Petrochemicals and energy: one data foundation for every blend and every test
A blend result is only useful when the team can see which formulation produced it, how it was processed, which method measured it, and which specification it has to meet. In most petrochemical and energy organizations, that context is split across lab spreadsheets, pilot plant files, instrument exports, and quality systems.
Build one data foundation for every blend and every test is a two-page overview of how Uncountable gives petrochemical and energy teams one structured home for formulations, process conditions, test results, and specifications, connected to the systems around them. Its worked example follows a lubricants team from an instrument export landing in an SFTP folder to results with units, checked against the spec and ready to compare.
Inside this solution brief:
- Ingredients, process parameters, and equipment stored as inputs, with measured properties and their test conditions as outputs
- Units on every value, with conversion between compatible units and work on a mass, volume, or mole basis
- Configurable uploaders that parse Excel, CSV, and XML files and instrument exports such as DSC, TGA, DMA, and tensile data
- Lot and lineage tracking down to the exact lot used on each recipe line
- Ranges, minimums, and maximums on measured properties, with pass or fail shown for each experiment
- The Python SDK, external API, scheduled integration jobs, SFTP and S3 file pickup, and webhooks
- Bodie AI Agent for searching records and querying your data
- Access by project or experiment, an audit log of every field change, SOC 2 Type II, and ISO 27001
See how lab, pilot plant, and quality data can sit on one model your team can trace and reuse.
FAQs
It is a two-page overview of Uncountable as data infrastructure for petrochemical and energy teams. It covers structured experiment data, unit handling, instrument file parsing, lots and lineage, specification checks, search, integrations, security, and Bodie AI Agent, with a worked example from a lubricants lab.
Configurable uploaders parse Excel, CSV, and XML files and instrument exports, such as DSC, TGA, DMA, and tensile data, into structured results. The values land on the experiment record with their units instead of staying in a separate file.
Every measured value carries its unit, such as cSt, cP, °C, or %. Teams can convert between compatible units, let each user pick display units, and work on a mass, volume, or mole basis.
Yes. The Python SDK and external API create and update records, set inputs and outputs, upload files, and query data from your own code. Scheduled integration jobs can pick up files from SFTP or S3, and webhooks let triggers in Uncountable call your systems.
Teams set ranges, minimums, and maximums on measured properties and see which goals each experiment passes or fails. In the brief’s engine oil example, one blend reads 5,900 cP for cold-cranking viscosity at -30 °C against a limit of 6,600 cP and shows green, while the other reads 6,900 cP and shows red.
