Why Crude Oil Analysis Matters at Every Stage of the Refinery
Where refineries sample crude and products, what each analysis measures, and how faster NMR-based predictions can support blending, fouli...
Blog
Insights on AI and NMR
Perspectives on AI, NMR spectral data, and the future of materials analysis.
What to read next
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The Future of Agentic AI in Analytical Chemistry
A practical capability horizon for agentic AI in analytical chemistry, from bounded software orchestration to governed closed-loop experi...
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Automating NMR Structure Elucidation with Confidence Scores
Learn what confidence scores mean in automated NMR structure elucidation, how to calibrate them, and when an agent should rank, abstain, ...
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Human-in-the-Loop AI for Laboratory Decision Making
A practical governance framework for assigning ownership, review gates, feedback, and escalation in human-in-the-loop laboratory AI workf...
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How to Benchmark an AI Agent for Scientific Data Analysis
A practical framework for benchmarking complete scientific AI agents across outcomes, trajectories, evidence, abstention, cost, latency, ...
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Failure Modes of Agentic Systems in Scientific Workflows
How scientific AI agents fail through bad data, tool misuse, unsupported hypotheses, loops, and weak escalation—and how to detect and con...
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How an AI Agent Interprets an NMR Spectrum: A Step-by-Step Pipeline
Follow an auditable AI-agent pipeline for NMR compound identification, from data-quality checks and preprocessing to candidate ranking, c...
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What Are Agentic Systems for NMR Data Analysis?
Learn what makes an AI agent for NMR data analysis different from a model or fixed pipeline, which tools it can coordinate, and where che...
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Build vs Buy: Spectral Interpretation with Spectra
A practical build-versus-buy framework for spectral interpretation, covering data, validation, maintenance, cross-instrument generalisati...
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Integrating Spectra into Laboratory Data Pipelines
A practical architecture for connecting Spectra to instrument exports, batch queues, LIMS records, review steps, and traceable compound-i...
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Spectra: Automated Compound Identification from NMR Spectra
A practical guide to what Spectra takes in, what it returns, how chemists review its ranked candidates, and how to evaluate it on represe...
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Compound Identification in Petrochemical and Crude Oil Analysis
A practical framework for identifying hydrocarbons, heteroatom species, contaminants, and additives across crude oil and petrochemical wo...
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Cosa abbiamo imparato costruendo un agente per la structure elucidation
Cosa abbiamo imparato costruendo un agente AI per la structure elucidation tramite NMR? Una riflessione su autonomia, overthinking e su c...
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Structure Elucidation Workflows for Natural Products
A practical structure elucidation workflow for natural products—from extract provenance and dereplication to isolation, NMR connectivity,...
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Impurity Identification in Pharmaceutical Quality Control
A practical guide to classifying, isolating, identifying, and documenting pharmaceutical impurities with chromatography, mass spectrometr...
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Combining NMR and Mass Spectrometry for AI Structure Elucidation
Learn how NMR and mass spectrometry can be fused as constraints, features, or ranked decisions—and how to keep multimodal AI structure el...
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How AI Identifies Unknown Compounds from Raw Spectra
AI spectroscopy turns instrument data into learned representations, candidate structures, and evidence for review. See what happens at ea...
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Generalization Across NMR Instruments: A Foundation Model Pre-Trained on Spectral Data
Most NMR deep learning models train an encoder and task head together for a particular endpoint, field strength, or instrument regime. Pe...
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Confidence Scoring in Compound Identification
A match score measures spectral similarity, not automatically the probability that an identification is correct. Learn how to interpret, ...
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Unknown Compound Identification: An End-to-End Workflow
A practical unknown compound identification workflow, from representative sampling and complementary spectra to candidate falsification, ...
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Spectral Deconvolution: Methods, Pitfalls, and AI
Spectral deconvolution separates overlapping signals into interpretable components. Learn how curve fitting, multivariate resolution, GC-...
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How Spectral Matching and Library Search Work
Spectral matching turns an unknown spectrum into a ranked hit list—but a high score is not automatically an identification. Learn how pre...
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NMR vs Mass Spectrometry for Compound Identification
NMR reveals chemical environments and atomic connectivity, while mass spectrometry constrains mass, formula, and fragmentation. Learn whi...
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What Is Compound Identification in Analytical Chemistry?
Compound identification determines which substance produced an analytical signal. Learn how NMR, MS, IR, and GC-MS contribute and how lab...
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AI Crude Oil Analysis: Lab Testing vs NIR vs High-Field NMR vs Rombo AI
Refinery teams now choose among laboratory testing, NIR, high-field NMR, and Rombo AI for crude oil analysis. Lab assays still certify a ...
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Best NMR & Analytical Chemistry Software: Comparison Guide
Analytical chemistry software now splits into instrument-native tools, vendor-neutral suites, and AI-native platforms. This guide compare...
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What Is Quantitative NMR (qNMR) and How Does It Work?
Quantitative NMR determines the amount or purity of a compound from integrated NMR signals, so a certified internal standard can quantify...
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What Is Quantitative NMR (qNMR)? Purity, Calibration Methods, and Accuracy
Quantitative NMR determines concentration or purity from the integrated area of an NMR signal, so a reference of known purity can quantif...
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Scalable NMR Deployment Across Multi-Site Laboratories: Architectural Trade-Offs
Rombo AI supports NMR fleets that combine Bruker, Varian/Agilent, and JEOL instruments. Its foundation model, pre-trained on millions of ...
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Why Maturity Tiers Matter for Choosing Spectral Software
Choosing software for Nuclear Magnetic Resonance (NMR) and spectroscopy analysis requires evaluating how much manual interpretation the s...
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AI-Powered Quality Control Automation for NMR Labs
Manual NMR interpretation can delay quality control decisions for weeks because trained specialists must review complex spectra. Rombo AI...
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Multi-Instrument NMR Compatibility: One AI Platform Across Multi-Brand Instruments
Rombo AI provides one vendor-neutral foundation model layer across Bruker, JEOL, Agilent/Varian, and Oxford Instruments without per-instr...
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The Missing Layer in NMR Machine Learning
Task-specific deep learning, low-field constraints, and the case for reusable spectrum representations.
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AI in Downstream Refinery: What’s the Formula to Success?
Where should refiners start if they want AI to deliver real operational value? If AI isn't the answer to every challenge, where can it cr...
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Machine Learning Applications in Materials Chemistry: The Rombo AI Approach
Machine learning is turning NMR into a predictive tool for industrial analytics. By applying AI directly to raw NMR spectra, complex mixt...
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Use Case: Accelerating Crude Oil Analysis for a Leading Asian Refinery
At one of Asia’s largest integrated energy companies, refinery R&D teams evaluate hundreds of crude oil feedstocks every day to support p...
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The Future Is Agentic: How AI Agents Bring Back Calm Technology
Tracing a personal and technological journey from Mark Weiser’s vision of ubiquitous, calm computing to today’s emergence of generative A...
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The AI Transformation of Crude Oil Analysis: From Laboratory Bottlenecks to Real-Time Intelligence
The Oil & Gas industry is embracing AI for crude oil analysis. Discover how Rombo AI combines NMR spectroscopy with machine learning to d...
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SpectraML: A framework designed for R&D teams
SpectraML breaks down barriers to Machine Learning adoption with its low-code/no-code approach. Designed for R&D teams, it simplifies dat...
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Esplorazione delle Potenzialità della Spettroscopia NMR nel Settore delle Energie Rinnovabili
Partnerhip con il cluster delle energie rinnovabili per l'esplorazione delle Potenzialità della Spettroscopia...
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Low-Field NMR for Crude Oil Analysis: Harnessing Machine Learning for Predicting Properties
In the oil industry, understanding the properties of crude oil is essential for refining, quality control, and maximizing production effi...
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How Low-Field NMR Works and How Machine Learning Enhances Sample Characterization
Nuclear Magnetic Resonance (NMR) is a powerful analytical technique used to determine the molecular structure and composition of various ...
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