Asking Bigger in Manufacturing

Mass production of semiconductors, chips, monitors, and other sensitive devices is being tracked today by hundreds and thousands of PLC logs and IoT sensors that are collecting big data for manufacturing directly from the production floor. These sensors are generating massive amounts of data which is being used for FDC (Fault Detection and Classification) and creating models to predict production errors before they occur. Data engineers can choose if they want to work on a small percentage of the data or run queries on the entire dataset, making their model more accurate which leads to increasing yield.

Contact us
scroll
down
arrowDown

Fault Detection & Classification at Scale

Budget_Icon_on_blue

Dramatic Cost-Savings

$12M yearly saving from more efficient data collection, loading, and reporting

Lowest_TCO_Icon_on_blue

Substantially Faster Insights

88%-99% reduction in loading and reporting time

Budget_Icon_on_blue

Exponentially Growth of Business

Yield increase from below 50% to 90%

Easily integrate with you exiting data stack
integrations-icon (1)
hitachi-vantara-logo
Weka
integrations-icon (8)
Rectangle 2428 (3)
f3f5c080-808b-11ea-9713-2bea65875d95
tensorFlow
integrations-icon (7)
integrations-icon (5)

Leading operators from all around the world rely on SQreamDB

Sinch logo
mobicom
lg-logo (1)
ais
Alibaba_com-logo-C45A9AADBD-seeklogo.com__1_-removebg-preview
liveaction-logo
pubmatic-logo
amdocs-logo
Vodaphone

Learn more

Case study
January 8, 2023

Enabling Peta-Scale Anomaly Detection in Manufacturing

Ebook
sqream db whitepaper
November 23, 2022

A Look inside SQreamDB

Case study
December 12, 2022

Electronics giant improves yield from 50% to 90%