Rombo AI
Rombo AI provides an AI platform that converts benchtop NMR scans into immediate quality predictions for industrial chemical analysis. Its ROSE foundation model, pretrained on 3.2 million spectra, predicts full quality profiles in real time, replacing slow, multi-method lab workflows. Deployments are benchmarked against ASTM or IEC reference methods with metrics agreed before go-live.
- Chemical Technology
- Artificial Intelligence
- Data & Analytics
- Manufacturing / Industry 4.0
- Software Only
- Clean Technology
- Energy
- Hardware
- Industrial Automation
- New Materials
Funding
Founders
Founder details are not available yet.
Product
Problem
Industrial quality control relies on slow, lab-based methods like GC, distillation, and Karl Fischer, each requiring separate instruments, operators, and queues. Every crude cargo, oil sample, or solvent stream is unique, so yesterday's analysis does not describe today's material, forcing decisions to wait for the lab.
Solution
Rombo AI provides an AI platform that turns benchtop NMR scans into immediate quality predictions, eliminating the need for multiple lab methods. Users run a simple NMR scan with minimal sample preparation, and the platform's foundation model, ROSE, predicts the full quality profile in real time. ROSE is pretrained on 3.2 million experimental, simulated, and benchtop proton NMR spectra, enabling it to generalize across different instruments and field strengths. The platform delivers traceable reports, tracks drift over time, and integrates with enterprise systems for seamless decision-making. Deployments are benchmarked against ASTM or IEC reference methods with metrics agreed before go-live, ensuring industry-standard validation.
Target Audience
Primary customers are industrial chemical companies, refineries, and quality control laboratories that need rapid, accurate analysis of crude oil, solvents, and other chemical streams without relying on slow, multi-method lab workflows.
Features
- ROSE foundation model pretrained on 3.2 million spectra with a multi-B0 pairing objective that aligns representations across field strengths
- Identity-level holdout evaluation ensures predictions transfer to unseen molecules rather than memorizing training data
- Supports low-field classification, structure-to-spectrum prediction, peak detection, denoising, and spectrum-to-structure retrieval
- Benchmarked against ASTM or IEC reference methods with agreed-upon metrics before go-live
- Cloud-based analytics with traceable reports, drift tracking, and enterprise system integration
- Open-source model weights and code available for research and deployment