Pairwise’s mission is to build a healthier world through better fruits and vegetables. Each new innovation, whether a pitless cherry or a more nutritious leafy green, starts as an idea. The team uses CRISPR to turn these ideas into plants, which are grown in a greenhouse, then molecularly tested for the desired phenotype and genotype. Throughout this process, the team has to manage high complexity workflows and high throughput data generation.
# of employees: 51–250
Industry: Agriculture, Plant Tech
Location: Durham, NC, USA
Use CRISPR and gene editing to develop new varieties of fruits and vegetables for consumer purchase.
Difficulty Tracking Large Amount of Samples
Plants don’t generate genetically identical progeny. Each plant has its own phenotypic and genotypic characteristics, and must be tracked as an individual. Thus, sample sets often contain hundreds of entities, each with its own optimization needs and custom steps — two orders of magnitude larger than a typical biopharma sample set.
Lacking A Central System to Organize and Communicate Data
Compared to cell-based workflows, plant-based workflows require more complex infrastructure, more team handoffs, and longer timelines. Pairwise needed a centralized, easily-accessible way to organize and communicate experiment data to every team, from discovery to development.
Lack of Aggregated Metrics for Decision Making
Program leaders needed to turn thousands of data points into insights to drive decision making. They needed pre-computed reports aggregating metrics for successful plants each week to move forward with.