AMIRA

The problem

Fractionation process engineers needed near-real-time insight across FP002, FP005 and G-Cent standard work, but the data lived in disconnected systems (OSI PI sensors, JDE commodity data, LIMS) and answering a single process question meant manual pulls and spreadsheet wrangling. PowerBI dashboards showed what happened but couldn't explain why — or be trusted to quote exact numbers.

What it does

AMIRA — Advanced Manufacturing Insights & Real-time Analytics — is a multi-process analytics platform covering FP002, FP005 and G-Cent standard work with a 15-minute refresh and a Tier-2 Agentic BI dashboard for constraint identification. It reconciles C3ME JDE commodity data against process data, which drove a reported 59% reduction in deviations.

TOMO — the AI assistant

TOMO is a hybrid-RAG chatbot built on a discipline I use everywhere: R computes the statistics (mean, median, percentiles, trend, shift) and the LLM only interprets them. That single decision cuts token spend ~70% and gives a hard anti-hallucination guarantee — the model cannot invent a number it was never handed. TOMO pre-filters by equipment before the vector search and applies date ranges client-side afterward.

• TOMO Vision — multimodal reasoning over charts, tables and text together, with chart-aware MVDA interpretation
• Voice input (Web Speech API) for gloved operators; TTS that skips table rows so operators hear analysis, not recitations
• Thumbs-up/down feedback and last-5 recent prompts persisted to S3 for one-click reuse

Process modeling

• Sensor Process Variation — OSI PI minute-resolution traces (flow rate, differential pressure, temperature, turbidity) with P15–P85 'Good' envelopes and per-lot anomaly overlays
• SIMCA-style Batch Evolution Model — DModX-over-maturity trajectories against the Good-population +3SD limit, with per-minute sensor contribution drill-downs (Hotelling's T², Q-residuals)
• Multi-lot drill-down — overlay up to 5 lots; the LLM payload is capped at 3 to bound token spend

Engineering & accessibility

A centralized RAG helper module consolidates 12 core functions — vector search, post-filtering, date parsing, R-powered summary stats and scheduling-delay enrichment — so the same logic isn't reimplemented across features. The UI ships a viridis colorblind-safe high-contrast mode (WCAG 2.1 AA), 44px+ tap targets, and data-freshness badges synced across users through S3.