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GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => Array
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                    [geoname_id] => 4509177
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                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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            [postal] => Array
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            [registered_country] => Array
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                            [es] => Estados Unidos
                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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                                    [es] => Ohio
                                    [fr] => Ohio
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                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
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            [traits] => Array
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        )

    [continent:protected] => GeoIp2\Record\Continent Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
                        (
                            [de] => Nordamerika
                            [en] => North America
                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                )

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    [country:protected] => GeoIp2\Record\Country Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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            [validAttributes:protected] => Array
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                    [0] => queriesRemaining
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        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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        )

    [traits:protected] => GeoIp2\Record\Traits Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [ip_address] => 216.73.216.213
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
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            [validAttributes:protected] => Array
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                    [8] => isHostingProvider
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                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
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                    [17] => network
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                    [20] => userCount
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        )

    [city:protected] => GeoIp2\Record\City Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
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                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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    [location:protected] => GeoIp2\Record\Location Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [validAttributes:protected] => Array
                (
                    [0] => averageIncome
                    [1] => accuracyRadius
                    [2] => latitude
                    [3] => longitude
                    [4] => metroCode
                    [5] => populationDensity
                    [6] => postalCode
                    [7] => postalConfidence
                    [8] => timeZone
                )

        )

    [postal:protected] => GeoIp2\Record\Postal Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => 43215
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            [validAttributes:protected] => Array
                (
                    [0] => code
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        )

    [subdivisions:protected] => Array
        (
            [0] => GeoIp2\Record\Subdivision Object
                (
                    [record:GeoIp2\Record\AbstractRecord:private] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

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                    [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                        )

                    [validAttributes:protected] => Array
                        (
                            [0] => confidence
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                )

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)
 country : United States
 city : Columbus
US
Array
(
    [as_domain] => amazon.com
    [as_name] => Amazon.com, Inc.
    [asn] => AS16509
    [continent] => North America
    [continent_code] => NA
    [country] => United States
    [country_code] => US
)
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

Objectives-01
  • 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

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

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

    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

    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.

    From Raw Data to Real-Time Decisions

    All in One Pipeline

    Scrape Structure Analyze Visualize

    Look Back Analyze historical data to discover patterns, anomalies, and shifts in customer behavior.

    Find Insights Use AI to connect data points and uncover market changes. Meanwhile.

    Move Forward Predict demand, price shifts, and future opportunities across geographies.

    Industry:

    Coffee / Beverage / D2C

    Result

    2x Faster

    Smarter product targeting

    ★★★★★

    “Actowiz Solutions has been instrumental in optimizing our data scraping processes. Their services have provided us with valuable insights into our customer preferences, helping us stay ahead of the competition.”

    Operations Manager, Beanly Coffee

    ✓ Competitive insights from multiple platforms

    Industry:

    Real Estate

    Result

    2x Faster

    Real-time RERA insights for 20+ states

    ★★★★★

    “Actowiz Solutions provided exceptional RERA Website Data Scraping Solution Service across PAN India, ensuring we received accurate and up-to-date real estate data for our analysis.”

    Data Analyst, Aditya Birla Group

    ✓ Boosted data acquisition speed by 3×

    Industry:

    Organic Grocery / FMCG

    Result

    Improved

    competitive benchmarking

    ★★★★★

    “With Actowiz Solutions' data scraping, we’ve gained a clear edge in tracking product availability and pricing across various platforms. Their service has been a key to improving our market intelligence.”

    Product Manager, 24Mantra Organic

    ✓ Real-time SKU-level tracking

    Industry:

    Quick Commerce

    Result

    2x Faster

    Inventory Decisions

    ★★★★★

    “Actowiz Solutions has greatly helped us monitor product availability from top three Quick Commerce brands. Their real-time data and accurate insights have streamlined our inventory management and decision-making process. Highly recommended!”

    Aarav Shah, Senior Data Analyst, Mensa Brands

    ✓ 28% product availability accuracy

    ✓ Reduced OOS by 34% in 3 weeks

    Industry:

    Quick Commerce

    Result

    3x Faster

    improvement in operational efficiency

    ★★★★★

    “Actowiz Solutions' data scraping services have helped streamline our processes and improve our operational efficiency. Their expertise has provided us with actionable data to enhance our market positioning.”

    Business Development Lead,Organic Tattva

    ✓ Weekly competitor pricing feeds

    Industry:

    Beverage / D2C

    Result

    Faster

    Trend Detection

    ★★★★★

    “The data scraping services offered by Actowiz Solutions have been crucial in refining our strategies. They have significantly improved our ability to analyze and respond to market trends quickly.”

    Marketing Director, Sleepyowl Coffee

    Boosted marketing responsiveness

    Industry:

    Quick Commerce

    Result

    Enhanced

    stock tracking across SKUs

    ★★★★★

    “Actowiz Solutions provided accurate Product Availability and Ranking Data Collection from 3 Quick Commerce Applications, improving our product visibility and stock management.”

    Growth Analyst, TheBakersDozen.in

    ✓ Improved rank visibility of top products

    Trusted by Industry Leaders Worldwide

    Real results from real businesses using Actowiz Solutions

    ★★★★★
    'Great value for the money. The expertise you get vs. what you pay makes this a no brainer"
    Thomas Gallao
    Thomas Galido
    Co-Founder / Head of Product at Upright Data Inc.
    Product Image
    2 min
    ★★★★★
    “I strongly recommend Actowiz Solutions for their outstanding web scraping services. Their team delivered impeccable results with a nice price, ensuring data on time.”
    Thomas Gallao
    Iulen Ibanez
    CEO / Datacy.es
    Product Image
    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 highly recommended!”
    Thomas Gallao
    Febbin Chacko
    -Fin, Small Business Owner
    Product Image
    1 min

    See Actowiz in Action – Real-Time Scraping Dashboard + Success Insights

    Blinkit (Delhi NCR)

    In Stock
    ₹524

    Amazon USA

    Price Drop + 12 min
    in 6 hrs across Lel.6

    Appzon AirPdos Pro

    Price
    Drop −12 thr

    Zepto (Mumbai)

    Improved inventory
    visibility & planning

    Monitor Prices, Availability & Trends -Live Across Regions

    Actowiz's real-time scraping dashboard helps you monitor stock levels, delivery times, and price drops across Blinkit, Amazon: Zepto & more.

    ✔ Scraped Data: Price Insights Top-selling SKUs

    Our Data Drives Impact - Real Client Stories

    Blinkit | India (Retail Partner)

    "Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

    ✔ Scraped Data, SKU availability, delivery time

    US Electronics Seller (Amazon - Walmart)

    With hourly price monitoring, we aligned promotions with competitors, drove 17%

    ✔ Scraped Data, SKU availability, delivery time

    Zepto Q Commerce Brand

    "Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

    ✔ Scraped Data, SKU availability, delivery time

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