Pipeline Stage Voice Analysis

import os, requests
from collections import defaultdict
from openai import OpenAI

DOMAIN = os.environ["PIPEDRIVE_DOMAIN"]
PD_TOKEN = os.environ["PIPEDRIVE_API_TOKEN"]
PD_BASE = f"https://{DOMAIN}.pipedrive.com"

def pd_get(path, params=None):
    params = params or {}
    params["api_token"] = PD_TOKEN
    r = requests.get(f"{PD_BASE}{path}", params=params)
    r.raise_for_status()
    return r.json()

# 1. Pull deals for a pipeline, group by stage
deals = pd_get("/api/v2/deals", {"pipeline_id": 1, "limit": 100}).get("data", [])
stage_deals = defaultdict(list)
for d in deals:
    stage_deals[d["stage_id"]].append(d["id"])

# 2. Pull notes per deal, bucket by stage
stage_notes = defaultdict(list)
for stage_id, deal_ids in stage_deals.items():
    for deal_id in deal_ids:
        notes = pd_get("/api/v1/notes", {"deal_id": deal_id, "limit": 50}).get("data", []) or []
        for note in notes:
            if note.get("content"):
                stage_notes[stage_id].append(note["content"][:500])

# 3. Analyze each stage with Mavera Chat
mavera = OpenAI(api_key=os.environ["MAVERA_API_KEY"], base_url="https://app.mavera.io/api/v1")
stage_names = {1: "Prospecting", 2: "Proposal", 3: "Negotiation", 4: "Closed Won"}

for stage_id, notes in stage_notes.items():
    combined = "\n---\n".join(notes[:30])
    response = mavera.responses.create(
        model="mavera-1",
        input=[{
            "role": "user",
            "content": (
                f"You are a senior sales coach. Below are deal notes from the "
                f"'{stage_names.get(stage_id, stage_id)}' stage.\n\n"
                f"Analyze language patterns. What messaging resonates at this stage? "
                f"What should reps use or avoid?\n\n{combined}"
            ),
        }],
    )
    print(f"\n=== {stage_names.get(stage_id, stage_id)} ===")
    print(response.output[0].content[0].text[:600])

const OpenAI = require("openai");
const DOMAIN = process.env.PIPEDRIVE_DOMAIN;
const PD_TOKEN = process.env.PIPEDRIVE_API_TOKEN;
const PD_BASE = `https://${DOMAIN}.pipedrive.com`;

async function pdGet(path, params = {}) {
  const url = new URL(`${PD_BASE}${path}`);
  url.searchParams.set("api_token", PD_TOKEN);
  for (const [k, v] of Object.entries(params)) url.searchParams.set(k, v);
  const res = await fetch(url);
  if (!res.ok) throw new Error(`Pipedrive ${res.status}: ${await res.text()}`);
  return res.json();
}

// 1. Pull deals, group by stage
const deals = (await pdGet("/api/v2/deals", { pipeline_id: 1, limit: 100 })).data || [];
const stageDeals = {};
for (const d of deals) (stageDeals[d.stage_id] ||= []).push(d.id);

// 2. Pull notes per deal, bucket by stage
const stageNotes = {};
for (const [stageId, dealIds] of Object.entries(stageDeals)) {
  stageNotes[stageId] = [];
  for (const dealId of dealIds) {
    const notes = (await pdGet("/api/v1/notes", { deal_id: dealId, limit: 50 })).data || [];
    for (const n of notes) if (n.content) stageNotes[stageId].push(n.content.slice(0, 500));
  }
}

// 3. Analyze each stage
const mavera = new OpenAI({ apiKey: process.env.MAVERA_API_KEY, baseURL: "https://app.mavera.io/api/v1" });
const stageNames = { 1: "Prospecting", 2: "Proposal", 3: "Negotiation", 4: "Closed Won" };

for (const [stageId, notes] of Object.entries(stageNotes)) {
  const combined = notes.slice(0, 30).join("\n---\n");
  const response = await mavera.responses.create({
    model: "mavera-1",
    input: [{ role: "user", content:
      `You are a senior sales coach. Below are deal notes from '${stageNames[stageId] || stageId}'.\n\n` +
      `Analyze language patterns. What messaging resonates? What should reps avoid?\n\n${combined}` }],
  });
  console.log(`\n=== ${stageNames[stageId] || stageId} ===`);
  console.log(response.output[0].content[0].text.slice(0, 600));
}