GeoIp2\Model\City Object
(
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                    [geoname_id] => 4509177
                    [names] => Array
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                            [ru] => Колумбус
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                            [en] => United States
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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        )

    [continent:protected] => GeoIp2\Record\Continent Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [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
                        )

                )

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

    [country:protected] => GeoIp2\Record\Country Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [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
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
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            [validAttributes:protected] => Array
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                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
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        )

    [locales:protected] => Array
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                )

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

    [registeredCountry:protected] => GeoIp2\Record\Country Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [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
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
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            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
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        )

    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
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            [validAttributes:protected] => Array
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                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                    [5] => type
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        )

    [traits:protected] => GeoIp2\Record\Traits Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [ip_address] => 216.73.216.141
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
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            [validAttributes:protected] => Array
                (
                    [0] => autonomousSystemNumber
                    [1] => autonomousSystemOrganization
                    [2] => connectionType
                    [3] => domain
                    [4] => ipAddress
                    [5] => isAnonymous
                    [6] => isAnonymousProxy
                    [7] => isAnonymousVpn
                    [8] => isHostingProvider
                    [9] => isLegitimateProxy
                    [10] => isp
                    [11] => isPublicProxy
                    [12] => isResidentialProxy
                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
                    [16] => mobileNetworkCode
                    [17] => network
                    [18] => organization
                    [19] => staticIpScore
                    [20] => userCount
                    [21] => userType
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        )

    [city:protected] => GeoIp2\Record\City Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 4509177
                    [names] => Array
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                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [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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                    [0] => en
                )

            [validAttributes:protected] => Array
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                    [0] => confidence
                    [1] => geonameId
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        )

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

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

        )

    [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
                                )

                        )

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

                    [validAttributes:protected] => Array
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                            [1] => geonameId
                            [2] => isoCode
                            [3] => names
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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
)
Navratri Mega Sale Price Tracking

Introduction – Why Food Price Transparency Matters

Online food delivery has exploded in India and the GCC markets, led by platforms like Zomato, Swiggy, and MagicPin. While consumers benefit from convenience, they often face inconsistent pricing for the same dishes across platforms. Taxes, delivery fees, and discounts differ by platform, time, and location.

Consumers routinely overpay without realizing that the same restaurant meal can cost 10–25 % more on one app than another. A data-driven, real-time comparison tool could fix this opacity—allowing users to make informed, cost-efficient choices.

Actowiz Solutions partnered with a food-tech startup that wanted to build a real-time food price comparison app integrating live data from multiple food-delivery ecosystems. The goal was to aggregate menu data, compare item prices, delivery charges, and discounts, and calculate the final checkout cost per dish across Zomato, Swiggy, and MagicPin.

Client Objective

Navratri Mega Sale Price Tracking

The client's vision was to build a consumer-facing web + mobile app that would:

  • Fetch live menu and price data for every restaurant across Zomato, Swiggy, and MagicPin.
  • Compare base prices, discounts, delivery fees, and taxes in real time.
  • Display the final payable amount and highlight the cheapest option instantly.
  • Offer advanced search, filter, and sort options by cuisine, location, and price.
  • Provide APIs for future integrations with wallets and loyalty programs.

They needed Actowiz Solutions to build the full data intelligence layer: continuous data extraction, cleaning, comparison, and delivery through a scalable API.

Challenges in the Food Data Ecosystem

Navratri Mega Sale Price Tracking
Dynamic and Frequent Price Updates

Menu prices, taxes, and delivery fees fluctuate hourly. Some restaurants modify discounts multiple times per day. Capturing such volatility required near real-time scraping.

Platform Variation

Each app used distinct price models.

  • Zomato: base + restaurant discount + delivery fee + platform charge.
  • Swiggy: dynamic delivery fee based on distance and traffic.
  • MagicPin: merchant-driven discounts and coupon stacking.
Anti-Automation Mechanisms

Food platforms employ CAPTCHA, rate-limits, and request pattern detection.Actowiz's challenge: build compliant crawlers that mimic human behavior without triggering blocks.

Real-Time Data Synchronization

To maintain relevance, price data needs to be updated every 5–10 minutes, while ensuring system stability and low latency.

Complex Tax and Billing Logic

Some platforms show pre-tax prices, others include GST or service charges differently. The system had to normalize data to achieve an apples-to-apples comparison.

Actowiz Solutions' Approach

Actowiz deployed a multi-layered AI data pipeline integrating intelligent crawlers, live caching, and normalization algorithms.

Discovery and Scoping

The data-engineering team audited all three platforms to identify:

  • HTML structures, API endpoints, and data patterns.
  • Dynamic elements rendered via React or VueJS.
  • Pagination, search parameters, and delivery-fee calculations.
Crawler Architecture

Each crawler was customized for a specific platform:

  • Playwright-based headless browser to render dynamic pages.
  • Proxy rotation and session management to avoid IP blocking.
  • Smart schedulers that auto-adjusted crawl frequency during peak meal hours.
AI Data Normalization

An AI model matched identical restaurants and dishes across platforms by handling minor name differences ("Domino's Pizza – Koramangala" vs "Domino's Koramangala").It standardized units and normalized tax logic for precise comparisons.

Data Pipeline
  • Crawler extracts raw menu and pricing JSON.
  • The normalization engine cleans and maps fields.
  • Currency conversion and tax standardization applied.
  • The comparison algorithm computes the total checkout price.
  • Results pushed to API + dashboard in under 60 seconds.

Data Points Captured

Field Description Example
Restaurant Name Listed brand/outlet Behrouz Biryani
Platform Zomato / Swiggy / MagicPin Swiggy
Dish Name Menu item Paneer Biryani
Base Price Before discounts ₹ 299
Discount Platform offer 20 % OFF
Delivery Fee Dynamic charge ₹ 32
Tax & Charges Service + GST ₹ 18
Final Payable Net bill after discounts ₹ 349
Timestamp Last update 2025-10-30 12:10 PM

Sample Data Snapshot

Restaurant Dish Zomato Final Swiggy Final MagicPin Final Cheapest
Domino's Pizza Veg Paradise Medium ₹ 412 ₹ 389 ₹ 398 Swiggy
Biryani Blues Chicken Biryani ₹ 321 ₹ 345 ₹ 310 MagicPin
KFC Zinger Burger Meal ₹ 289 ₹ 279 ₹ 299 Swiggy
Behrouz Biryani Paneer Biryani ₹ 349 ₹ 370 ₹ 339 MagicPin

The data shows how users can save anywhere from ₹ 10 to ₹ 40 per order just by switching platforms.

Technology Stack

Layer Tools / Frameworks
Web Scraping Python, Playwright, Requests HTML
Database PostgreSQL, AWS S3
Normalization Pandas, NumPy, Custom NLP matcher
Automation & Scheduling Apache Airflow, AWS Lambda
Visualization Tableau & Power BI
Delivery API FastAPI + JWT authentication

Dashboard & User Interface

Actowiz Solutions also designed a data dashboard to visualize price comparison in real time.Features included:

  • Side-by-side price cards for Zomato, Swiggy, and MagicPin.
  • Filters by cuisine, distance, offer type, or rating.
  • "Best Deal" badges highlighting the lowest final billing.
  • Historical price charts showing fluctuations for popular dishes.

The dashboard was integrated with mobile and web apps via API, delivering a consistent experience across devices.

Performance Metrics

Metric Before Project After Implementation
Data refresh time Manual (> 1 day) Every 10 min
Price accuracy ~ 70 % 97.8 % verified
Restaurant coverage Limited (800 outlets) 12 k + restaurants
API response latency > 4 s < 800 ms
User savings Unknown Avg 18 – 22 % per order

AI Enhancements

  • Duplicate Detection: Eliminated identical menus listed under multiple IDs.
  • NLP Matching: Mapped menu items with different naming patterns (e.g., "Paneer Roll Combo" vs "Combo Paneer Roll").
  • Dynamic Scheduler: Increased crawl frequency during lunch/dinner rush.
  • Anomaly Detection: Flagged sudden > 25 % price changes for verification.

Key Results & Insights

Navratri Mega Sale Price Tracking
Data Accuracy

Actowiz's system maintained > 97 % accuracy for all price points with minimal latency.

Pricing Trends

Zomato tended to offer the lowest base price for budget meals, while Swiggy had lower delivery fees on average. MagicPin offered the highest discount coupons on weekends.

Restaurant Insights

High-volume brands like Domino's and KFC maintained near-identical pricing, but premium local restaurants varied by as much as 12–15 % across apps.

User Behavior

Beta testing showed users chose the cheapest platform 70 % of the time when differences exceeded ₹ 20.

Business Impact
  • Boosted user trust through pricing transparency.
  • Enabled restaurants to align cross-platform pricing.
  • Laid the foundation for affiliate and cash-back revenue models.

Compliance and Ethics

Navratri Mega Sale Price Tracking

Actowiz Solutions adheres strictly to ethical data collection policies:

  • Scraping only publicly available menu data.
  • Respecting robots.txt and request rate limits.
  • Complying with GDPR and India's Data Protection Bill standards.
  • Providing transparent data-usage agreements to clients.

Scalability and Future Expansion

Navratri Mega Sale Price Tracking

The architecture was built for scalability.

Next phases included:

  • Integrating more platforms like EatSure and Uber Eats (UAE).
  • Introducing AI-based price forecasting models.
  • User notifications for price drops and flash discounts.
  • Launching an open API for third-party price tracking tools.

This positions the app as India's first live food price comparison engine powered by Actowiz Solutions.

Broader Industry Implications

For Consumers

Transparent pricing leads to better value and trust.

For Restaurants

Cross-platform pricing visibility helps maintain brand consistency and margin control.

For Aggregators

Such tools encourage healthy competition and fair pricing models.

Why Actowiz Solutions

  • Proven expertise in FoodTech and Quick Commerce data scraping.
  • AI-driven pipelines handling dynamic pages at scale.
  • Comprehensive delivery: data feeds, dashboards, and API integration.
  • Track record across Zomato, Swiggy, Uber Eats, DoorDash, and MagicPin data projects.
  • Client support and SLA guarantees for uptime and accuracy.

With robust experience in menu data scraping, delivery price monitoring, and discount analytics, Actowiz Solutions has become a trusted partner for FoodTech innovation.

Want to build your own real-time price comparison or food delivery data platform? Actowiz Solutions can power it with scalable web scraping APIs and AI-based price intelligence.
Contact Us Today!

Conclusion

This project proved that real-time food price comparison is not only technically possible but also a high-value consumer utility.By combining AI, web scraping, and smart data engineering, Actowiz Solutions enabled a startup to deliver live pricing transparency for Zomato, Swiggy, and MagicPin users.

Impact Highlights
  • 3 platforms integrated | 12 k + restaurants | 150 k + menu items monitored.
  • Near real-time updates every 10 minutes.
  • 97 % accuracy | 20 % average user savings.

The success of this solution positions Actowiz Solutions as a leader in Food Data Intelligence and Aggregator Price Monitoring across India and global markets.

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

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'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.
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“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
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“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!”
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Febbin Chacko
-Fin, Small Business Owner
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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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