Beer, Wine, Whiskey & AI
How artificial intelligence, data science, and BI tools — Power BI, Tableau, and the Power Platform — are transforming the drinks industry: beer, non-alcoholic beer, wine, whiskey, and mead. From the brewhouse and the still to fermentation, quality control, sensory tasting, demand forecasting, and ESG. Practitioner-grounded, honest about what works and what doesn't, and free of hype.
Start here
New to the blog? Begin with the flagship series on how a brewer becomes an AI and data practitioner — the real path, the failures, and what actually works.
AI for drinks producers, from someone who has run the brewhouse
Ankur Napa spent a decade in breweries (AB InBev, SABMiller, United Breweries) before building AI and data tools for beer, wine, and spirits. Select consulting engagements in demand forecasting, quality and process analytics, dashboards, and production-grade GenAI.
Talk to a brewing data scientist →Explore by track
Two kinds of deep dive. Business tracks — executive, data-driven analytics across the beverage business, applied to beer and the fast-growing non-alcoholic segment. And production & science tracks — the technical process itself, from grain and grape to glass, through AI, data science, and generative AI:
Explore by series
Prefer a guided, multi-part read? Each series builds in order — start to finish on one theme:
- From Brewer to AI — the honest journey
- The Brewer's AI Roadmap — step-by-step, free resources
- Asking Better Questions — AI for new brewers
- Vibe-Coding for Brewers — build tools with Claude Fable 5
- Seeing Your Beer — data visualizations brewers underuse
- The Brewer's Chart Field Guide — every chart type and its beer use cases
- AI Foundations for Distillers — AI, ML & GenAI explained
- Process Intelligence for Distilleries — the four analytics
- Process Intelligence for Wineries — the four analytics
- Beer NPD with Data — new product development
Core reading on AI in drinks
Latest posts
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AI in Sensory and the QA System
The final part of the brewery-lab AI series: taster notes as data via NLP, a predictive freshness index, predictive calibration from instrument drift, and deviation and out-of-spec triage. Honest about where a confident model quietly misleads.
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Machine Vision on the Packaging Line
Inline machine vision on the brewery packaging line: fill-level checks with SPC to catch drift, crown-seal and label-defect inspection, washed-bottle grading, and pasteurisation-unit modelling from tunnel temperatures.
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From Plate Counts to Risk Triage in Microbiology
How AI earns its keep in brewery micro: contamination-risk triage that ranks sample points across propagation, packaging, water and CIP, plus vision colony counting. Honest about where it breaks.
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Soft Sensors in the Beer-Chemistry Lab
Part 3 of the brewery-lab AI series: diacetyl and VDK forecasting from the fermentation curve, dissolved-oxygen anomaly detection at the filler, and shelf-life survival models, with the reference method still the arbiter.
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On the Malt Bench: Vision, NIR and Incoming QC
How machine vision reads acrospire growth, malt colour and foreign material on the raw-materials bench, and how NIR soft sensors predict extract and moisture before the mash, with the honest caveats.
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AI in the Brewery Lab: What's Real and What's Hype
A map of where AI genuinely fits in the brewery QC lab in 2026 (soft sensors, machine vision, contamination-risk triage, and predictive calibration) and where a confident model quietly breaks. The opening of a 6-part series that walks the lab bench by bench.
Frequently asked questions
How is AI used in the drinks industry?
AI and data analytics help breweries, wineries, and distilleries forecast fermentation, predict demand, control quality, optimize ESG metrics like water and carbon, and sharpen marketing — while human judgment and taste stay essential.
Can AI improve brewing and winemaking?
Yes, where clean data exists. AI reliably helps with forecasting, monitoring, and demand planning, but it cannot taste or replace a brewer's or winemaker's craft, and it can produce confident-but-wrong outputs if its results aren't verified.
Who writes Beer, Wine, Whiskey & AI?
Ankur Napa, a brewer turned brewing data scientist who spent about a decade at breweries including AB InBev, SABMiller, and United Breweries before building AI and data tools for the drinks industry.