AI Commercial Proposal Generation Automation Implementation

AI Automation of Commercial Proposal Generation

A manager spends 1–3 hours per commercial proposal per client. An AI system generates personalized commercial proposal in 2–5 minutes: extracts data from CRM, selects relevant case studies, formulates value proposition for client's specific pain points, assembles PDF with branded design.

System Architecture

CRM data (AmoCRM/Bitrix24) + industry template
    ↓ GPT-4o: content and structure personalization
    ↓ Case study selection from knowledge base (vector search)
    ↓ PDF generation (WeasyPrint / python-docx)
    ↓ Open tracking (pixel tracking / DocuSign)

Personalized Proposal Generator

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 manager CRM 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"""You are a B2B copywriter, expert in persuasive commercial proposals.
            Create a proposal focused on the decision maker: {brief.decision_maker_role}.

            STRUCTURE:
            1. Personal greeting (client's pain, not our achievements)
            2. Problem understanding (show you understand the challenge)
            3. Our solution (concrete to their task, not generic service)
            4. Why us (case studies, numbers, not words)
            5. What you'll get (measurable result)
            6. Next steps (concrete CTA with date)

            Tone: confident, no flattery or clichés ("we're pleased to offer...").
            Client budget level: {brief.budget_tier} — adjust detail accordingly.
            {"Add urgency emphasis to solution." if brief.deadline_pressure else ""}

            Return JSON: {{executive_summary, problem_statement, solution_description, why_us, deliverables, next_steps, subject_line}}"""
        }, {
            "role": "user",
            "content": f"""
            Client: {brief.client_name}, {brief.client_company} ({brief.industry})
            Pain points: {', '.join(brief.pain_points)}
            Service: {brief.service_type}
            Relevant case studies:
            {cases_summary}
            Manager: {brief.manager_name}
            """
        }],
        response_format={"type": "json_object"}
    )
    return json.loads(response.choices[0].message.content)

Relevant Case Study Search 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 similar to client's task"""
    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]]

PDF Assembly with Branded Design

from weasyprint import HTML, CSS
from jinja2 import Environment, FileSystemLoader
import base64

def render_proposal_pdf(proposal_data: dict, template_name: str = "default") -> bytes:
    env = Environment(loader=FileSystemLoader("templates/proposals"))
    template = env.get_template(f"{template_name}.html")

    html_content = template.render(**proposal_data)

    # Branded CSS with brand color variables
    css = CSS(string="""
        @page { size: A4; margin: 20mm 15mm; }
        body { font-family: 'Inter', sans-serif; color: #1a1a2e; }
        .highlight { background: #f0f4ff; border-left: 4px solid #4361ee; padding: 12px; }
        .case-block { border: 1px solid #e2e8f0; border-radius: 8px; padding: 16px; }
    """)

    pdf_bytes = HTML(string=html_content).write_pdf(stylesheets=[css])
    return pdf_bytes

CRM Integration

System connects to AmoCRM or Bitrix24 via API: when deal moves to "Proposal Preparation" stage, automatically requests contact data, conversation history from manager notes, service type from deal field. Manager receives draft within 2–3 minutes and makes final edits in web editor before sending.

Tracking implemented via pixel in HTML email or DocuSign API — manager sees when client opened proposal and time spent on each page.

Personalization Variants by Decision Maker Role

Proposal generator with single CRM integration and PDF export — 2–3 weeks. Full platform with case base, tracking, A/B testing proposal versions, and conversion analytics — 6–8 weeks.