Seeq, a leader in industrial analytics and AI, and Swan-Black, an operating affiliate of GS Systems, jointly announce the appointment of Swan-Black as the first Seeq Master Industry VAR (MIV) partner, specializing in the Food and Beverage industry sectors.

This new partnership designation is reserved for select partners that possess the highest level of Seeq maturity and commitment to customer success in a dedicated industry vertical. This advancement is a testament to the fruitful collaboration between Seeq and Swan-Black, acknowledging Swan-Black's unparalleled expertise in this field.

"We are thrilled to become Seeq's Master Industry VAR for the Food and Beverage industry vertical," says Joseph Gardner, President at Swan-Black.

"This recognition validates the positive impact we have on our customers globally. We believe a collaborative partnership, one that combines our Seeq expertise and leverages our knowledge of common process data challenges in the industry, can significantly accelerate our customers' time to desired outcomes. Whether it's a challenge related to quality improvement, batch process optimization, or simply lowering the cost of curiosity, Seeq has proven to be an exceptional platform to help solve these problems for our customers."

Swan-Black has a proprietary, proven customer engagement model, enabling the identification and realization of impactful applications for Seeq. These applications yield tangible results, including cost savings, improved production outcomes, sustainability advancements, and workforce development opportunities for customers.

"Seeq is pleased to continue our momentum with Swan-Black by appointing them as our first Master Industry VAR," said Megan Buntain, Chief Market Officer at Seeq. "From significant global account expansions and wins to continued delivery of top-tier customer support, Swan-Black consistently demonstrates the partner excellence required of this new partner designation."

Seeq delivers a self-service, industrial analytics and AI platform that accesses and leverages vast amounts of historically underused data.