B2B Sales & Quoting Automation: Lightning-Fast Offers and Zero Lost Leads with AI

Overview

The ClientThe client is a film industry company that sells film rights licenses to B2B buyers, largely at international industry markets.

Objective: radically reduce the time from inquiry to offer and maximize the conversion of leads acquired at key industry markets. Instead of waiting days for a quote, the client receives a precise proposal in a matter of hours.

The Challenge

The rights sales process relied on fragmented knowledge, creating significant bottlenecks:

• Slow QuotingComplex licensing meant pricing and catalog curation took days, leading to the loss of "hot" market leads.
• Rights Collision RiskManual verification of territories, licenses, and trade policies carried the risk of offering rights that had already been sold.
• Lack of Scalable PersonalizationSending generic offers failed to address the specific preferences of individual buyers.

The Strategy

We approached the problem as an architectural challenge.

Instead of simplifying the product itself, we built a cohesive AI-supported ecosystem of supply and demand data:

• Knowledge CentralizationIntegrating a dedicated rights management system with the CRM.
• Hyper-PersonalizationShifting the perspective from "company-wide offers" to "individual buyer profiles."
• Human-in-the-Loop OversightDelegating the heavy analytical lifting (matching, pricing, drafting) to AI, while maintaining final human validation.

Licensing complexity is a feature of the product, not a flaw in the process. Territories, licenses and trade policies stayed, and a data and rules layer took over handling them. This is why the same system cuts quoting to hours. Simplifying the product would have shortened the process, but it would also have narrowed what can be sold.

The Solution

We built an integrated business environment based on three core pillars:

1. 1

   Complex Supply and Demand Data Model

   We built a comprehensive database combining insights on over 2,500 buyer contacts (enriched with external market data) with a complex sales structure: licenses, territories, and trade policies.

2. 2

   Extreme Matching (1:1 Offer)

   We applied precise scoring and business rules that match the catalog directly to the individual buyer's purchasing profile, rather than just the organization they work for.

3. 3

   AI Quoting and Negotiation Agent

   An advanced system autonomously prepares pricing, commercial terms, and draft offer content. The agent handles the heavy analytical work, delivering a ready-to-send document to the sales rep for final verification.

The sales rep does not build an offer from scratch. They receive pricing, commercial terms and a draft text from the agent, prepared from rights that passed the availability check. Their job is to review, correct and decide whether to send. This is how Human-in-the-Loop works in this implementation: AI handles matching, pricing and drafting, and a person always has the final word before an offer goes out.

The Impact

By implementing automated quoting and AI models into a dedicated rights system, we achieved lightning-fast response times with zero compromise on quality.

For the sales team, these numbers mean a different working rhythm after a market. Offers in hours and follow-up no later than 24h after the market make it possible to reply to a buyer before the meeting goes cold. Half an FTE saved, emails to 300 clients and responsiveness and follow-up raised to 80% show that scale grows without overloading the sales team.

How to implement it and what to watch for