AI models are powerful, but their technical descriptions are often incomprehensible to non-engineers. Sales teams especially struggle to explain AI capabilities to clients without oversimplifying or misrepresenting them.
In this case study, I translated a highly technical object detection model description into clear, actionable language for a sales audience — demonstrating how to bridge the gap between engineering complexity and business communication.
The Challenge
Here’s the original technical paragraph from the engineers:
Our proprietary detection framework implements a multi-scale feature pyramid network with deformable convolutions and focal loss optimization. The backbone utilizes an EfficientNet-B4 architecture pretrained on ImageNet, fine-tuned using mixed precision training with the AdamW optimizer. We've achieved state-of-the-art mean Average Precision (mAP) of 0.87 on the internal benchmark dataset, with inference latency of 17ms on our edge hardware, making it suitable for real-time detection tasks in constrained computational environments.
As you can see, this paragraph is dense with jargon and metrics — accurate but completely inaccessible to a sales team.
My Approach
- Identify the audience: Sales team members needing clarity and confidence to explain AI to clients.
- Focus on key aspects: Accuracy, speed, and real-world limitations.
- Translate step by step: Rewrite each sentence in plain, conversational language without losing meaning.
- Add a visual analogy: Something memorable to help explain how the model works.
- Create a translation glossary: Simplify recurring technical terms for easy reference.
The Result
Sales-Friendly Rewrite:
This object detection model has a high reliability rate for real-time use and is designed to assist the average human when driving. It reacts fast enough to keep up with real-world driving conditions in supported environments.
Visual Analogy:
It’s like an extra pair of eyes that assists you in identifying objects in real time while driving.
Translation Glossary:
- Multi-scale Feature Pyramid Network → Lets the system notice both big and small objects at the same time.
- Deformable Convolutions → Helps the system adjust to unusual or stretched shapes so it can recognize them better.
- EfficientNet-B4 Backbone → The “engine” of the system that efficiently extracts important details from images.
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