Opportunity
Sales specialists operate in a space where product complexity, business priorities, and customer expectations constantly intersect.
In practice, this often means:
Large and changing assortments that are difficult to keep in memory
Fragmented product information spread across tools and browser tabs
Missed opportunities to recommend alternatives that better serve both customer needs and business goals
Inconsistent performance driven by experience rather than shared guidance
This concept explores how AI can reduce that friction by supporting comparison, recall, and positioning without removing human judgment from the conversation.
Key Results
Reduced onboarding period length for new sales specialists
Higher sales performance baseline of part-time specialists
Consistency in sales performance for full-time specialists
Concept: AI-assisted sales support grounded in real product data
The intent is not to automate selling, but to make complex decisions easier and allow for more focus on the human connection. The Sales Assistant starts from a product the customer already prefers. From there, it supports the sales specialist by:
Providing the right sales choice
Machine Learning foundation to balance data inputs and improve.
Suggesting relevant alternatives that align better with business priorities
Presenting key product attributes in a single, easy-to-compare view
Flexibility to ensure recommendations meet customer needs
Coaching on how to sell any product
LLM comparison for a simple, human explanation of advantages and tradeoffs.
Answers questions on product and feature knowledge.
Only publicly visible product data sent to LLM for better simplicity, security, and costs.
Designed as a system, not just an interface
This concept focuses heavily on system design and data flow. Understanding how data, logic, and AI services interact was central to evaluating whether the idea could work in practice.
Flowchart #1: System & Data Architecture
An overview of how product data, pricing logic, and the LLM service interact to support recommendations, comparisons, and generated sales notes. The architecture is intentionally modular for simplicity and resiliency.
Flowchart #2: Product Selection Flow
A simplified flow showing how a customer’s preferred product becomes the starting point for identifying relevant alternatives. The logic balances similarity, availability, and predefined business criteria.
FlowChart #3: Suggested Pricing Flow
A focused view on how pricing data supports recommendations while remaining transparent and explainable. This helps ensure that suggestions can be trusted and discussed openly in a sales context.
