Using AI to Generate Personalized Commercial Proposals
In today’s competitive market, speed, precision, and personalization are no longer optional — they’re the foundation of successful sales. Businesses are increasingly turning to artificial intelligence (AI) to streamline workflows, reduce manual effort, and deliver tailored experiences at scale.
Where sales managers once manually tracked competitor pricing, customized terms for each client, and drafted commercial proposals (CPs) from scratch, AI now automates much of this process — without sacrificing quality or personalization.
One of the most powerful approaches combines Large Language Models (LLMs) with Retrieval‑Augmented Generation (RAG). This pairing not only accelerates proposal creation but ensures each offer is grounded in real‑time data, client history, and proven sales strategies.
Where sales managers once manually tracked competitor pricing, customized terms for each client, and drafted commercial proposals (CPs) from scratch, AI now automates much of this process — without sacrificing quality or personalization.
One of the most powerful approaches combines Large Language Models (LLMs) with Retrieval‑Augmented Generation (RAG). This pairing not only accelerates proposal creation but ensures each offer is grounded in real‑time data, client history, and proven sales strategies.
What Can Be Automated
With AI, repetitive sales operations become faster, more accurate, and more consistent:
- Client segmentation by purchase history, order volume, frequency, and price sensitivity.
- Proposal drafting that reflects each client’s preferences — favorite products, frequently ordered categories, budget range.
- Dynamic pricing and offers with optimal discounts, payment terms, and delivery conditions.
- Tone adaptation to match the client segment — from formal to conversational.
- CRM/ERP integration for instant data retrieval and one‑click proposal generation.
From Data to a Ready‑to‑Send Proposal
1. Data Collection & Preparation
Automation starts with high‑quality data.
The system aggregates each client’s purchase history — SKUs, volumes, order dates, sales channels, average spend, and lifetime value.
It then calculates price sensitivity — how responsive the client is to discounts in specific categories. This enables segmentation into:
2. The RAG + LLM Architecture
RAG enriches the LLM’s generative capabilities with up‑to‑date, context‑specific data.
Retrieval
Prompt Creation
“You are a sales manager preparing a CP for client {Name}. Their history: {list of SKUs, volumes, dates}. They often order {categories}, prefer a budget of {min}–{max}. Recommended margin: {value}.”
Generation
Post‑Processing
Example of Personalization in Action:
If a client frequently purchases professional shampoos and responds to discounts in the “hair care” category, the model might suggest:
“Alongside your favorite shampoos, we’ve prepared a special offer on hair masks and serums — 20% off for orders placed by the end of the month.”
3. Automation & Integration
A “Generate CP” button in the CRM triggers the LLM via API, returning a ready‑to‑send proposal with metadata such as generation time and confidence score.
Automated triggers can then send proposals based on events — for example, when a forecast changes or a discount threshold is reached.
4. Measuring & Improving Performance
Key metrics track the system’s impact:
Feedback loops from managers and clients refine prompts and continuously improve the model.
Automation starts with high‑quality data.
The system aggregates each client’s purchase history — SKUs, volumes, order dates, sales channels, average spend, and lifetime value.
It then calculates price sensitivity — how responsive the client is to discounts in specific categories. This enables segmentation into:
- Price‑sensitive — respond strongly to promotions.
- Loyal — purchase consistently without heavy discounting.
2. The RAG + LLM Architecture
RAG enriches the LLM’s generative capabilities with up‑to‑date, context‑specific data.
Retrieval
- Client profiles are stored as vector embeddings in a knowledge base.
- On request, the system retrieves the top‑K most relevant facts: purchase history, segment, recent interactions, price sensitivity.
Prompt Creation
- A structured template guides the LLM, for example:
“You are a sales manager preparing a CP for client {Name}. Their history: {list of SKUs, volumes, dates}. They often order {categories}, prefer a budget of {min}–{max}. Recommended margin: {value}.”
- Dynamic fields include:
- frequent SKUs;
- recommended new products;
- target margin;
- payment and delivery terms.
- Examples of successful past proposals are added to align tone and structure.
Generation
- The LLM produces a headline, offer details, and terms.
- Automated checks ensure compliance with business rules — minimum margins, maximum discounts.
Post‑Processing
- Real product links, SKUs, images, and BI‑generated visuals are inserted.
- The final, client‑ready document is produced.
Example of Personalization in Action:
If a client frequently purchases professional shampoos and responds to discounts in the “hair care” category, the model might suggest:
“Alongside your favorite shampoos, we’ve prepared a special offer on hair masks and serums — 20% off for orders placed by the end of the month.”
3. Automation & Integration
A “Generate CP” button in the CRM triggers the LLM via API, returning a ready‑to‑send proposal with metadata such as generation time and confidence score.
Automated triggers can then send proposals based on events — for example, when a forecast changes or a discount threshold is reached.
4. Measuring & Improving Performance
Key metrics track the system’s impact:
- Email open and click‑through rates.
- Conversion to orders and average order value.
- Performance uplift versus control groups.
Feedback loops from managers and clients refine prompts and continuously improve the model.
Conclusion
Combining AI with LLM and RAG creates a “virtual salesperson” — an intelligent assistant that delivers highly personalized, data‑driven proposals at scale. This not only speeds up sales cycles and boosts conversion rates but also ensures every offer is relevant, timely, and strategically aligned.
Beyond personalization and speed, the benefits are strategic:
Looking ahead, LLM + RAG can integrate with auto‑ordering systems to monitor stock, summarize key business metrics, and analyze supplier reports — flagging risks like delivery delays or changing terms before they impact operations.
Bottom line: Implementing AI in the form of LLM with RAG isn’t just a tech upgrade — it’s a step toward a self‑learning, adaptive sales ecosystem that unites analytics, automation, and personalization.
Beyond personalization and speed, the benefits are strategic:
- Adaptability — instantly adjusting offers to market shifts and client changes.
- End‑to‑end integration — connecting with ERP/CRM and BI tools to track proposal performance in real time and feed results back into model training.
- Scalable best practices — using a vector knowledge base to replicate winning strategies across regions and segments.
Looking ahead, LLM + RAG can integrate with auto‑ordering systems to monitor stock, summarize key business metrics, and analyze supplier reports — flagging risks like delivery delays or changing terms before they impact operations.
Bottom line: Implementing AI in the form of LLM with RAG isn’t just a tech upgrade — it’s a step toward a self‑learning, adaptive sales ecosystem that unites analytics, automation, and personalization.