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R / Shiny / RAGProduction · v2.0.0

BioTrack

Proactive bioburden monitoring & investigation aid

Internal enterprise application — built at Takeda Pharmaceutical

3
Data sources unified
30-day
Closure window kept
6
Auto chart types
8
Analytics modules

The problem

A single bioburden deviation could take hours to assemble: pulling and reconciling DeltaV, MODA and Labware by hand before any analysis could start — time that ate into the 30-day window reserved for physical inspection, CAPA design and QA review.

Architecture

01 · Sources
  • DeltaV
  • MODA
  • Labware
02 · R analytics
  • Alert rates
  • Cycle-time corr.
  • Organism freq.
  • Risk scores
03 · Visualization
  • 6 Plotly types
  • Heatmaps · risk matrix
04 · RAG context
  • Trackwise vector search
  • Cites real PR numbers only
05 · Interpretation
  • LLM narrates R output
  • Investigation summary
BioTrack analytics + RAG architectureThe LLM never computes numbers — it only explains what R produced.

What it does

BioTrack is a manufacturing investigation aid for bioburden teams, built on the proven TrackLA v2.5.0 architecture. It aggregates DeltaV, MODA and Labware automatically so investigators start from analysis, not data plumbing — preserving the 30-day closure window for the work that actually needs human judgment.

R-first analytics, LLM interpretation

R computes alert rates, cycle-time correlations, organism frequencies and risk scores; the LLM only interprets them. RAG over Trackwise (Databricks Vector Search) pulls relevant past deviations, and an anti-hallucination guard ensures the assistant only cites PR numbers that actually exist in the loaded data.

  • Auto-generates 6 Plotly chart types per analysis — equipment heatmaps, scatter plots, trend lines, risk matrices
  • Modular design: analytics_orchestrator, visualization_engine, deviation_context, bioburden_rag, bioburden_analytics, equipment_cycle_analytics, file_processor, user_guide

Stack

R ShinyDatabricks Foundation ModelsDatabricks Vector SearchPlotly