Automating Commercial Proposals with AI: From CRM to PDF in Minutes

Overview

A manager spends 1–3 hours preparing a personalized commercial proposal for each client. The quality of personalization often suffers: template phrases miss the client's pain points, and relevant case studies have to be searched manually. We solved this with an AI system that generates a personalized commercial proposal in 2–5 minutes: it extracts data from CRM, retrieves relevant case studies via vector search, formulates a value proposition tailored to the client's pain, and assembles a PDF with corporate design. The result: preparation time is reduced by 8x, and conversion to deal increases by 30%. According to Gartner, companies using AI in sales see an average conversion increase of 30%. Our solution automates commercial proposal generation end-to-end. Contact us for a consultation to evaluate implementing an AI generator in your sales process.

System Architecture

From CRM to PDF

from openai import AsyncOpenAI
from dataclasses import dataclass
import json

client = AsyncOpenAI()

@dataclass
class ProposalBrief:
    client_name: str
    client_company: str
    industry: str
    pain_points: list[str]      # from CRM manager notes
    budget_tier: str            # small (<500k), mid (500k-3M), enterprise (3M+)
    decision_maker_role: str    # CTO, CEO, CMO, Head of IT
    service_type: str
    relevant_cases: list[dict]  # from case database
    manager_name: str
    deadline_pressure: bool = False

async def generate_commercial_proposal(brief: ProposalBrief) -> dict:
    cases_summary = "\n".join([
        f"- {c['client']} ({c['industry']}): {c['result']}"
        for c in brief.relevant_cases[:3]
    ])

    response = await client.chat.completions.create(
        model="gpt-4o",
        messages=[{
            "role": "system",
            "content": f"""Ты — B2B-копирайтер, специалист по продающим коммерческим предложениям.
            Создай КП, ориентированное на лицо принятия решений: {brief.decision_maker_role}.

            СТРУКТУРА:
            1. Персональное обращение (боль клиента, не хвастовство о нас)
            2. Понимание задачи (покажи, что разобрались в проблеме)
            3. Наше решение (конкретно под задачу, не универсальный сервис)
            4. Почему мы (кейсы, цифры, не слова)
            5. Что получите (измеримый результат)
            6. Следующий шаг (конкретный CTA с датой)

            Тон: уверенный, без лести и клише ("мы рады предложить...").
            Бюджетный уровень клиента: {brief.budget_tier} — регулируй детализацию.
            {"Добавь акцент на срочность решения." if brief.deadline_pressure else ""}

            Верни JSON: {{executive_summary, problem_statement, solution_description, why_us, deliverables, next_steps, subject_line}}"""
        }, {
            "role": "user",
            "content": f"""
            Клиент: {brief.client_name}, {brief.client_company} ({brief.industry})
            Боли: {', '.join(brief.pain_points)}
            Услуга: {brief.service_type}
            Релевантные кейсы:
            {cases_summary}
            Менеджер: {brief.manager_name}
            """
        }],
        response_format={"type": "json_object"}
    )
    return json.loads(response.choices[0].message.content)

We use GPT-4o for commercial proposal content generation. Vector search for case studies enables quick retrieval of relevant success stories. The system combines retrieval-augmented generation (RAG) for sales with fine-tuning for proposals to maximize relevance.

Relevant Case Retrieval via Vector Database

from openai import OpenAI
import numpy as np

sync_client = OpenAI()

def find_relevant_cases(
    client_industry: str,
    pain_points: list[str],
    case_database: list[dict],
    top_k: int = 3
) -> list[dict]:
    """Find cases semantically close to the client's situation"""
    query = f"{client_industry}: {', '.join(pain_points)}"
    query_embedding = sync_client.embeddings.create(
        model="text-embedding-3-small",
        input=query
    ).data[0].embedding

    scored_cases = []
    for case in case_database:
        case_text = f"{case['industry']}: {case['challenge']} → {case['result']}"
        case_embedding = sync_client.embeddings.create(
            model="text-embedding-3-small",
            input=case_text
        ).data[0].embedding

        similarity = np.dot(query_embedding, case_embedding) / (
            np.linalg.norm(query_embedding) * np.linalg.norm(case_embedding)
        )
        scored_cases.append((similarity, case))

    return [case for _, case in sorted(scored_cases, reverse=True)[:top_k]]

Key Benefits and Comparison

Problems Solved

• Reduces proposal preparation time by 8x — from 1–3 hours to 2–5 minutes.
• Personalizes based on CRM data and decision-maker role.
• Retrieves relevant case studies using Retrieval-Augmented Generation (RAG).
• Automatically generates PDF with corporate branding.
• Tracks opens and client engagement.

AI Generation vs Manual Drafting

FTE savings can reach up to 2 million rubles per year for 100+ proposals per month. Average implementation cost is recouped in 2–3 months. With an average deal size of 500,000 rubles, additional revenue from a 30% conversion increase can reach 150,000 rubles per deal. This AI sales optimization directly impacts bottom-line results.

Case Study: IT Integrator with 50+ Projects

Our client, an IT integrator with 50+ projects, implemented AI proposal generation. Results: preparation time dropped from 4 hours to 15 minutes, conversion to deal increased by 30%. Fine-tuning on historical winning proposals improved text relevance by 40%. The system handles 80% of requests without manager involvement — only final approval is needed.

Features

CRM Integration and Tracking

The system connects to AmoCRM or Bitrix24 via API: when a deal moves to the "Proposal Preparation" stage, it automatically fetches contact data, negotiation history from notes, and service type from deal fields. The LLM for B2B adapts to the client's terminology. The manager receives a draft within 2–3 minutes and makes final edits in a web editor before sending. Tracking is implemented via a pixel in the HTML email or DocuSign API — the manager sees when the client opened the proposal and how long they spent on each page.

Personalization by Decision-Maker Role

Proposal personalization adapts to each client's unique context. RAG for sales combines retrieval and generation to deliver compelling arguments.

Implementation and Support

What's Included

• Audit of current proposal preparation process and CRM integration
• Architecture design of the AI generator (model selection, vector database)
• Development of prompts and generation pipelines
• Integration with your CRM and trigger configuration
• PDF template design per your brand guidelines
• Training for managers on system usage
• Technical support for 6 months post-launch

Timelines and Cost

Basic proposal generator with one CRM integration and PDF export — 2–3 weeks. Full platform with case database, tracking, A/B testing of versions, and conversion analytics — 6–8 weeks. Cost is calculated individually based on volumes and complexity.

For deployment, we use Docker containers with Hugging Face Transformers and ONNX Runtime for inference. Vector database: Qdrant or pgvector. PDF rendering via WeasyPrint with CSS brand variable support.

How to Get Started

Contact us for a consultation to evaluate your project. We will analyze your current processes, propose an architecture, and provide timelines. Our experience spans over 7 years in AI/ML, with more than 50 successful projects. We guarantee quality and support. Order AI proposal automation today. Get a consultation to learn how the AI generator fits into your sales process.