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.

Generative AI Development: From Prompt to Production API

We often receive a task "generate a product image" — on the surface it seems simple. But behind this lies a choice between dozens of models, configuring the inference pipeline, manually solving consistency issues, integrating into the product backend, and answering why the model generates hands with six fingers in staging but not in production. Let's break down the directions we work with.

Image Generation: From Prompt to Production API

The current landscape includes FLUX.1 [dev/schnell/pro] from Black Forest Labs and Stable Diffusion 3.5. FLUX.1 [schnell] takes 4 steps instead of 20–50 for SDXL — 5–12 times faster — while maintaining higher quality. On an A100 80GB — 1.2–1.8 s per 1024×1024 image at batch_size=4.

A typical deployment issue: FLUX.1 [dev] requires 24+ GB VRAM in fp16. On A10G 24GB it fits tightly; at batch_size>1 — OOM. Solution: torch_dtype=torch.bfloat16 + enable_model_cpu_offload() from diffusers, or quantization via bitsandbytes to NF4 — minimal quality drop, memory consumption drops to 12–14 GB.

ControlNet and IP-Adapter are key tools for production tasks where controllability is needed. ControlNet with Canny/Depth/Pose maps provides structural control. IP-Adapter (especially IP-Adapter-FaceID) allows transferring character identity to generations — this is the foundation for personalized content. More about ControlNet can be found on Wikipedia.

Case study: e-commerce photography. A retailer with 8000 SKUs needed lifestyle photos for each product. Pipeline: product segmentation (Segment Anything Model 2) → background removal → inpainting with FLUX.1 [dev] using product image as IP-Adapter reference → upscale via RealESRGAN_x4plus. The generation cost is negligible compared to professional photography, providing huge savings. Throughput — 200 images/hour on 2× A100. Our extensive experience from 30+ projects ensures we select the optimal model for your task — an evaluation can be obtained upfront.

Why Is Model Selection Only Half the Battle?

Fine-tuning for a Specific Style or Character

Dreambooth and LoRA are the standard for adapting to a specific visual style or object. LoRA trains in 2–4 hours on 20–30 reference images on a single A100. Rank 16–32 is usually sufficient for style; rank 64+ is needed for precise face reproduction.

A common mistake: training LoRA too long — the model overfits to references, losing the ability to vary. Sign: at cfg_scale=7, all images look like copy-paste of references. Solved by early stopping (usually 1500–2000 steps for 20 images) and prior_preservation_loss.

For deeper customization — full fine-tuning via diffusers + accelerate with FSDP on multiple GPUs. But that already takes 40–80 hours of training and requires a truly large dataset (1000+ images).

Comparison of Image Generation Approaches

Video Generation: Which Models Are Best?

Open-weight video models still lag behind commercial ones in stability and length. Wan2.1 is the best choice for self-hosting: 14B parameters, runs on 2× A100, delivers acceptable quality for short clips.

The main pain of video generation is temporal consistency: the character changes clothing color at the third second, objects "drift." Partial solution — generation with motion_bucket_id and noise_aug_strength in Stable Video Diffusion, or using I2V (image-to-video) instead of pure text-to-video. As noted in VideoPoet research, consistency is achieved by training on long sequences.

AnimateDiff remains a working tool for short loops and motion effects on top of SD/FLUX. Not Sora, but deployable locally and predictable.

Music and Audio Generation

AudioCraft from Meta (MusicGen + AudioGen) is a production-ready stack for music generation. musicgen-large (3.3B) generates 30 s of music in ~8 s on A100. Control via text prompt and melody conditioning — you can specify a melody by humming.

Stable Audio Open from Stability AI is an alternative with length up to 47 s, better structural control (intro/verse/chorus). Deployment is similar: diffusers + FastAPI.

For voice-over and dubbing — ElevenLabs API or self-hosted XTTS v2 (see Speech AI service). For sound design and foley — AudioGen.

3D Generation: Current Practical State

3D generation has not yet reached the same maturity as 2D. But for specific tasks, tools are already working:

TripoSG and Shap-E — text/image-to-3D. Shap-E from OpenAI generates simple 3D meshes in seconds, but geometry is rough. TripoSG gives more detailed results but requires post-processing (remeshing, UV unwrapping).

Wonder3D and Zero123++ — 3D reconstruction from a single image. They work by generating multi-views (6–8 views) and then 3D reconstruction via NeuS or instant-ngp.

Gaussian Splatting (3DGS) — not generation, but reconstruction from a series of photos/videos. For product cards and real estate it's already production: 50–200 photos → 3DGS model in 15–30 min on RTX 4090 → interactive 3D viewer in browser.

What Infrastructure Is Needed for Generative AI Deployment?

Critical for generative models:

• Task queue — Celery + Redis or Ray Serve. Synchronous HTTP for image generation is unacceptable with >5 concurrent requests.
• Caching — similar prompts yield similar results. Semantic cache via embeddings (faiss + sentence-transformers) can reduce GPU load by 20–40%.
• Quality monitoring — CLIP score for text-image alignment, FID for evaluating generation distribution. Integrate into MLflow or Weights & Biases.
• Storage — generated images immediately to S3/MinIO, not on the inference server disk.

What's Included in the Deliverables

We take the project turnkey — from model selection to deployment and monitoring. The result includes:

• Model (or API integration) with performance benchmarks (latency p99, throughput).
• Pipeline documentation (prompt engineering guide, model card, dependency versions).
• Integration with your backend (REST/gRPC, queues).
• Configured monitoring (dashboards, alerts for quality drift).
• Training workshop for the team (2–4 hours).
• Warranty support for 3 months after launch — as part of our quality certificate.

We have completed 30+ projects in generative AI — this gives us the right to guarantee results.

How Is the Generative AI Development Process Structured?

1. Analysis (1–2 days): audit of current architecture, clarification of use case, selection of models and success metrics. We evaluate the project free of charge.
2. Proof of Concept (1–3 weeks): quick prototype on your data — to see real quality, not blog demos.
3. Design (1–2 weeks): pipeline architecture, infrastructure (GPU cluster/API), A/B testing plan.
4. Implementation and fine-tuning (4–12 weeks): development, LoRA/full fine-tuning, integration with queue and cache.
5. Testing (1–2 weeks): load tests, metric validation, edge-case verification (negative scenarios).
6. Deployment and monitoring (1–2 weeks): production deployment, monitoring setup, documentation.

Timeline Estimates

Integration of a ready API (DALL·E 3, Midjourney API, Stability API) — 1–2 weeks. Self-hosted pipeline with fine-tuning — 6–12 weeks. Full platform with UI, queues and monitoring — 3–6 months. The specific cost is calculated individually after analyzing your scenario.

Contact us — order a consultation, and we will select the optimal architecture for your project. Get a preliminary cost and timeline estimate for free.