AI systems are becoming more capable every month. But when it comes to getting the right data, such as research papers, financial filings, or structured information from the web, they still struggle. The problem is not the model architecture. It is access.
In this blog post, we explain why AI systems cannot reliably get the information they need. We break down the four biggest obstacles and what needs to change for AI to reach its full potential.
TL;DR
- AI systems can reason, summarise, and generate, but they still struggle to access the underlying information they need. Poor data access is a limiting factor across nearly every use case.
- Web content is noisy and fragmented. Research papers are locked behind paywalls or stripped of structure. Financial filings are released in messy formats, and tables, charts, and code are often lost entirely.
- Most retrieval tools offer only shallow results such as titles, snippets, or partial text, with little support for structured or multimodal data. This makes it difficult to build reliable and agent-ready systems.
- Valyu solves this by connecting AI to the web through an AI-native search engine, across domains like research, finance, and healthcare. It supports tool-calls, precise search parameters, and retrieval of both text and images.
1. Why AI Tools Struggle to Use Web Content
The internet seems like the most obvious place for AI to find information. It is massive, constantly updated, and covers nearly every topic. Most AI systems today, including tools like ChatGPT with browsing or Perplexity, rely on the web as their default source of truth.
But most websites are not designed for machines. They are designed for people. Here is what goes wrong:
- Pages are full of ads, cookie banners, navigation bars, and scripts that distract from the actual content
- The same article is copied across dozens of domains, making it hard to know which version is original or reliable
- Information is often split across multiple sections, buried in inconsistent HTML, or hidden behind paywalls
- Most search APIs return only titles, snippets, and URLs- not the full content that an AI needs to understand the context
- Scraping tools break easily because websites change layouts, block bots, or throttle traffic
As a result, even simple tasks like summarising a blog post or extracting a product comparison become error-prone. AI systems can query the web, but what they get back is often too shallow, too noisy, or too incomplete to be useful.
2. Why AI Systems Fail to Access Research Papers
Research papers contain some of the most valuable knowledge in fields like medicine, science, law, and engineering. Unlike blogs or news articles, they are written by subject-matter experts, peer-reviewed, and backed by data. They contain the primary evidence behind how things work, what has been proven, and where the edge of current knowledge lies.
For AI systems trying to reason about complex topics, this kind of information is essential, but hard to access and even harder to use.
Here is where the breakdown happens:
- Most academic content is locked behind paywalls and restrictive licenses
- Open-access repositories usually only provide abstracts, not full papers
- When full text is available, it often comes in PDF or XML, which is difficult for machines to parse cleanly
- Important information is hidden in tables, figures, appendices, or footnotes
- The structure of papers varies between publishers, making the retrieval of specific sections like methodology or results inconsistent
AI tools that rely on abstracts or summaries miss the substance that makes research useful. They lose the ability to trace claims back to the source, evaluate the quality of the evidence, or compare findings across studies. That missing depth limits what AI can reliably do in knowledge-heavy fields.
3. Why Financial Filings and Market Data Are Hard for AI to Use
Financial filings, earnings reports, and market data are critical for many AI use cases. These include investment research, compliance monitoring, corporate intelligence, and financial journalism. But most of that information is difficult to access, and even harder to structure in a way that machines can use effectively.
Here are the main issues:
- Regulatory filings like 10-Ks or 8-Ks are often released in formats like PDF or raw HTML, with inconsistent structure across companies
- Some data is available in XML, but parsing and normalising it requires significant engineering effort
- Real-time feeds are expensive and usually locked behind proprietary platforms or APIs
- Global filings are fragmented across jurisdictions, languages, and formats, with no shared standard
- Even basic metrics like revenue, risk factors, or executive compensation are buried in different sections and labelled inconsistently
This creates major overhead for developers building AI systems that need financial context. Instead of focusing on product quality or insights, teams spend time and resources just trying to get the raw data into a usable format.
4. Why AI Misses Value in Tables, Charts, and Code Blocks
Not all valuable information comes in paragraphs. In fields like healthcare, finance, engineering, and law, some of the most important data is presented as tables, charts, images, or code blocks. This is known as multimodal content. Most AI systems still struggle to retrieve or use it properly.
Several factors make this difficult:
- Tables are often flattened or lost entirely when documents are scraped or converted to plain text
- Figures and charts are treated as images, with no structured metadata or explanation
- Code snippets may be mixed with prose or formatted inconsistently, making them hard to extract
- PDFs often contain multiple data formats on the same page, with no machine-readable structure
- Existing APIs usually return only text, leaving out non-text elements completely
When AI systems miss or mangle this kind of content, they lose access to the most precise part of the information. This reduces accuracy, especially in domains where the structure and layout of the data carries meaning.
5. What AI-Ready Data Access Should Look Like
For AI systems to work reliably, they need better access to the information they depend on. Not just more data, but higher-quality data in the right format.
At a minimum, this means:
- Full-text access instead of summaries or snippets
- Clean structure so key sections like tables or figures are preserved
- Consistent formatting across domains, languages, and content types
- Real-time updates so models are working with current information
- Attribution and licensing so the data can be used and trusted
- Support for non-text formats like charts, tables, and code
Right now, developers are forced to patch this together from multiple sources. That takes time, budget, and maintenance work that could be better spent on building the actual product.
6. How Valyu Makes Structured Data Accessible for AI Systems
At Valyu, we’re building infrastructure that helps AI systems access the information they need — cleanly, quickly, and at scale. Over the last six months, we’ve released features that directly address the gaps in web content, research papers, financial data, and multimodal content.
Here’s how:
Structured Web Content Retrieval
Valyu’s WebSearch API returns full-page content in clean, structured formats like Markdown or JSON. It removes popups, ads, and layout clutter, making the text easy for AI systems to understand and cite. Results include source links and timestamps so they can be traced back when needed.
Full-Text Research Integration
We’ve partnered with academic publishers and collective rights organisations to index research content beyond abstracts. This includes enriched metadata, consistent formatting, and support for retrieving full sections like methods or results. That gives AI models more depth and traceability when reasoning across disciplines.
Parsed and Searchable Financial Filings
Our financial index covers key regulatory documents like 10-Ks and 8-Ks. These are parsed, sectioned, and returned in structured formats that make it easy to extract risk factors, management commentary, and financial tables — without needing to scrape or preprocess raw PDFs.
Multimodal Retrieval Support
We’ve expanded our retrieval layer to include tables, code blocks, and other non-text formats. This helps AI systems access content that would otherwise be flattened or lost during standard crawling. The structure is preserved, so the output remains usable and accurate.
Agent-Friendly API Design
Our API is built for use inside AI workflows. It supports tool calls, clean error handling, and low-latency responses. Developers can also use search parameters like relevance_threshold, max_results, or included_sources to control exactly what the model retrieves and how much.
Conclusion: Unlocking Better AI Performance Starts with Better Data
AI systems have advanced rapidly, but their ability to generate high-quality output still depends on something basic: access to the right data.
Today, that access is broken. The information exists, but it is locked behind paywalls, scattered across formats, or stripped of structure. Developers are forced to build around these gaps, adding time, cost, and complexity to every product.
Improving data access isn’t a side issue; it is the foundation for building AI tools that are reliable, explainable, and useful in the real world.
That is what we are focused on at Valyu. Making high-quality, structured, and machine-ready content accessible through a single API. So developers can spend less time fixing the data layer and more time building what comes next.