In AI, model performance hinges almost entirely on the quality and diversity of its training data. A report by Akaike suggests that poor data quality, rather than model architecture itself, is the primary culprit for sub-standard results, responsible for 87% of AI project failures.
Organizations with access to high-quality, diverse training data have a better chance at beating competitors on model performance benchmarks.
With the levelling playing field, the relevant question for AI labs these days is less "which model architecture should we follow?" and more of "where do we get the best training data?"
The web is still a relevant source of high-quality, diverse training data
The web contains vast amounts of valuable training data - multimedia, multilingual content, research implementations - but sourcing it reliably, at the speed and scale AI teams need, requires specialized infrastructure and expertise.
Organizations that solve this challenge systematically will build superior AI models.
How leading AI teams build superior models with web data
The path forward starts with three concrete approaches.
1. Multimedia data acquisition: Unlocking value beyond text
Text data powered the first wave of machine learning. But as text-based training becomes commonplace, models’ topic knowledge reach their limits. Acquiring multimedia training data is becoming essential for building competitive models.
But multimedia data collection is fundamentally different from text scraping.
- Multimedia files are massive compared to HTML pages. At production scale, downloads become so heavy they strain infrastructure for processing, storage, and transfer.
- Furthermore, ingesting superfluous multimedia data hurts projects. Teams need systems that retrieve and evaluate assets on the fly, filtering out irrelevant or low-quality content before delivery.
A robust multimedia pipeline has to account for these factors from the beginning.
Example: Power to gather multimedia
One AI company building vision-language models found that sourcing diverse video and image content at scale was its primary bottleneck. It needed to collect hundreds of millions of images across multiple platforms and tens of millions of videos from diverse sources to train models that could understand visual content across different contexts and cultures.
Rather than building custom infrastructure to handle this scale - managing bandwidth, storage, and compute costs across petabytes of data - the company partnered with Zyte Data.
- Zyte's infrastructure handled the daily ingestion of massive video volumes while streaming processed content directly to their storage buckets.
- Computer vision-powered deduplication ensured the company wasn’t paying to store duplicate or near-duplicate content.
Within months, the company had access to a diverse, high-quality multimedia datasets that would have cost more to acquire in-house. The team could focus on model architecture and training instead of infrastructure management.
2. Multilingual model development: Building balanced language coverage
Building multilingual models requires balanced, high-quality data across languages. Balanced coverage is achieved when each language has enough representation that the model performs consistently across all of them.
That requires continuous collection of regional sources from across the globe. Regional sources capture local variations and cultural context. But popular languages tend to dominate publicly available sources of training data, while less popular ones are underrepresented.
Insufficient linguistic input can hurt AI models’ ability to speak less popular languages. Those trained on imbalanced language input see 20% to 40% performance variance across languages while those trained with balanced multilingual training data boast less than 5% performance variance.
Example: Model strength through linguistic breadth
An AI company working to build models in underrepresented languages found that Hindi news datasets were scarce. It came to Zyte, looking to build custom extraction pipelines specifically for Hindi-language sources in a bid to close the quality parity gap with English datasets.
Zyte Data, Zyte’s done-for-you data-gathering service, handles language-specific encoding quirks, formatting, and quality issues, ensuring consistent data quality regardless of source language or region.
Zyte’s team also leveraged Zyte API to handle access management while returning the language of the content through the inLanguage attribute.
1{
2 "url": "https://example-regional-news.com/article",
3 "article": {
4 "headline": "स्थानीय बाजार में नए रुझान",
5 "articleBody": "...",
6 "inLanguage": "hi",
7 "datePublished": "2026-04-26T10:00:00Z",
8 "author": "Regional Correspondent"
9 }
10}Knowing the language of extracted web data, thanks to the inLanguage field, allows a company to count and audit training data for linguistic diversity.
Organizations tapping into this capability can now confidently expand into new markets, knowing their models will perform consistently across languages and regions, reducing market entry risk and accelerating global expansion.
3. Research-driven development: Staying ahead of emerging techniques
AI techniques evolve rapidly. New architectures, training methods, and approaches are emerging continually. The window between research publication and mainstream adoption is getting narrower, and teams that adopt emerging techniques early gain competitive advantage.
But building models with emerging techniques requires access to research implementations and datasets. Research papers and implementations are scattered across academic repositories, GitHub, and research platforms, in a variety of formats, such as PDFs and static HTML were never meant to own.
Zyte Data can deliver data feeds of metrics such as stars, forks, and commits on public repositories to help teams distinguish signal from hype, identifying high-impact techniques gaining real adoption. This data helps teams surface high-signal research papers and reference implementations, and to eliminate the need for research teams to manually hunt through scattered platforms and repositories.
1{
2 "repository": {
3 "name": "mixture-of-experts-implementation",
4 "stars": 4521,
5 "forks": 892,
6 "lastCommit": "2026-04-25T14:30:00Z",
7 "topics": ["machine-learning", "moe", "pytorch"],
8 "license": "MIT"
9 }
10}Winning teams adopt emerging techniques early, acquiring advantage through technical innovation. By automating the discovery and tracking of research implementations, AI teams can accelerate their research and development cycles and maintain their competitive edge.
Laying the foundations for superior models
As AI models proliferate, the differentiator will be the quality and diversity of training data. Organizations that gain access to robust data acquisition infrastructure now will have a structural advantage for years to come.
Whether it's unlocking the value of multimedia data, ensuring multilingual performance parity, or staying ahead of research trends, reliable web data extraction is the foundation of superior AI models.
Teams that replace scraping debt with reliable data supply will build the next generation of competitive models.
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