AI Nutrition Info Scraping from FairPrice Singapore

AI-Based-Scraping-of-Nutritional-Info-in-Grocery-SKUs-from-FairPrice-Singapore

Introduction:

In today’s health-conscious environment, consumers are demanding transparency—not just in price but in nutritional data. Grocery retailers like FairPrice Singapore host thousands of SKUs with nutrition labels, yet this valuable information is not easily accessible or structured for analytics.

Actowiz Solutions leveraged its AI-powered web scraping and data extraction engine to automatically capture, clean, and analyze nutritional information from FairPrice’s product listings. This allowed food brands, health startups, and analytics platforms to build nutrient-rich databases for insights, labeling compliance, and dietary analysis.

Client Objectives

Extract structured nutritional data (calories, sugar, sodium, etc.) from FairPrice product listings
Normalize varied data formats and units into a consistent dataset
Identify high/low nutrient categories for marketing and product positioning
Enable advanced filtering by nutrition type (low sugar, high protein, etc.)
Deliver daily updated data feed via API and dashboard

Challenges Faced

Nutrition data was embedded in multiple formats (HTML tables, PDFs, text blocks)
Inconsistent labeling terminology across SKUs (e.g., “Energy (kcal)” vs “Calories”)
Units varied—some in 100g, some per serving, others per piece
Parsing ingredient lists required natural language processing
No public API for nutrition—scraping was the only scalable approach

Actowiz’s Scraping + AI Solution

1. Targeted Web Scraping Pipeline

Actowiz’s scrapers navigated FairPrice’s product pages and captured:

Product title and category
Ingredient list
Nutrition facts table (when available)
Serving size, calories, carbs, protein, sugar, sodium, etc.
Brand and country of origin

2. Sample Extracted Data (Raw)

Product	Serving Size	Calories	Sugar (g)	Sodium (mg)	Protein (g)	Category
Meiji Fresh Milk	250 ml	≈130	~12	≈120	≈8	Dairy
Kellogg’s Corn Flakes	30 g	≈108	~2	≈285	≈2	Cereal
Lay’s Classic Chips	28 g	160	1	170	2	Snacks

3. AI Models Used

NLP Parsing – Extracted nutrition data from free-text and OCR-type content

Unit Normalization Engine – Converted different nutrition reporting units into 100g/ml base

Named Entity Recognition (NER) – Detected ingredient-level allergens and additives

Data Clustering – Grouped products by nutrition profile (e.g., low-carb, high-protein)

Missing Data Imputation – Estimated nutrition values where labels were incomplete

Key Features in Actowiz Dashboard

Feature	Description
Nutritional Search Engine	Filter products by sugar, sodium, protein, calorie count, etc.
Ingredient Tag Analyzer	Detect common additives, allergens, and sweeteners in SKUs
Health Score Generator	Assigns each SKU a score based on WHO/FDA nutrition guidelines
Category Nutrition Heatmap	Shows nutrition averages by category (dairy, snacks, frozen, etc.)
CSV/API Export	Full structured dataset for research, app, or product dev use

Use Cases Enabled

For Health Startups: Curated low-sodium, diabetic-friendly grocery lists for Singapore users
For Nutrition Researchers: Identified excessive sodium levels in canned soups & snacks
For Food Brands: Benchmarked their SKUs against competitors in fat, sugar, and calories
For Fitness Apps: Synced real-time nutrition info to meal loggers and barcode scanners

City-Wise SKU Monitoring in Singapore

Actowiz tracked availability and nutritional labeling from:

Online listings across FairPrice’s Singapore-wide delivery zones
Specific regional availability tags (e.g., “Available in Tampines, not in Woodlands”)
Store-only items (tagged but excluded from online delivery)

Business Impact in 45 Days

KPIs After Launch:

Metric	Before Actowiz	After Actowiz
Structured Nutritional Dataset	Not Available	7,000+ SKUs
Consistency in Units (e.g., per 100g)	Low	98% Normalized
Ingredient Keyword Indexing	Manual	AI-Automated
Time to Extract 1000 SKUs	~40 hours	<3 hours
New Use Cases Enabled	Limited	5+ Active Use

Real-World Insights Delivered

Ready-to-eat meals had 2x sodium levels vs recommended daily intake
Gluten-free labeling lacked consistency across cereals and snacks
Frozen foods often missed full nutritional labels—Actowiz flagged 12+ brands
“Healthy” categories still contained >20g sugar in popular juice SKUs

Testimonial

“Actowiz gave us instant visibility into nutrition data we previously couldn’t access. It powers our app’s healthy recommendations in Singapore daily.”

– Founder, Singapore-Based NutritionTech Startup

Next Steps:

Integrate scraped nutrition data with barcode/UPC databases
Launch multi-platform scraping (e.g., RedMart, Cold Storage, Shopee Supermarket)
Offer personalized meal plans based on scraped SKUs
Train AI models to predict missing nutrition data using deep learning

Conclusion

Nutrition transparency is no longer a luxury—it’s a necessity. For platforms, food brands, and health-focused apps, access to structured, real-time nutritional data unlocks better product decisions and healthier consumer journeys.

Actowiz Solutions’ scraping and AI pipeline now powers nutrition intelligence at scale—starting with FairPrice Singapore and expanding globally.

Hear It Directly from Our Clients

Watch how businesses like yours are using Actowiz data to drive growth.

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1 min

★★★★★

"Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing!"

Thomas Galido

Co-Founder / Head of Product at Upright Data Inc.

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2 min

★★★★★

"Actowiz delivered impeccable results for our company. Their team ensured data accuracy and on-time delivery. The competitive intelligence completely transformed our pricing strategy."

Iulen Ibanez

CEO / Datacy.es

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1:30

★★★★★

"What impressed me most was the speed — we went from requirement to production data in under 48 hours. The API integration was seamless and the support team is always responsive."

Febbin Chacko

-Fin, Small Business Owner

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