Engineering data is typically scattered across manually maintained spreadsheets, so reconstructing a sample’s history means hours of cross-referencing files.
This use case connects engineering data across the full manufacturing process and every site. A single sample identifier retrieves a complete history, with every record traceable back to source. It flags gaps in the data, supports analysis workflows directly over the connected records, and compares process data and outcomes across any manufacturing situation.
Engineers see how process decisions shape material characteristics and performance, and spot the patterns driving variation between sites. Work that once took hours of data assembly starts as analysis.
How it works
The Manufacturing Intelligence Hub runs as a pipeline of six MisaCores - modular building blocks from our platform. Each one owns a single stage, from authentication to validation, and the whole pipeline runs inside the customer's own environment.
End-user authentication - login, hybrid access and refresh tokens, and the session of record. Runs dual-mode, verifying against the MisaLabs platform UMS.
Turns raw .xlsx workbooks into structured rows through a stable ingest interface. The customer-specific part - their sheet layouts - is a config profile, not custom code.
Pulls source bytes the system doesn’t yet hold from a pluggable source, then hands a checksummed, provenance-stamped payload to Ingestion.
Domain-agnostic review and approval state machine, modelling draft → submitted → approved/rejected for furnace-trial records, with re-approval on post-approval edits.
Given linked records and a start ID, returns everything connected to it - what came before and after. Powers the cross-stage lineage lookup and the lineage graph view.
Deterministic, versioned rule validation over proposed records and actions, covering declarative validation and pipeline-integrity checks.
