.stringify(talkRatios)}\n\n` +
          `Sentiment breakdown:\n${sentimentSummary}\n` +
          `Transcript:\n${speakerTranscript}\n\n` +
          "Produce:\n" +
          "1. Call summary (2-3 sentences)\n" +
          "2. Talk/listen ratio analysis (ideal is 40/60 for the rep)\n" +
          "3. Customer sentiment shifts and what caused them\n" +
          "4. Top 3 coaching suggestions for the sales rep",
      },
    ],
    max_tokens: 2000,
  }),
});
if (!res.ok) throw new Error(`Error: ${res.status}`);
const llmResult = await res.json();

console.log("=== Sales Call Scorecard ===\n");
console.log(llmResult.choices[0].message.content);