This company provides data-driven solutions to accelerate chemical process development and optimization. They generate FAIR, machine-actionable reaction data and utilize computational models to deliver rapid solvent screening and intrinsic kinetic modeling. Their services enable clients to de-risk scale-up by accurately predicting process performance before committing to production.
Funding
$990K 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
Chemical process development suffers from a lack of comprehensive data on process conditions, hindering accurate predictions and efficient optimization. Traditional data collection methods are often slow and expensive, especially for understanding solvent effects, while theoretical models can be inconsistent with experimental results.
Solution
SOLVE employs advanced data collection and machine learning techniques to optimize chemical processes, enabling clients to develop more efficient and sustainable operations. The company's approach provides real-world data and predictive models that reduce uncertainty in equipment selection and accelerate time-to-market for new products. SOLVE's methods facilitate the implementation of more cost-efficient and sustainable process conditions, reducing operating costs and increasing sustainability. The generated data and models also help justify condition choices to regulators, easing regulatory burden and demonstrating trade-offs.
Target Audience
SOLVE's primary customers are companies in the chemical industry, including those in pharmaceuticals and agrochemicals, seeking to optimize their chemical processes for efficiency and sustainability.
Features
- Data collection and machine learning approaches tailored to specific chemical processes.
- Predictive models that inform equipment selection, reducing uncertainty and risk.
- Methods for implementing more efficient and sustainable process conditions.
- Efficient data collection to reduce process development time cycles.
- Real-world data and models to justify condition choices to regulators.
- Automated optimization of multi-step, multi-phase continuous flow processes.
- Operator-free HPLC automated method development guided by Bayesian optimization.