Suppliers
United States
Provectus
Overview
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Provectus |
| United States | |
| Palo Alto | |
| 2010 | |
| Private | |
| $10-100m | |
| 201 - 1,000 | |
| Open website |
IoT Snapshot
Technology Stack
Case Studies
Number of Case Studies30
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Blue Bottle Coffee Enhances Customer Satisfaction with AWS Cloud Migration
Blue Bottle Coffee (BBC), a global coffee roaster and retailer, was facing significant challenges with its IT infrastructure. The infrastructure was insecure and distributed among several cloud providers, lacking a DevOps approach. This situation increased the Total Cost of Ownership (TCO) and slowed down the time to market for their products. BBC aimed to assemble and optimize the disjointed IT-infrastructure elements to increase the security of the entire ecosystem. They also wanted to implement Continuous Integration/Continuous Deployment (CI/CD) pipelines to accelerate and facilitate the deployment process by eliminating manual operations. Operational inefficiencies were causing high TCO and slow Time to Market (TTM), keeping BBC’s engineering team occupied with non-strategic tasks. BBC approached Provectus to prepare their IT-infrastructure for AWS migration, optimize and enhance its deployment process, and make BBC’s entire ecosystem more secure, which would allow the company to further spur its expansion, both in the USA and abroad. |
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Blue Bottle Coffee Enhances Ordering Accuracy and Reduces Waste with ML-Driven Demand Forecasting
Blue Bottle Coffee (BBC), a global coffee roaster and retailer, faced a significant challenge in managing the supply of pastries across its international network of cafes. The company was using a manual ordering system, where cafe leaders estimated the required quantity of pastries based on historical sales data, current inventory, and growth projections. This system was effective when BBC had a few cafes, but with over 70 cafes worldwide, it became inefficient and inaccurate. The inaccuracies led to either under-ordering, causing sell-outs and customer dissatisfaction, or over-ordering, resulting in food waste and profit loss. The suboptimal utilization of pastries was also affecting BBC's bottom line. Therefore, BBC needed a scalable, precise, and predictive ordering solution to improve pastry ordering accuracy, reduce food waste, and meet its sustainability goals. |
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Appen's Transformation: From Manual to Automated Fraud Detection with AI/ML
Appen, a leading provider of high-quality training data for AI systems, was facing a significant challenge in scaling its fraud detection mechanism. The company was using a partially automated but mostly manual system to detect and prevent malicious activity on their platform. This system, which relied on SQL and Python scripts, was not efficient enough to handle the increasing volume of work. Appen was struggling to monitor more than 50 jobs per day manually and considered hiring 20+ data analysts to keep up with the platform’s growth. The company needed a solution that would allow them to scale their fraud detection, increase the efficiency of their crowd workers, and attract new enterprise clients. The existing system also posed a challenge in terms of data quality, as it was prone to human error and could not efficiently eliminate low-quality contributions. |
