INDUSTRIAL DATA & AI PLATFORM
Flux Foundry & Reliability
An AWS-native industrial data platform that turns fragmented spare-parts records into one trusted catalogue — then gives reliability engineers evidence-backed duplicate and substitution decisions in minutes.
Flux Foundry
Identity pipeline
Source records
Bearing · Deep groove ball
SKF 6205-2RS
Bore
25 mm
Width
15 mm
Seals
2RS
Aliases
4 linked
About the Project
Flux Foundry & Reliability
Industrial parts data rarely arrives ready to use. The same bearing can appear under a manufacturer number, an OEM number, a supplier code, and an internal ID — with inconsistent names and incomplete technical attributes across every record. Code Huddle worked with Ensemble AI on Flux Foundry, the data infrastructure beneath the Flux Suite, and Flux Reliability, its first engineer-facing application. Together they ingest messy operational data, enrich it with engineering context, resolve every alias to a canonical identity, and make each recommendation explainable enough for an engineer to approve.
The system, not a screen
Raw records in. One source of truth out.
Foundry preserves provenance at every stage, while Reliability turns the trusted graph into decisions engineers can inspect and approve.
Inputs
Medallion pipeline
Bronze
Source-faithful records
Silver
Clean attributes & units
Gold
Canonical identities
Reliability
Turnaround
~5 min
Project Deep Dive
Project Deep Dive
Unpacking the architecture, innovations, and real-world results
One entry point for fragmented industrial data
Foundry accepts the formats industrial teams already have: ERP exports, CSV and Excel spreadsheets, and upstream APIs. Ingestion is event-driven and asynchronous, so large files can move through the pipeline without blocking the product API or requiring an operator to configure a custom flow for every source.
Ingestion queue
Non-blocking events
From raw records to engineering-ready data
A Bronze, Silver, and Gold medallion architecture keeps transformation traceable. Raw source records are retained first; column names, units, and formats are then normalized; finally, technical attributes such as bore size, load ratings, and voltage are enriched using LLM-assisted extraction and engineering standards.
Gold
Canonical + queryable
Silver
Normalised + enriched
Bronze
Raw + source-faithful
Four part numbers become one identity
The identity engine detects exact and near duplicates, connects equivalent parts across manufacturers, and persists those relationships in a graph database. Manufacturer, OEM, supplier, and internal identifiers all resolve to the same canonical part while preserving their source history and where-used relationships.
Canonical node
SKF 6205-2RS
Every alias and relationship preserved in the graph.
Recommendations engineers can verify
Flux Reliability turns Foundry’s graph into practical decisions: duplicate clusters, identical-part resolution, cross-manufacturer equivalency, and a ranked substitution ladder. Every recommendation carries a confidence score, the attributes compared, and the relevant ISO or IEC logic, giving engineers an evidence trail instead of a black-box answer.
Illustrative evidence trail
Candidate equivalent → canonical part
confidence scored
Enterprise scale without crossing tenant boundaries
Tenant context is enforced throughout ingestion, enrichment, graph storage, and API responses — not added as a filter at the end. That isolation model combines with asynchronous processing to handle millions of rows while ensuring one customer can never access another customer’s industrial catalogue.
Customer A catalogue
Private tenant lane
Customer B catalogue
Private tenant lane
Customer C catalogue
Private tenant lane
Key Outcomes
Key Outcomes
Measurable results that demonstrate the impact of thoughtful design
~5 min
from a messy parts spreadsheet to a cleaned, enriched, and rationalised result set
4 → 1
manufacturer, OEM, supplier, and internal part numbers resolved to one canonical identity
3 layers
Bronze, Silver, and Gold stages keep raw, normalized, and trusted data traceable
Millions
of rows processed asynchronously with tenant isolation enforced throughout the pipeline
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