Datachematic LAB offers a cloud‑based SaaS platform that enables researchers and manufacturers in chemistry, pharmaceuticals, food, and related fields to perform advanced data analysis and machine‑learning tasks without extensive coding. The service provides ready‑to‑use tools such as regression analysis and Bayesian optimization for material composition exploration and property prediction, allowing engineers to accelerate formulation development and improve product performance. Comprehensive support and security features are built in to suit each stage of the development workflow.
Funding
Funding not disclosed
Founders
Product
Problem
Material scientists and process engineers often lack accessible tools to apply advanced data analytics and machine learning to small‑ to medium‑sized experimental datasets, leading to slow material discovery, high development costs, and reliance on expert data‑science skills.
Solution
Datachemical LAB is a cloud‑based SaaS platform that enables users to perform regression analysis, Bayesian optimization, and other machine‑learning techniques without writing code. The service guides users through data preprocessing, model building, and prediction, turning experimental results into actionable insights. It supports a wide range of material classes—including chemicals, polymers, glasses, and composites—and integrates domain‑specific algorithms developed in collaboration with academic research labs. All computations run in the cloud while keeping user data on‑premise, ensuring data security and compliance. The platform also offers consulting and support tailored to each development stage, helping teams accelerate formulation design and reduce trial‑and‑error cycles.
Target Audience
Primary users are material scientists, chemists, and process engineers in chemical, pharmaceutical, food, electronics, and polymer industries, as well as research institutions developing new materials.
Features
- No‑code interface for regression, classification, and Bayesian optimization on 20‑30 to several thousand data points
- Automated experiment design that suggests optimal next‑run conditions and generates candidate formulations
- Material‑agnostic modeling that can learn chemical structures, predict target properties, and suggest novel compositions
- Real‑time sensor data integration for soft‑sensor estimation of hard‑to‑measure factors
- Built‑in workflow steps (preprocessing → model construction → prediction) with extensive statistical tools
- On‑premise data handling: raw data never leaves the user’s environment, meeting strict confidentiality requirements
- Tiered support and consulting services aligned with development phases, from early research to production scaling