PerkinElmer and Scitara join forces to create tech-driven ‘model of the modern lab’

Both companies will integrate their products to improve data management and automation in a laboratory setting  

The move will see PerkinElmer integrate its Signals Research Suite informatics platform with Scitara’s Scientific Integration Platform (SIP) to create a fully connected laboratory with enhanced data management and automation capabilities.  

PerkinElmer’s Signals Research Suite provides end-to-end scientific data and workflow management, while Scitara’s SIP provides a universal connectivity solution in a cloud native infrastructure. The deal aligns with PerkinElmer’s strategy of using its informatics offerings to drive digital transformation in the lab.  

Scitara’s CEO Ajit Nagral said the agile framework offered by the collaboration guarantees flexibility as standard.  

‘It provides the ability to extract maximum value from legacy equipment, while dramatically lowering the barrier to introduce new, innovative technology into the connected lab. We drive data aggregation to any selected customer repository in an agile, compliant and future-proof solution. 

A breakthrough level of automation – from the simplest to the most complex tasks – is achieved while complying with the most stringent requirements of regulated markets,' he said. 

Earlier this year Scitara raised $15 million in Series B funding, which analysts say was fuelled by the success of its SIP.  

Speaking about the collaboration, Kevin Willoe, VP and General Manager, Informatics at PerkinElmer, said merging the two services will create a ‘truly integrated laboratory environment.’ 

‘This will help tie together instruments, software and systems and support our customers in curating data in a standardized platform. This is the power of the lab of the future in action,’ he said.  

Both companies say integrating the SIP infrastructure with the PerkinElmer Signals Research platform will allow for improved data management, enhanced collaboration and more lab automation. Integrated data science can be used in tandem with AI and analytic tools, providing scientists with faster insights, fuelling decision making and ultimately accelerating products to market.