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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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                            [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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                    [iso_code] => US
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                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [location] => Array
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                    [longitude] => -83.0061
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            [postal] => Array
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            [registered_country] => Array
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                            [es] => Estados Unidos
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                            [ru] => США
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                                    [es] => 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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                    [geoname_id] => 6255149
                    [names] => Array
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                            [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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                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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    [country:protected] => GeoIp2\Record\Country Object
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                    [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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    [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
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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
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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                    [3] => isoCode
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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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            [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
                (
                    [ip_address] => 216.73.216.150
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
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            [validAttributes:protected] => Array
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                    [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
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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
                )

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

Client overview

Navratri Mega Sale Price Tracking

A mid-sized consumer electronics and lifestyle manufacturing group sells across multiple EU marketplaces and retail partners. Their portfolio includes:

  • Consumer electronics accessories (chargers, cables, small devices)
  • Lifestyle goods (bags, textiles, mixed-material products)
  • Rechargeable-battery products (power banks and battery-included devices)

They were preparing for the EU’s product sustainability and transparency direction, where product-level information must be accessible, structured, and shareable across the value chain through Digital Product Passports (DPPs). The client’s biggest concern was not “creating a PDF and calling it done,” but building a repeatable data system that could support:

  • Faster compliance response for each product category
  • Evidence-ready sustainability claims
  • Repair, reuse, and recycling information at SKU level
  • Retailer and marketplace data requests, without manual work

The client engaged Actowiz Solutions to build a scalable pipeline to collect, normalize, and validate DPP-ready product data from multiple internal and external sources.

Why Digital Product Passports matter in the EU now

The EU is rolling out a framework that enables Digital Product Passports under the Ecodesign for Sustainable Products Regulation (ESPR). The European Commission describes ESPR as introducing a Digital Product Passport (DPP) as a “digital identity” for products to improve sustainability, circularity, and information access across the value chain.

The ESPR itself is Regulation (EU) 2024/1781, published in the EU’s official legal repository (Official Journal via EUR-Lex).

Separately, the EU Battery Regulation introduces a “battery passport” concept with concrete timelines. For example, industry summaries consistently cite that from 18 February 2027, certain EV and industrial batteries (>2 kWh) must have a digital battery passport accessible via a QR code.

What this means for brands: DPP requirements will land category-by-category, and companies need a system to assemble product, material, compliance, and lifecycle data in structured formats and keep it updated.

The business challenge

The client’s DPP problem looked simple on paper (“create a passport”), but operationally it was messy:

  1. Fragmented product truth
    • Product specifications lived in ERP/PIM, but sustainability attributes lived across supplier spreadsheets, testing labs, and email threads.
    • Repair manuals and safety documents existed as PDFs with inconsistent naming and versions.
  2. Multi-tier supply chain gaps
    • Tier-2 and Tier-3 material data was incomplete or not standardized (adhesives, coatings, mixed materials).
    • Supplier declarations varied by geography and format.
  3. Retail and partner requests were accelerating
    • Large retailers asked for structured sustainability attributes.
    • Marketplaces began enforcing tighter listing and compliance documentation.
  4. No “single view” of lifecycle information
    • End-of-life instructions and recycling guidance were not mapped consistently to product variants.
  5. Scaling pressure
    • Hundreds of SKUs, multiple EU countries, frequent packaging updates.
    • Manual compilation was too slow and error-prone.

Actowiz Solutions approach

Actowiz delivered a DPP-ready data pipeline that combined:

  • Automated extraction (web + portals + supplier sites)
  • API-based ingestion (internal systems and partners)
  • Document parsing and metadata enrichment
  • Validation workflows and audit logs
  • Output formats designed for interoperability (JSON/CSV + API)

This approach aligned with how many DPP programs are evolving: structured, machine-readable outputs and strong data integration, not one-off documents.

Solution architecture (high level)

  1. Data source mapping
    Actowiz mapped all required data inputs into a DPP field framework:
    • Product identity: SKU, model, GTIN, batch/lot
    • Materials and composition: material breakdown, hazardous substances, packaging materials
    • Sustainability: recycled content, carbon footprint values (where available), durability indicators
    • Compliance evidence: declarations, test reports, certificates
    • Repair and end-of-life: repairability guidance, spare parts, recycling instructions
    • Supply chain links: supplier IDs, origin indicators, process metadata
  2. Collection and extraction layer
    Actowiz implemented:
    • Portal scraping for supplier compliance portals and document libraries
    • Marketplace and retailer page scraping to cross-check public-facing claims and attributes
    • Internal system connectors for ERP/PIM exports and document repositories
    • Incremental refresh to capture changes (new versions, updated PDFs, revised packaging)
  3. Normalization and entity resolution
    A rules + ML-assisted normalization process standardized:
    • Units (g, kg, Wh), material naming, and synonyms
    • Date formats and certificate expiry dates
    • Duplicate document detection (hashing + semantic similarity)
    • SKU-to-variant mapping (size/color/region packaging)
  4. DPP output builder
    Actowiz generated:
    • Category-specific DPP records
    • Public vs restricted views (role-based data exposure)
    • Data-carrier-ready payloads (QR code URL + minimal on-label fields)
  5. QA + auditability
    Because DPP data is compliance-facing, Actowiz built:
    • Validation checks (required fields, format checks, outlier detection)
    • Evidence links for each key attribute (source URL / document ID)
    • Audit trails (who changed what, when, and why)

Implementation workflow

Phase 1: Pilot (3–4 product lines)
  • Selected representative SKUs: electronics + textile/mixed materials
  • Built initial DPP schema and data rules
  • Produced sample “passport views” for internal teams and retailer partners
Phase 2: Scale (full catalog)
  • Expanded extraction to all suppliers and document sources
  • Introduced automatic versioning and alerts for changed files
  • Automated weekly refresh and exception reporting
Phase 3: Continuous compliance operations
  • Monthly governance reports
  • Coverage scorecards by supplier and category
  • Readiness pack for regulators/retailers: evidence + structured outputs

Sample data (for demo)

A) DPP JSON example (simplified)
{
  "dpp_id": "EU-DPP-CHG-2409-000184",
  "product": {
    "brand": "ExampleCo",
    "model": "FastCharge 30W",
    "gtin": "05012345678901",
    "sku": "FC30W-EU-WHT",
    "category": "electronics_accessory"
  },
  "identity": {
    "serial_supported": false,
    "batch_lot_supported": true,
    "manufacture_country": "CN",
    "market_region": "EU"
  },
  "materials": [
    {"component": "housing", "material": "PC", "weight_g": 42.5},
    {"component": "pins", "material": "Cu alloy", "weight_g": 8.2},
    {"component": "pcb", "material": "FR-4", "weight_g": 12.1}
  ],
  "sustainability": {
    "recycled_content_pct": 18,
    "packaging_recyclable_pct": 92,
    "carbon_footprint": {
      "value": 1.84,
      "unit": "kgCO2e",
      "scope": "cradle_to_gate",
      "method": "ISO 14067"
    }
  },
  "compliance": {
    "declaration_of_conformity_url": "https://example.com/docs/doc-184.pdf",
    "certificates": [
      {"type": "CE", "doc_id": "CERT-CE-7782", "expires_on": "2028-06-30"},
      {"type": "RoHS", "doc_id": "CERT-RoHS-1093", "expires_on": "2027-11-15"}
    ]
  },
  "repair_and_eol": {
    "repairability_score": 7.5,
    "spare_parts_available_months": 36,
    "recycling_instructions_url": "https://example.com/recycle/fc30w"
  },
  "data_carrier": {
    "qr_url": "https://dpp.example.com/EU-DPP-CHG-2409-000184",
    "label_fields": ["model", "gtin", "recycling_instructions_url"]
  },
  "provenance": {
    "collected_at": "2026-02-02T10:15:00Z",
    "sources": [
      {"type": "supplier_portal", "source_ref": "SUP-PORTAL-ACME"},
      {"type": "document_repo", "source_ref": "DOCS-v3"}
    ]
  }
}
B) Tabular sample (CSV-style)
dpp_id sku gtin category recycled_content_pct packaging_recyclable_pct repairability_score ce_doc rohs_doc qr_url
EU-DPP-CHG-2409-000184 FC30W-EU-WHT 05012345678901 electronics_accessory 18 92 7.5 DOC-184 DOC-191 /EU-DPP-CHG-2409-000184
EU-DPP-TXT-2409-000067 BAG-TRVL-22L 05012345670011 textiles_mixed 12 88 6.2 DOC-067 DOC-071 /EU-DPP-TXT-2409-000067
C) QR “label payload” example (minimal)
{
  "gtin": "05012345678901",
  "model": "FastCharge 30W",
  "dpp_url": "https://dpp.example.com/EU-DPP-CHG-2409-000184",
  "version": "1.3"
}

What Actowiz scraped and validated

Depending on category and availability, the pipeline extracted:

  • Product listing attributes (dimensions, weight, wattage, material claims)
  • Compliance documents (Declarations of Conformity, test reports, certificates)
  • Sustainability statements (recycled content, packaging claims)
  • Repair content (manuals, exploded views, parts lists)
  • End-of-life guidance (recycling instructions, take-back programs)
  • Supplier declarations (restricted substances, material composition)

And for batteries (where applicable), it aligned the dataset to passport-style requirements and timelines referenced in EU battery passport guidance.

Outcomes and impact

Within the first rollout quarter, the client achieved:

  1. Time-to-passport reduced dramatically
    • From days per SKU (manual compilation) to hours or less (automated + QA exceptions)
  2. Higher data completeness
    • Mandatory fields coverage improved via supplier follow-ups driven by gap reports
  3. Fewer compliance fire drills
    • Versioning and expiry alerts reduced “last-minute” certificate chasing
  4. Retailer readiness
    • Structured outputs made it easier to respond to partner questionnaires and onboarding forms
  5. Circularity enablement
    • Repair and recycling content was finally mapped to exact SKUs, not generic product families

Why this is a strong fit for Actowiz Solutions services

This case study is a clean example of “compliance-grade data engineering powered by extraction”:

  • Web scraping + portal scraping
  • Document extraction and structuring
  • Product master data enrichment
  • Data QA, audit logs, and change tracking
  • Interoperable outputs (API + datasets) that business teams can actually use

Deliverables provided to the client

  • DPP-ready dataset (CSV + JSON)
  • DPP API endpoints for internal apps/partners
  • Supplier completeness scorecards
  • Certificate expiry monitoring
  • QR payload generator (for label workflows)
  • Evidence pack links per SKU (audit readiness)

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.
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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
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1 min
★★★★★
“Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing highly recommended!”
Thomas Gallao
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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The Race for "Now": Noon Minutes vs. Talabat Mart for the UAE’s Quick-Commerce Crown

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