Custom web scraping services for data-driven businesses | Reliable data extraction & delivery since 2015
Joined September 2020
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Since 2015, we've helped businesses turn data chaos into advantage.
We build custom data pipelines around your goals — not templates. Tailored to your workflow, transparent and scalable as you grow.
Need help with web scraping or data extraction? DM us: bit.ly/499XEIM
How do you scale an AI recruitment platform over 8+ years?
DataOx helped Discovered ai grow to 900K+ candidates and 2.5K active client companies through SaaS development, integrations, and AI automation.
Read the full case study: data-ox.com/resources/case-s…
Reliable ML models depend on high-quality training data.
Effective datasets require clear requirements, relevant sources, quality checks, and regular updates.
Our guide covers ML data collection challenges and practical solutions.
Read more: data-ox.com/resources/blog/d…
Training data quality directly affects ML performance.
Even large datasets can contain inconsistencies or gaps that impact model results.
Our video covers 3 common data collection challenges and practical ways to address them.
Full guide:
data-ox.com/resources/blog/d…
Reliable web scraping requires regular monitoring of data collection, quality, and delivery.
Our checklist covers what to monitor, what to check, and how often to review your scraping pipeline.
Full guide: data-ox.com/resources/blog/w…
When does outsourcing web scraping make sense?
Frequent scraper failures, rising costs, scaling issues, difficult data access, and manual data quality checks can slow down product development.
Learn when to outsource and how to choose the right model: data-ox.com/resources/blog/o…
What does a ready-to-use dataset actually look like?
Explore data samples from Amazon, Airbnb, LinkedIn, Indeed, Reddit, and other sources. See the fields, structure, and output available for different use cases
Explore datasets and download a sample: data-ox.com/data-marketplace…
Web scraping outsourcing can mean hiring a freelancer, working with an agency, or using a managed service.
Which model fits your project?
We compare 4 outsourcing models and what to consider when choosing a provider.
Full guide: data-ox.com/resources/blog/o…
Web scraping can break for many reasons: website changes, anti-bot systems, pipeline failures, or data quality issues.
Here are 8 common scraping challenges and practical ways to address them.
Full guide: data-ox.com/resources/blog/c…
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: data-ox.com/resources/blog/c…
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:
startuprise.co.uk/how-dataox…
A successful Discord API request doesn't guarantee a usable dataset.
Incomplete coverage, missing context, and API limitations can affect data quality.
We break down 5 common challenges and how to address them.
Read the full article: data-ox.com/resources/blog/h…
Downloading 100 competitor product pages doesn’t give you 100 competitor prices.
A website ripper creates a local copy. It doesn’t create a structured dataset or track changes.
Where does a website ripper stop being enough? Read the comparison:
data-ox.com/resources/blog/w…
Annual comp surveys are 12 months stale before they reach your desk. Meanwhile, your competitors post salary ranges on Indeed & LinkedIn every day. Scraping that data = real-time benchmarking.
data-ox.com/?utm_source=X&ut…
#CompensationData #HRTech #JobMarket #TalentStrategy
