Rombo AI Platform
Turn benchtop NMR data into material property predictions. Start with a free review of your samples, reference methods and validation goals.
Read our published refinery case study to see the analytical challenge and evaluation approach. Results depend on sample coverage, reference data and validation conditions.
Planning a model evaluation? See what to prepare for an NMR AI feasibility review.
Start with your sample, the decision you need to make and the evidence required to validate it.
Problem. Purchasing and blending decisions cannot always wait for a full crude assay.
Data. Benchtop NMR measurements, representative crude samples and reference assay results.
Outcome to evaluate. Assess property prediction against your laboratory methods before a focused pilot.
Explore crude oil and blends → Discuss feasibility →Problem. Asset teams need comparable evidence of oil condition across samples and time.
Data. Oil spectra, sample history and reference measurements of condition.
Outcome to evaluate. Evaluate whether spectral changes can support your maintenance decisions.
Explore transformer oil → Discuss feasibility →Problem. Manual candidate review creates a bottleneck in research and impurity investigations.
Data. NMR data and available sample context, with expert review of candidate structures.
Outcome to evaluate. Use Spectra to support candidate review; confirm assignments with your chemists.
Explore nmr compound identification → Discuss feasibility →In oil & gas, petrochemicals, chemicals and energy, material quality determines how a product will perform. Yet accurate characterization is still slow and hard to scale:
In industrial contexts, while process indicators may look stable to QC operators, the chemical change and material shift may already be happening. The problem is not the measurement — it's the lack of early signals, with critical deviations leading to significant production loss.
What you gain from deploying Rombo AI Platform in your operations.
Rombo AI Platform brings NMR acquisition and domain-specific AI into industrial workflows, with traceability, reproducibility, local validation, and expert review as first-class requirements.
Build a consistent, explainable material state baseline. Detect drifts, chemical regime changes, and early signs of irreversibility.
NMR AI Analyzer and AutoML Framework are modules of Rombo AI Platform. Each production workflow is validated on relevant instruments, samples, and operating conditions.
Module 1
Discover the unique AI + Spectroscopy power. NMR AI Analyzer is designed for industrial labs where traceability and comparability matter. The software controls the NMR data acquisition, AI model integration, report generation, and integration with enterprise systems.
Key Features:
Module 2
Our AI expertise, at your service. As an optional module, we enable R&D teams to build their own internal ML modules and custom pipelines, while retaining 100% Intellectual Property and utilizing their own data. It supports model selection, validation, and comparison — grounded in experimental datasets.
Technology
Rombo AI Platform is powered by a spectral foundation model trained to learn reusable representations from NMR data. This shared foundation supports faster adaptation across analytical tasks while each production use remains subject to validation on the relevant instruments, samples, and operating conditions.
How we work
We first assess the analytical problem, available data or samples, and target decision. If there is a strong fit, we define a focused pilot with clear objectives, success metrics, and a roadmap to scale.
1. Share the challenge
What sample type, workflow, and decision do you need to improve?
2. Review the fit
We review technical fit, data quality, sample requirements, and validation constraints.
3. Define the pilot
If the fit is strong, we propose objectives, metrics, timeline, and expected outputs.
4. Plan scale-up
After validation, we support integration and production-readiness.
What you get first: a clear view of fit, sample needs, validation criteria, and a recommendation for the next step.
