Company: BLLN
Filing Date: 2025-10-07
Form Type: S-1
Source: 0001193125-25-233697
Chunk: 198

Company: BillionToOne, Inc.
Filing Date: 2025-10-07
Form: S-1
Chunk 198
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. The scalability of our tests has been built over time through automation, which has enabled us to maintain single molecule sensitivity while driving down COGS. While the vast majority of our laboratory processes are now fully automated, it was accomplished through a deliberate step-by-stepapproach of continuous implementation. In addition to the smNGS technology that we have developed, we believe that the current scalability of our assays also gives us a multi-year advantage. For example, it took an entire year to automate one single step of our sgNIPT assay, automated cfDNA extraction with the same yield as manual column-based extraction, to maintain the same single molecule sensitivity. Once a sample is received at one of our facilities, it is processed through a single-directional workflow across four separate labs, each with strictly separated and segregated airflows and air filtration systems. Every step in the laboratory process is designed to optimize yields and support a high-quality testing process. By leveraging smNGS techniques and strictly controlling the workflow and environment, we can detect sample cross-contamination at the single molecule level and even determine the exact point in the process where the contamination occurred, including potentially at the clinic where the sample was collected. Even if QCTs were deployed in a traditional NGS lab, post-PCRcontamination that is typically found in such labs would preclude the single molecule sensitivity, absent a complete redesign of not only the assays and bioinformatics, but of the entire laboratory infrastructure. We have also utilized automation and AI throughout our infrastructure to reduce errors and increase laboratory throughput. By automating our labs, we are able to build uniformity into sample processing and can rapidly identify and triage problems as they occur. Each individual lab contains specialized equipment with bespoke programming to drive specific processes and to progress the sample through the testing workflow. In addition, we also integrate third-party large language models and AI to improve our laboratory workflows. For example, we completely redesigned and automated our biological sample accessioning process by incorporating AI and computer vision that automates labor-intensive clerical tasks. This allowed us to redeploy our laboratory personnel to higher impact areas. As a technology-forward organization, we have decreased our Overall Cost Per Test by more than 21% in the 24 months ended June 30, 2025, in part by deploying automation and AI. While our current laboratory space is already sufficient for at least four times more growth, our process engineering and automation teams continue to work on creative solutions to further scale our laboratory capacity and decrease our COGS. 140

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