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Hrushiekesh Reddy Kanjula

Project Deep Dive

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A Python-native manufacturing intelligence system built to close the gap between machine exports and engineer decisions. Async agents. Shared database. Web dashboard. Every module connected through a single intake pipeline.

Python async agentsShared DBWeb dashboardIPC-2581AOI + FPTBOM normalization

Job Loader

The entry point. Every downstream module lives or dies by what comes in here.

Process Flow

Select job filesParse job documentsExtract: components,values, dimsNormalize + deduplicateentriesAssign packages (e.g.0402-12)Shared DBAOI ReportX/Y RotationFPT AnalysisAlso seeds: Validation, Message Log, BOM pipeline

The Problem

Before Job Loader existed, engineers navigated a tangle of machine folders and vendor exports on multiple workstations — no single canonical source of what a job actually contained. The wrong revision of a file would silently propagate to AOI analysis, rotation logic, and test validation. The damage only surfaced later, on the floor.

The Approach

Job Loader became the authoritative intake pipeline: parse the incoming job documents, extract every component with its reference designator, actual values, body dimensions, and lead dimensions, normalize them into a consistent schema, then write to the shared database. Every downstream module — AOI Report, X/Y Rotation, FPT Analysis — reads from that clean record rather than from raw vendor files.

Why This Solution

Making Job Loader the mandatory gate rather than an optional convenience was the key architectural decision. It meant that body and lead dims were always available when the rotation logic needed them, that BOM cross-references always had a source of truth, and that package assignments could be created or confirmed from consistent data. The 0402-12 package naming convention came directly from having structured dims available at intake — something that wasn't possible when each module was parsing its own raw files.

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