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

Processing

Source records

MFRSKF 6205-2RS
OEMOEM bearing reference
SUPSupplier bearing reference
INTInternal bearing reference
Canonical

Bearing · Deep groove ball

SKF 6205-2RS

Bore

25 mm

Width

15 mm

Seals

2RS

Aliases

4 linked

Identity resolved with evidence

حول المشروع

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

ERP systems
CSV / Excel
External APIs
Event-triggered

Medallion pipeline

01 · Raw

Bronze

Source-faithful records

02 · Normalised

Silver

Clean attributes & units

03 · Trusted

Gold

Canonical identities

PreserveEnrichResolve

Reliability

Duplicate clusters
Equivalent parts
Substitution ladder

Turnaround

~5 min

الغوص العميق في المشروع

الغوص العميق في المشروع

تفكيك البنية والابتكار والنتائج الواقعية

01

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.

ERP export
Parts.xlsx
Supplier API
Async

Ingestion queue

Non-blocking events

02

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.

03

Gold

Canonical + queryable

02

Silver

Normalised + enriched

01

Bronze

Raw + source-faithful

03

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.

MFRSKF 6205-2RS
OEMOEM bearing reference
SUPSupplier bearing reference
INTInternal bearing reference

Canonical node

SKF 6205-2RS

Every alias and relationship preserved in the graph.

04

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

Bore diameterCompared
Dynamic load ratingCompared
Seal configurationCompared
IEC 62402ISO 14224Attribute trail
05

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.

A

Customer A catalogue

Private tenant lane

B

Customer B catalogue

Private tenant lane

C

Customer C catalogue

Private tenant lane

Isolation enforced at every layer

النتائج الرئيسية

النتائج الرئيسية

نتائج قابلة للقياس تُظهر تأثير التصميم المدروس

~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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