Download PDF
Provectus
Overview
HQ Location
United States
Year Founded
2010
Company Type
Private
Revenue
$10-100m
Employees
201 - 1,000
Website
Twitter Handle
Company Description
Provectus is an Artificial Intelligence consultancy and solutions provider, helping businesses achieve their objectives through AI.
Provectus is an AWS Premier Consulting Partner with competencies in Data & Analytics, DevOps, and Machine Learning. The company designs and builds AI solutions for industry-specific use cases, Data and Machine Learning foundation, Cloud transformation, and DevOps adoption.
IoT Snapshot
Provectus is a provider of Industrial IoT analytics and modeling, cybersecurity and privacy, platform as a service (paas), infrastructure as a service (iaas), networks and connectivity, application infrastructure and middleware, sensors, and functional applications technologies, and also active in the buildings, cement, construction and infrastructure, education, equipment and machinery, finance and insurance, food and beverage, healthcare and hospitals, life sciences, mining, national security and defense, oil and gas, pharmaceuticals, recycling and waste management, retail, telecommunications, and transportation industries.
Technologies
Analytics & Modeling
Computer Vision Software
Machine Learning
Natural Language Processing (NLP)
Predictive Analytics
Real Time Analytics
Infrastructure as a Service (IaaS)
Cloud Computing
Cloud Middleware & Microservices
Cloud Storage Services
Use Cases
Behavior & Emotion Tracking
Chatbots
Clinical Image Analysis
Computer Vision
Construction Management
Demand Planning & Forecasting
Fraud Detection
Infrastructure Inspection
Intelligent Packaging
Last Mile Delivery
Leasing Finance Automation
Livestock Monitoring
Machine to Machine Payments
Machine Translation
Movement Prediction
Object Detection
Predictive Maintenance
Predictive Replenishment
Predictive Waste Reduction
Real-Time Location System (RTLS)
Retail Store Automation
Tamper Detection
Time Sensitive Networking
Vehicle-to-Infrastructure
Virtual Training
Visual Quality Detection
Functional Areas
Industries
Services
Technology Stack
Provectus’s Technology Stack maps Provectus’s participation in the analytics and modeling, cybersecurity and privacy, platform as a service (paas), infrastructure as a service (iaas), networks and connectivity, application infrastructure and middleware, sensors, and functional applications IoT Technology stack.
-
Devices Layer
-
Edge Layer
-
Cloud Layer
-
Application Layer
-
Supporting Technologies
Technological Capability:
None
Minor
Moderate
Strong
Case Studies.
Case Study
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.
Case Study
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.
Case Study
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.
