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DataOx

Name: DataOx
Address: Vesivärava tn 50/201, Tallinn, 10152, EE
Telephone: +380981590722
Price range: ££

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DataOx

Custom web scraping services for data-driven businesses | Reliable data extraction & delivery since 2015

21/10/2026

How do you scale an AI-powered recruitment platform over 8+ years?

DataOx has partnered with Discovered ai to develop and improve its recruitment platform, including SaaS development, system integrations, and AI-driven automation.

Today, the platform supports over 900K candidates and 2.5K active client companies, with 120K assessments completed in the past year.

Our case study covers the challenges, solutions, technologies, and results of this long-term partnership.

Read the full case study: https://data-ox.com/resources/case-studies/hr-software-development/

19/10/2026

High-quality data is essential for reliable machine learning results.

Building an effective ML dataset requires clear requirements, relevant data sources, consistent quality checks, and regular updates as model needs evolve.

Our latest article covers the main challenges of ML data collection and practical approaches to building datasets that support real-world applications.

Read the full guide on DataOx: https://data-ox.com/resources/blog/data-collection-for-machine-learning/

16/10/2026

The quality of training data has a direct impact on machine learning performance.

Even a dataset with millions of records can have gaps in real-world scenarios or inconsistencies that affect model results.

To build reliable ML datasets, teams need clear data requirements, consistent quality checks, and a way to identify gaps based on model performance.

In this video, we cover 3 common data collection challenges and practical ways to address them.

For a closer look at building reliable data collection pipelines for machine learning.

Read the full guide on DataOx:
https://data-ox.com/resources/blog/data-collection-for-machine-learning/

14/10/2026

Reliable web scraping requires regular monitoring of data collection, quality, and delivery.

Our checklist covers the key areas to monitor, what to check, and how often to review them to keep your data pipeline running reliably.

Save the checklist for your next scraping project.

Read the full guide on DataOx: https://data-ox.com/resources/blog/web-scraper-monitoring-maintenance-checklist/

12/10/2026

When does outsourcing web scraping make sense?

As data requirements grow, maintaining scraping infrastructure in-house can put more pressure on engineering teams.

5 signs it may be time to consider a different approach:

• Your team can’t scale scraping fast enough
• Scrapers keep breaking
• Infrastructure costs are increasing
• New data sources are becoming harder to access
• Data quality requires too much manual work

These challenges can affect engineering capacity, costs, and product development.

Our latest article covers the main web scraping outsourcing models and what to consider when choosing between them.

Read the full article: https://data-ox.com/resources/blog/outsourcing-web-scraping/

08/10/2026

What does a ready-to-use dataset actually look like?

Explore downloadable data samples across different industries and use cases in the DataOx Data Marketplace, including examples from Amazon, Airbnb, LinkedIn, Indeed, Reddit, and other sources.

See the fields, structure, and output that can be collected. Dataset content, sources, structure, and delivery format can also be adapted to specific project requirements.

Explore the datasets and download a sample: https://data-ox.com/data-marketplace/

06/10/2026

Outsourcing web scraping can take different forms, from hiring a freelancer to using fully managed services.

The right model depends on your project’s scale, complexity, and how much of the data collection process you want to manage internally.

In this article, we compare 4 web scraping outsourcing models and the key factors to consider when choosing a provider.

Read the full guide on DataOx: https://data-ox.com/resources/blog/outsourcing-web-scraping/

02/10/2026

Web scraping can fail at many points, from website changes and anti-bot systems to pipeline failures and data quality issues.

In this carousel, we break down 8 common web scraping challenges and practical ways to address them.

Save the checklist for your next scraping project.

Read the full guide on DataOx: https://data-ox.com/resources/blog/challenges-in-web-scraping/

29/09/2026

Web scraping comes with challenges, from website structure changes to data quality issues.

In this video, we cover 5 common challenges and practical ways to address them.

For a detailed breakdown of all 8 challenges and their solutions, read our full guide: https://data-ox.com/resources/blog/challenges-in-web-scraping/

24/09/2026

DataOx founders Sergii Klius and Dmitriy Forys share their perspectives on the challenges of data acquisition and how the company helps startups build reliable data infrastructure.

Read the full story on Startup Rise:

https://startuprise.co.uk/how-dataox-helps-data-driven-startups-get-competitive-advantage-in-the-market/

Address

Vesivärava Tn 50/201
Tallinn
10152

Telephone

+380981590722

Website

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