Solutions Built for a Purpose

Tech-driven companies join forces with Provectus to drive AI/ML initiatives, to improve time to innovation, and to develop cutting-edge solutions to outpace the competition.


01.Cloud Engineering

Cloud Migration

Deep knowledge of AWS technologies, products, and workloads enables us to drive risk reduction and accelerate time to market for migration of legacy to the cloud.


Microservices architecture including in-house recipes and quick starts for critical components such as deployment, monitoring, tracing, service discovery, application metrics sub-systems.


Optimization of development and delivery pipelines through continuous integration and continuous delivery practices. Enabling high-level data security in the cloud.


02.Big Data Engineering

Data Science Research

Scientific research to drive data mining, data modeling, machine learning, deep learning, and AI applications. Deep expertise in NLP, image recognition, anomaly detection, time-series analytics.

Big Data Platform

Streaming and batch big data platforms including stateful applications based on Apache Kafka and Kinesis streaming engines. Tuning and optimization of Apache Spark workloads.

Data & Analytics

Build free-flowing data pipelines for Big Data harvesting and processing to enable real-time analytics and to gain insight into hidden patterns, market trends, and customer preferences.


03.Artificial Intelligence & Machine Learning

ML-Aware Data Lake

Architect and build ingestion, processing, storage, and metadata capturing components of the feature lake for datasets versioning, reproducible machine learning experiments, and machine learning governance.

Machine Learning Infrastructure

Drive reproducible machine learning experiments, accelerate and simplify production deployments, improve velocity of Data Science teams, and ensure better visibility and auditability of the ML process.

ML Model Management

Automate deployment and serving of ML models, monitoring and profiling of production traffic, monitoring of models performance, data subsampling and model retraining.


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