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Case Gelita

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Gelita

Customer Request Allocation with Artificial Intelligence

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In the competitive world of health and nutrition manufacturing, speed and precision can make or break a customer relationship.

When complex product formulations meet diverse client needs, traditional response workflows often struggle to keep up — leading to slower turnaround times, inconsistent proposals, and lost opportunities.

A leading B2B manufacturer set out to change that by introducing AI‑driven automation into its customer engagement process.
The goal: streamline formulation requests, boost accuracy through intelligent data insights, and empower sales and R&D teams with a single, smart interface for every customer inquiry.

Together with a specialized AI partner, the company built an advanced Product Finder Chatbot — a conversational engine designed to accelerate decision‑making, automate proposals, and deliver a richer customer experience from the very first interaction.

The develoed AI‑powered Formulation Assistant is serving as an intelligent layer between customers, sales, and product experts.

The chatbot combined conversational AI with predictive analytics to manage every stage of the process — from request intake to proposal generation.

  • Artificial Intelligence
  • Predictive Analytics
  • Automation
  • Data-driven Decision Making
Feature 01
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Formulation Recommendation Engine

AI models analyze historic formulations, ingredient data, and pricing trends to recommend optimal combinations and ensure consistent proposal quality.

Feature 02
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Sales Funnel Intelligence

Predictive analytics forecast opportunity success rates, expected revenue, and response times — helping sales teams allocate resources more effectively.

Feature 03
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Automated Proposal Generation

LLM based automation drafts technical formulations and customer proposals using verified internal and external data, reducing manual workload and improving delivery speed.

Achieved Results

The Impact

In just eight weeks, the new solution transformed how customer requests are handled. reducing manual effort, increasing visibility, and enabling true data‑driven decision‑making across teams.

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