Scailyte provides an AI‑powered platform that integrates large‑scale single‑cell and spatial omics data with longitudinal clinical outcomes to identify disease‑driving cellular states and predictive biomarkers for autoimmune disorders. Using proprietary supervised representation learning, the platform delivers unbiased molecular patterns and high‑resolution patient endotypes, enabling pharma partners to accelerate target discovery, diagnostic development, and precision patient stratification.
Funding
$1.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Pharmaceutical R&D for autoimmune diseases, especially Inflammatory Bowel Disease (IBD), relies on bulk omics data that masks cellular heterogeneity, leading to high non‑response rates to biologic therapies and a therapeutic ceiling. Identifying disease‑driving cellular states and predictive biomarkers from complex single‑cell and spatial datasets remains difficult and time‑consuming for drug developers.
Solution
Scailyte offers an AI‑powered platform that integrates large‑scale single‑cell and spatial omics data with longitudinal clinical outcomes to uncover disease‑driving cellular states without predefined cell clusters. Its proprietary supervised representation learning method extracts unbiased molecular patterns linked to treatment response, enabling precise patient endotyping and biomarker discovery. The platform translates these insights into clinically actionable models for diagnostics, therapeutic target identification, and manufacturing quality control. By delivering high‑resolution cellular maps and predictive analytics, Scailyte helps pharma partners accelerate precision‑medicine development and overcome current efficacy limits in autoimmune therapies.
Target Audience
Primary customers are pharmaceutical and biotech companies developing autoimmune and inflammatory disease therapies that require high‑resolution cellular insights for target discovery, biomarker development, and precision patient stratification.
Features
- Supervised representation learning that identifies molecular patterns without requiring pre‑defined cell type clusters
- Integration of multimodal single‑cell, spatial omics, and longitudinal clinical outcome data
- Generation of predictive models for diagnostics, therapeutic target validation, and manufacturing QC markers
- Proven use cases including a B‑cell subpopulation marker for NK cell therapy manufacturing, a tissue‑based diagnostic for endometrial lesions, and a high‑accuracy blood‑based diagnostic for CTCL
- Scalable platform architecture supporting large patient cohorts (e.g., 500+ IBD patients, 5 M cells) and rapid hypothesis testing
- Exportable assets with patent protection and ready for licensing or strategic partnership