We create agentic AI solutions to extract the maximum value from materials research data at scale and to integrate it into proprietary R&D workflows.
Machine-learning-ready data
Highly accurate
Extracted at scaleWe turn private and public R&D information into structured, machine-learning-ready data. Our agentic systems mine, extract, and standardize information with high accuracy and at scale.
Research organizations generate large volumes of data through extensive experimental work. This is often documented manually, with standards and ontologies evolving over the years. OGHMA orders and rationalizes historical data into highly structured datasets, following the ontology defined by the customer. Our accuracy is superior to what is manually achievable, and our throughput is beyond the reach of human curation.
Many R&D-intensive industries routinely ingest information from customers and contractors. The information format is often based on the customer/contractor schemas, so reports are not readily usable for R&D. OGHMA converts information across formats, schemas, and ontologies, integrating the data into the customer’s R&D workflow.
Access to available R&D data is critical to any research-intensive organization. Information, however, is scattered over a large volume of unstructured documents (papers, patents, reports, etc.). OGHMA harvests R&D information at scale. We identify the primary sources and extract high-quality information. Our machine-learning-ready datasets can be used to construct models and can be integrated into the R&D process.
We sit with you, embedding our team in your organization for a limited time, to define the problem and establish the scope, metrics, and timeline.
We perform a pilot task on a controlled initial corpus of documents. This refines our schemas and ensures that the outcome matches the customer’s specifications.
We scale up the process to large document corpora, ensuring that accuracy is preserved as the data volume increases.
Explore our work with APC and BASF and the technology behind it.
OGHMA · Company overview
Explore how our agentic workflows find, extract, and validate scientific information for integration into R&D systems.
Source-traceablescientific data for R&D
Download overviewPDF · 804 KB
APC · Case study
OGHMA built a solubility dataset from scientific literature to support machine-learning models for pharmaceutical R&D.
25,600solubility dataset entries
Download case studyPDF · 250 KB
BASF · Case study
OGHMA converted 30 years of historical R&D records into structured data for research and predictive modeling.
99.7%accuracy on a control dataset
Download case studyPDF · 256 KB
Tell us about your research data and what you want to achieve.
