Biostate.ai’s core mission is to combine cutting-edge RNA sequencing technology (as well as other omics tech) with artificial intelligence to predict disease progression, drug responses, as well as in the long term create custom therapy and therapeutics. In all of this, the first step is to get massive amounts of data, to do that we need to reduce the cost of data collection massively and this is the first pillar, focusing on developing proprietary methods that make RNA sequencing (and other omics tech being worked on) orders of magnitude more affordable while maintaining high quality, allowing researchers and healthcare providers to collect more comprehensive data. This allows our AI tools to analyze this data to provide insights that weren’t previously possible, helping to guide treatment decisions and develop more personalized approaches to medicine.

Unlike companies that focus on just one part of the process, we’ve built an integrated system that handles everything from sample processing to AI analysis and insight generation. We achieve this integration by adopting AI, automation, and novel chemistry that leverage all of these elements together. BIRT and PERD are our biochemistry innovations that reduce RNA sequencing costs by 50-70% compared to traditional methods. OmicsWeb, Copilot, and QuantaQuill are our AI tools that transform how researchers analyze and interpret data. Our lab has also embraced automation to accelerate our workflows and increase throughput. This vertical integration creates efficiencies and insights that fragmented approaches simply can’t match.

We’re addressing three fundamental challenges in healthcare and biotech: (a) reducing the cost of omics technologies, starting with RNAseq but with more innovations coming soon, to enable data collection at unprecedented scale; (b) implementing AI in every aspect of biotech to massively accelerate research pace and insight generation; and (c) developing clinical insights drawing inspiration from how LLMs were developed. Traditional RNA sequencing is expensive (around $350 per sample), forcing researchers to limit their studies and preventing the collection of the massive datasets needed for effective AI. Additionally, the data requires specialized expertise to analyze, creating bottlenecks in research. We’ve solved these problems by drastically reducing costs, standardizing data collection to eliminate technical variation, and developing AI tools that allow non-experts to analyze complex data through natural language queries—essentially democratizing access to powerful biological insights.

Our technology benefits diverse groups across the healthcare and research ecosystem:

Academic Research Labs: Successfully sequence difficult samples like degraded FFPE tissue at 50-70% lower cost while enabling non-computational scientists to conduct sophisticated analyses without specialized expertise.

Pharmaceutical R&D: Deliver consistent, high-quality RNA-seq data across large cohorts for biomarker discovery while applying our pre-trained models to stratify patients for clinical trials.

Biobanks and Clinical Research Organizations: Enhance value of stored tissue collections through cost-effective sequencing while eliminating batch effects in multi-site studies.

Clinical Oncology Research: Capture disease dynamics missed by DNA-only approaches and develop models for treatment response and relapse risk beyond traditional markers.

Biotechnology Startups: Stretch limited research budgets by providing 3-5x more transcriptomic data for the same cost while eliminating the need for specialized bioinformatics staff.

Ultimately, patients benefit as these advances lead to more personalized and effective healthcare approaches.

We train our AI similar to how large language models like GPT are trained. First, we pre-train on massive amounts of unlabeled RNA data, allowing the AI to learn the “grammar” of biology—patterns related to age, sex, pathway activity, and disease states. Then we fine-tune for specific applications with much smaller labeled datasets. In some of our internal studies, this approach improved prediction accuracy from 65% with traditional machine learning to 89% with our method. This two-stage approach means we can build powerful models with much less labeled data than traditionally required, which is particularly valuable for developing diagnostics for rare conditions and situations where samples are difficult to obtain.

OmicsWeb Copilot is our AI assistant that allows researchers to analyze complex RNA data using simple, natural language questions instead of complex code. For example, you could ask “Which genes are overexpressed in my treatment group compared to control?” and Copilot will perform the appropriate statistical analysis and visualization. This eliminates the need to wait for specialized bioinformaticians, who are in short supply, and democratizes access to sophisticated analysis for scientists of all backgrounds. Combined with our QuantaQuill tool, which helps draft scientific reports and manuscripts, we’re dramatically accelerating the research cycle.

OmicsWeb is our comprehensive data lake that allows researchers to securely store their omics data in the cloud. It provides automated data analysis capabilities, seamless integration with Copilot for custom analyses, and tools to safely prepare and transform data. Researchers can easily consolidate their data with other datasets or public repositories to create robust training datasets. In the future, we plan to enable direct model training within OmicsWeb itself, creating a complete environment for omics data management, analysis, and AI development—all without requiring specialized computational infrastructure or expertise.

We’re building what we call the “Netflix for molecular medicine” through a multi-phase approach. We begin with revolutionary RNA sequencing services that generate immediate revenue while building our data assets. Next, we’ll develop clinical tests that predict disease outcomes and treatment responses. Ultimately, we aim to enable truly personalized “N-of-1” therapeutics tailored to individual molecular profiles, transforming healthcare from reactive to predictive and from one-size-fits-all to truly personalized medicine. Each phase funds and de-risks the next, creating a sustainable path to our vision.

We’re developing comprehensive data security protocols aligned with best practices in the industry. Our approach includes implementing secure infrastructure, encryption technologies, and protected processing pipelines. As we expand globally, we’re building systems that respect various regional data requirements while still enabling scientific progress. We’re also developing sophisticated federated learning approaches that will allow us to learn from distributed data without compromising privacy or regional data sovereignty requirements. These solutions are evolving alongside our technology to ensure we maintain the highest standards of data protection as we scale.

While DNA remains largely static throughout your life, RNA expression changes dynamically in response to disease, drugs, and environmental factors. RNA serves as a real-time readout of your biological state—what’s actually happening in your cells right now. DNA can tell you about inherited risks, but RNA reveals how those risks are currently manifesting. Additionally, RNA analysis is much more cost-effective than proteomics (analyzing proteins), which can only measure about 10% of proteins at costs around $1 per data point, compared to less than $0.001 per data point with our RNA technology.

Absolutely! We’re actively exploring multiple omics technologies including proteomics, single-cell analysis, methylation profiling, whole genome/exome sequencing, and metabolomics. As we achieve our milestone of reducing costs and enabling data collection at massive scale with RNAseq, we’ll begin introducing these additional technologies to our customer base. These efforts are ongoing, and we welcome discussions about potential collaborations in these areas. Our ultimate goal is to create a multi-omics platform that provides the most comprehensive view of biological systems possible, with the same focus on cost reduction, quality, and integrated AI analysis that we’ve pioneered with RNA sequencing.

Getting started is simple! Contact us through the form on our website, and our team will discuss your specific needs. For research institutions and companies interested in RNA sequencing services, we’ll guide you through sample requirements and provide a customized quote based on your project scope. For those interested in our AI tools, we offer demonstrations of OmicsWeb, Copilot, and QuantaQuill to show how they can accelerate your research. We also actively seek clinical partnerships across various disease areas and are happy to discuss potential collaborations that align with your institution’s focus.