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GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => Array
                (
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                    [names] => Array
                        (
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                            [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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                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                            [zh-CN] => 美国
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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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                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

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            [traits] => Array
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                    [ip_address] => 216.73.216.150
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        )

    [continent:protected] => GeoIp2\Record\Continent Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 6255149
                    [names] => Array
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                            [de] => Nordamerika
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                            [es] => Norteamérica
                            [fr] => Amérique du Nord
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                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                )

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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
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                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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            [validAttributes:protected] => Array
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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
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                    [0] => queriesRemaining
                )

        )

    [registeredCountry: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
                        )

                )

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            [validAttributes:protected] => Array
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        )

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

    [traits:protected] => GeoIp2\Record\Traits Object
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                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
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                    [3] => domain
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                    [14] => isTorExitNode
                    [15] => mobileCountryCode
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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
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                            [fr] => Columbus
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                            [pt-BR] => Columbus
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                            [zh-CN] => 哥伦布
                        )

                )

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        )

    [location:protected] => GeoIp2\Record\Location Object
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            [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
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                    [validAttributes:protected] => Array
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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

In the US hospitality market of 2026, the intersection of dining and licensed beverage sales represents a multi-billion dollar opportunity. For businesses like yours—equipped with their own analytics and scrapers but needing a high-fidelity "Seed List"—the challenge lies in the fragmentation of state-level data. This case study explores how Actowiz Solutions architected a nationwide data pipeline to identify, verify, and monitor every licensed on-premise alcohol location in the United States, from bustling Manhattan wine bars to quiet suburban bistros.

The Challenge: Navigating the 50-State Alcohol Licensing Labyrinth

Navratri Mega Sale Price Tracking

Extracting a list of "all restaurants" is relatively simple; however, filtering for those specifically licensed for on-premise alcohol consumption is a different beast.

The Hurdles:
  • Decentralized Sources: Each state (and sometimes individual counties) has its own Alcoholic Beverage Control (ABC) board or Liquor Authority. There is no single "Master List" for the USA.
  • Dynamic Status: Licenses are issued, expired, or revoked daily. A static list becomes obsolete within weeks.
  • Format Inconsistency: State portals range from modern APIs to 1990s-era database search forms and unsearchable PDF registries.
  • On-Premise vs. Off-Premise: Distinguishing between a restaurant (on-premise) and a liquor store (off-premise) requires deep-link parsing and categorical validation.

The Actowiz Solutions Strategy: Scaling to 700,000+ Locations

Actowiz Solutions doesn't just "scrape"; we build Intelligent Data Ecosystems. For this project, we deployed a three-tier architecture.

Tier 1: The Regulatory Source Layer

Instead of relying on third-party aggregators (like Yelp or Google Maps), which often have outdated "has liquor" tags, we go to the primary source: State ABC Databases.

We developed 50+ unique crawlers, each tailored to the specific search query parameters of state registries.

We targeted specific license types, such as Full Liquor (4COP in FL, Type 47 in CA), Wine & Beer (2COP in FL, Type 41 in CA), and Tavern/Club licenses.

Tier 2: The Validation & Enrichment Layer

Once we have the license holders, we cross-reference them with digital presence data to provide the URLs you need.

  • Web-Match Engine: Our AI identifies the official website, social media profiles, and third-party delivery links (DoorDash, UberEats) for each license holder.
  • Status Verification: We confirm the business is "Active" and "Open," filtering out inactive corporate shells that hold licenses but lack physical operations.
Tier 3: The Deep-Data Layer (Optional for Clients)

For clients who choose our end-to-end service, we go beyond the URL to extract the Food Menus and Wine Lists. Using Computer Vision (OCR), we can even extract data from "Picture Menus" or PDF uploads that standard scrapers often miss.

Technical Implementation & Sample Data

To ensure your own scrapers can ingest our "Seed List" effortlessly, we deliver data in structured, machine-readable formats.

Sample Dataset: US Licensed On-Premise Locations
Entity Name License Type Address State Official URL Last Verified
The Gilded Grape On-Premise Wine & Beer 452 Broadway, NY NY thegildedgrape.com 2026-01-08
Blue Wave Seafood Full Liquor (Restaurant) 120 Ocean Dr, Miami FL bluewavemiami.com 2026-01-09
Sunset Social Club Tavern / Nightclub 88 Sunset Blvd, LA CA sunsetsocial.la 2026-01-05
Sample JSON: Extended Menu & Wine Data
{
  "business_name": "Vintage Vines Bistro",
  "license_status": "Active - On-Premise",
  "urls": {
    "official": "https://vintagevines.com",
    "menu_page": "https://vintagevines.com/dinner-menu"
  },
  "wine_list_sample": [
    {"label": "Chateau Margaux 2018", "type": "Red", "price_glass": "N/A", "price_bottle": "$850"},
    {"label": "Cloudy Bay Sauvignon Blanc", "type": "White", "price_glass": "$18", "price_bottle": "$65"}
  ]
}

Why Actowiz Solutions?

99.9% Data Accuracy

We don't tolerate "hallucinated" URLs. Our verification pipeline includes a Status-200 Check, ensuring every URL we provide is live and relevant.

Cost-Effectiveness

By utilizing our pre-built state crawlers, you avoid the massive R&D costs of building 50 separate scrapers. You pay for the outcome, not the infrastructure.

Ethical & Compliant Scraping

All data is collected following the latest USA Data Privacy Acts and CCPA guidelines, ensuring your business remains compliant when using our datasets for outreach or analysis.

Service Proposal: Project Scope & Timeline
1. Data Availability & Customization

We have the primary US licensed location data available as a "Ready-to-Ship" dataset, which can be custom-filtered based on:

  • Geography: (e.g., Only NYC, or West Coast Only).
  • License Class: (Full Liquor vs. Beer/Wine only).
  • Business Category: (Fine Dining vs. Bars/Nightclubs).
2. Estimated Cost & Timeframe

URL List Only (USA Nationwide): * Cost: Starting at $3,500 - $7,000 (depending on the depth of validation required).

Timeline: 5–7 business days for delivery.

Full Data Extraction (URLs + Menus + Wine Lists):

Cost: This is typically priced per 1,000 records or as a monthly subscription. Estimated $0.15 - $0.45 per location depending on volume.

Timeline: Initial batch in 10 days; recurring updates available.

Conclusion

Having your own scraper is a great asset, but a scraper is only as good as the targets you give it. By partnering with Actowiz Solutions, you ensure your internal tools are focused on the highest-value, most accurate URLs in the USA hospitality sector.

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:

Fintech / Digital Payments

Result

Accurate daily voucher &

cashback visibility across platforms

★★★★★

“Actowiz Solutions helped us automate daily voucher and cashback data collection across PhonePe, Paytm, Flipkart, and Hubble. The API-driven delivery significantly improved offer accuracy and operational efficiency.”

Product Manager, Fintech Platform (India)

✓ Daily voucher & cashback tracking via Push & Pull APIs

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
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Case Studies
Infographics
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