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GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [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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            [continent] => Array
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                        (
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                            [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
                        )

                )

            [country] => Array
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                    [iso_code] => US
                    [names] => Array
                        (
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                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [location] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [postal] => Array
                (
                    [code] => 43215
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            [registered_country] => 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
                        )

                )

            [subdivisions] => Array
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                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
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                                    [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
                                )

                        )

                )

            [traits] => Array
                (
                    [ip_address] => 216.73.216.161
                    [prefix_len] => 22
                )

        )

    [continent:protected] => GeoIp2\Record\Continent Object
        (
            [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
        (
            [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
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                    [0] => confidence
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                    [3] => isoCode
                    [4] => names
                )

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    [locales: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
                (
                    [0] => queriesRemaining
                )

        )

    [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
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                    [0] => en
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            [validAttributes:protected] => Array
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                    [0] => confidence
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                    [3] => isoCode
                    [4] => names
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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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                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                    [5] => type
                )

        )

    [traits:protected] => GeoIp2\Record\Traits Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [ip_address] => 216.73.216.161
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

            [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
                )

        )

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

                )

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

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

        )

    [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
        (
            [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
                        (
                            [0] => en
                        )

                    [validAttributes:protected] => Array
                        (
                            [0] => confidence
                            [1] => geonameId
                            [2] => isoCode
                            [3] => names
                        )

                )

        )

)
 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

The rapid adoption of electric vehicles across Europe has created an urgent need for efficient charging networks. Cities require comprehensive insights into station availability, distribution, and utilization to plan infrastructure investments strategically. EV Charging Infrastructure Mapping enables urban planners, energy providers, and fleet operators to monitor and optimize EV charging networks in real time. Actowiz Solutions leveraged its Web Scraping API to extract large-scale data from multiple sources, including ChargePoint and EVgo, ensuring a complete understanding of the European EV ecosystem. By integrating structured datasets and advanced mapping techniques, the solution provided actionable insights into underserved areas, utilization trends, and potential growth zones. Through EV Charging Infrastructure Mapping, stakeholders could identify locations with the highest demand, prioritize new station installations, and optimize existing infrastructure, ultimately supporting a 35% projected growth in EV adoption and ensuring European cities stay ahead in the transition to sustainable transportation.

The Client

The client, a leading European urban mobility consultancy, specializes in planning and optimizing EV networks for municipalities and energy providers. Their objective was to support city governments in deploying charging infrastructure that meets current and projected EV adoption rates. With an extensive portfolio of projects across Europe, the client needed a robust solution to map the entire EV network landscape, including privately operated and public stations. They sought insights into EV Charging Station Mapping, station utilization patterns, and real-time availability to drive data-backed decisions for expansion planning. Additionally, the client wanted to benchmark performance against competitors and identify high-demand zones where infrastructure investments could yield maximum returns. By collaborating with Actowiz Solutions, the consultancy aimed to leverage ChargePoint & EVgo Station Mapping Europe data to gain a comprehensive overview of existing stations, their capacities, and distribution patterns, ensuring strategic and future-ready deployment of EV infrastructure across multiple urban centers.

Key Challenges

Key Challenges-01

The client faced several challenges in understanding and optimizing the European EV network. First, the lack of centralized data made it difficult to identify all operating charging stations, including both public and private networks. Data from ChargePoint, EVgo, and other operators was fragmented and inconsistent, requiring standardization for meaningful analysis. Real-time monitoring was also a challenge, as EV station availability fluctuates frequently, affecting user experience and infrastructure planning.

Additionally, urban areas exhibited uneven distribution of charging stations, with high-demand zones underserved and low-demand zones oversaturated. The client needed a clear picture of station density, utilization patterns, and projected growth areas to allocate resources efficiently. Existing mapping solutions were unable to provide insights into competitor networks, station capacities, and utilization trends simultaneously. Extracting data from multiple platforms without violating usage policies posed another hurdle, necessitating advanced and compliant scraping techniques.

Ultimately, the client required a scalable, automated, and precise solution that could integrate multiple datasets into a single actionable framework. This framework needed to enable European EV Network Mapping Using Scraping, identify gaps, and support strategic decision-making for a rapidly growing EV market.

Key Solutions

The-Client

Actowiz Solutions implemented a comprehensive approach to address the client’s challenges. Using EV Charging Infrastructure Mapping, the team first consolidated public and private charging station data across Europe. By deploying the Web Scraping API, they extracted structured data from ChargePoint and EVgo, including station locations, capacities, real-time availability, and operational status.

To ensure accurate insights, the team implemented Scrape EV Charger Availability Data, enabling the client to monitor utilization trends, queue times, and peak usage hours. This allowed for precise identification of high-demand zones where additional stations were needed. By integrating historical usage data with real-time updates, Actowiz provided predictive analytics for future infrastructure planning.

Additionally, Web Scraping Services were employed to collect data from other regional networks, providing a holistic view of the European EV landscape. Competitor benchmarking was incorporated, comparing station density, service coverage, and utilization rates across urban areas. The solution also included EV Infrastructure Data Extraction for mapping station types, power ratings, and accessibility features, ensuring detailed insights for strategic deployment.

Finally, the team delivered interactive dashboards that enabled the client to visualize Scrape ChargePoint and EVgo data for EV station mapping effectively. Decision-makers could filter by city, station type, and availability to optimize infrastructure expansion. The approach also supported Scrape ChargePoint & EVgo for mapping of underserved regions, enabling a 35% projected growth in network coverage and improved user accessibility across European cities.

Client Testimonial

“Actowiz Solutions transformed our approach to EV infrastructure planning. Their EV Charging Infrastructure Mapping solution, combined with real-time station data from ChargePoint and EVgo, allowed us to identify growth opportunities we couldn’t see before. The dashboards are intuitive, the data is accurate, and their team provided excellent support throughout the project. Thanks to their expertise, we can now strategically expand charging networks across multiple European cities, ensuring optimal utilization and enhanced user experience. Actowiz truly understands the complexities of EV networks and delivered actionable insights that will drive our projects forward.”

— Senior Urban Mobility Consultant

Conclusion

The European EV market is expanding rapidly, and efficient infrastructure planning is crucial to meet rising demand. Through EV Charging Infrastructure Mapping, Actowiz Solutions helped the client gain comprehensive insights into station availability, utilization, and distribution patterns across urban centers. By combining ChargePoint & EVgo Station Mapping Europe with predictive analytics, historical trends, and real-time updates, the solution revealed a 35% growth opportunity in key zones.

Leveraging European EV Network Mapping Using Scraping, EV Infrastructure Data Extraction, and interactive dashboards, the client can now optimize deployments, enhance accessibility, and reduce gaps in coverage. This data-driven approach ensures informed decision-making, supports future expansion, and strengthens competitiveness in the EV ecosystem.

Unlock the potential of EV infrastructure planning today. Use EV Charging Infrastructure Mapping with Actowiz Solutions to analyze, optimize, and expand charging networks across Europe with precision and efficiency.

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

Actowiz Insights Hub

Actionable Blogs, Real Case Studies, and Visual Data Stories -All in One Place

All
Blog
Case Studies
Infographics
Report
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Jan 22, 2026

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Scraping Grab Hotel Listings enables real-time access to hotel prices, availability, ratings, and amenities, helping businesses track trends, optimize pricing

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How We Helped a Brand with Scraping UAE Grocery Chain Data for SKU-Level Monitoring of 20K+ Items, Updated Daily

Scraping UAE Grocery Chain Data enables brands to monitor 20K+ SKUs daily, track pricing, stock levels, and trends for smarter grocery retail decisions.

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Malaysia GrabFoods Market Analysis - City-Wise Food Delivery Demand and Pricing Trends

Malaysia GrabFoods market analysis delivers insights into pricing trends, restaurant availability, demand patterns, and competitive dynamics

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Jan 22, 2026

Scraping Grab Hotel Listings to Track Room Types, Amenities & Ratings

Scraping Grab Hotel Listings enables real-time access to hotel prices, availability, ratings, and amenities, helping businesses track trends, optimize pricing

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Jan 21, 2026

How Scraping Product & Price Data from DMart Helps Track 30% Faster Price Changes in Indian Retail?

Scraping Product & Price Data from DMart enables real-time price tracking, product comparison, and smarter pricing decisions for India’s leading retail platform.

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Jan 20, 2026

Extract DoorDash API for Location-Wise Menu - Unlocking Hyperlocal Food Data for Your App

Learn how to Extract DoorDash API for Location-Wise Menu to access hyperlocal food data, optimize apps, and deliver personalized dining experiences.

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How We Helped a Brand with Scraping UAE Grocery Chain Data for SKU-Level Monitoring of 20K+ Items, Updated Daily

Scraping UAE Grocery Chain Data enables brands to monitor 20K+ SKUs daily, track pricing, stock levels, and trends for smarter grocery retail decisions.

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How We Tracked Menu and Service Changes When Scrape Food Delivery App in India Benchmarking Swiggy vs Zomato Pricing & Delivery Times

Learn how we tracked menu and service changes when scrape food delivery apps in India, benchmarking Swiggy vs Zomato pricing and delivery times for data-driven insights.

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Reducing Price Gaps Across Indian Cities Using Flipkart Minutes Quick Commerce Intelligence

Flipkart Minutes Quick Commerce Intelligence delivers real-time insights on pricing, inventory, delivery speed, and trends to power smart retail decisions.

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Malaysia GrabFoods Market Analysis - City-Wise Food Delivery Demand and Pricing Trends

Malaysia GrabFoods market analysis delivers insights into pricing trends, restaurant availability, demand patterns, and competitive dynamics

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The 2026 Food & Quick Commerce Intelligence Report

10-minute delivery se lekar AI-driven dark stores tak, Actowiz Solutions ki 3000-word research report mein dekhiye Food & Q-commerce ka bhavishya aur data trends.

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The 2026 Energy & Utilities Data Intelligence Report

Drive the green transition with data. Actowiz Solutions reveals how AI-driven scraping and real-time grid analytics are optimizing the 2026 energy landscape.

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