Post-call analysis of transcripts and saving recordings

import type { NextRequest } from 'next/server'

export async function POST(req: NextRequest) {
  const payload = await req.json()

  if (payload.type === 'session.end') {
    // Persist transcript + metrics for analytics or QA queues.
    // For example, insert payload.transcript rows into your database along with
    // the latency + duration stats so dashboards and QA reviewers can query the
    // conversation later.
  }

  if (payload.type === 'session.update' && payload.recording_status === 'completed') {
    // Recording is ready—download the file and kick off downstream processing.
    // For example, stream payload.recording_url into your storage bucket (S3,
    // GCS, etc.) and enqueue summarization or compliance jobs that work off the
    // stored WAV file.
  }

  return new Response('ok')
}

curl -H "Authorization: Bearer $LAYERCODE_API_KEY" \
  https://api.layercode.com/v1/agents/AGENT_ID/sessions/SESSION_ID

curl -L -H "Authorization: Bearer $LAYERCODE_API_KEY" \
  -o session.wav \
  https://api.layercode.com/v1/agents/AGENT_ID/sessions/SESSION_ID/recording

import { generateObject } from 'ai'
import { createGoogleGenerativeAI } from '@ai-sdk/google'

type PostCallInsights = {
  summary: string
  intent:string
  follow_ups: string | null
  customerName: string | null
  sentiment: 'happy' | 'frustrated' | 'neutral'
}

export async function analyzeSessionTranscript(transcript: string) {
  const { object } = await generateObject<PostCallInsights>({
    model: createGoogleGenerativeAI({ apiKey: process.env.GOOGLE_GENERATIVE_AI_API_KEY })('gemini-2.5-flash-lite'),
    prompt: `You are analyzing an AI voice recording for an out of hours plumbing service.\n\nTranscript:\n${transcript}\n\nSummarize the call in 2 sentences, capture the caller's first name if stated, their call intent, follow ups (if applicable) and classify whether the caller felt happy, frustrated, or neutral by the end of the call.`,
    schema: {
      type: 'object',
      properties: {
        summary: { type: 'string' },
        customerName: { type: ['string', 'null'] },
        sentiment: {
          type: 'string',
          enum: ['happy', 'frustrated', 'neutral'],
        },
      },
      required: ['summary', 'sentiment'],
    },
  })

  return object
}