How dida Automates Sales Processes with Mathematics and Machine Learning
公司规模
Large Corporate
地区
- Europe
国家
- Germany
产品
- Google Cloud
- Google Maps Platform API
- Compute Engine
- TensorBoard
- Vertex AI
技术栈
- Machine Learning
- CI/CD pipeline
- Projective Geometry
- Google Cloud Storage
- GPUs
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Cost Savings
- Customer Satisfaction
技术
- 分析与建模 - 机器学习
- 应用基础设施与中间件 - 数据交换与集成
- 平台即服务 (PaaS) - 数据管理平台
适用行业
- 可再生能源
- Software
适用功能
- 销售与市场营销
- 商业运营
用例
- 预测性维护
- 过程控制与优化
- 远程资产管理
服务
- 软件设计与工程服务
- 系统集成
关于客户
Enpal is a leading greentech company in Germany, known for its innovative approach to providing solar energy solutions. As the first greentech unicorn in the country, Enpal has been experiencing rapid growth due to the rising demand for environmental sustainability solutions. The company specializes in offering solar panel installations to residential customers, aiming to make renewable energy accessible and affordable. Enpal's commitment to sustainability and innovation has positioned it as a key player in the renewable energy sector, driving the transition towards a greener future.
挑战
Enpal, a rapidly growing greentech company in Germany, faced challenges in scaling its solar panel sales process. The traditional method involved manually estimating roof angles and counting roof tiles, which was time-consuming and error-prone. This process took 120 minutes per customer, making it difficult to keep up with the increasing demand for solar panels. Enpal needed a more efficient and accurate solution to generate quotes for prospective customers, which led them to seek a custom AI solution to automate the process.
解决方案
Dida developed a custom AI solution for Enpal using Google Cloud's platform. The solution involved breaking down the sales process into smaller, modular steps, allowing for a more explainable and efficient system. Google Maps Platform API was used to gather rooftop images, which were stored in Cloud Storage. A machine learning model was trained to distinguish rooftops and calculate roof angles using projective geometry. Compute Engine with GPUs was utilized to accelerate the model training process, and TensorBoard was used to monitor the training. The final solution automated the calculation of roof size and solar panel requirements, significantly reducing the time and errors associated with the manual process.
运营影响
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