Anthropic just shipped 1M token context windows for claude code and now for claude cowork. That’s more than the entire collection of the Harry Potter series. You can fit multiple contracts, financial statements, and internal documentation into a single prompt. Impressive, and based on the trajectory, we should expect it to keep growing.

Context windows this large are a meaningful step toward better document automation. But a larger context window is a necessary condition, not a sufficient one. Processing large volumes of content matters. Parsing unstructured documents accurately is the harder problem, and it’s the one that general-purpose tools still fail at.

The Document Quality Problem

Credit automations run on documents that were never designed for machines. Partially scanned PDFs where half the pages are images and the other half are native text. Excel files with merged cells, hidden sheets, and embedded charts carrying critical portfolio data. Word documents with six months of tracked changes from legal negotiations. PowerPoint decks used as data delivery formats, which happens more often than you’d think in loan sales.

When you upload these to the latest AI agents (Claude, ChatGPT, Copilot), the default parsing is surprisingly basic. Most rely on simple text extraction that strips tables of their structure, misreads scanned pages, and silently drops content it can’t parse. The model then answers questions confidently based on incomplete or garbled input. This is where hallucination and incomplete answers start: not in the model’s reasoning, but in the extraction layer.

For a software engineer asking an AI to summarise a research paper, this is a minor nuisance. For a credit professional reviewing a €200M loan portfolio where a misread amortisation schedule or a dropped covenant clause can mean real financial exposure, it’s a dealbreaker.

Why we built AI Document Analysis

We saw this early. Our clients process thousands of documents across transaction due diligence, portfolio monitoring, and regulatory reporting processes: loan agreements, financial statements, servicer reports, covenant compliance packs. These documents are inconsistent across originators/servicers, asset classes/products, jurisdictions, and vintages. A single due diligence exercise can involve PDFs that mix native text, scanned pages, and embedded images of tables, all in the same file.

AI Document Analysis is our answer. It’s a purpose-built document processing engine for credit work that handles the full complexity of real financial documents. Partially scanned PDFs get proper hybrid extraction, combining OCR and native text parsing in a single pass. Tables retain their structure, rows, columns, and hierarchy. Excel files are parsed with awareness of merged cells, multi-sheet layouts, and formula dependencies. Every extraction traces back to its source, so your team knows exactly where a number came from.

This isn’t just OCR with a language model on top. It’s a structured document pipeline: classify, extract, validate, review. Low-confidence results get flagged for human review. Analysts see the original document side by side with the extraction output. We call it the glass box approach: full traceability, no black box surprises. It powers due diligence automation, covenant monitoring, regulatory compliance, and report drafting across asset classes.

Context Needs Quality

Bigger context windows are useful. Being able to process an entire credit agreement in one pass, or compare multiple financial statements simultaneously, that’s a real capability gain. But context without quality is just more room for errors. The teams that will get the most out of these expanding model capabilities are the ones with infrastructure that feeds clean, structured, traceable data into them.

We’re building this at Accuria for credit professionals globally, currently supporting €110B+ in managed assets, performing and non-performing loans, across 28 countries. If this is the problem you’re trying to solve in your organisation, reach out at accuria.com or talk to us.

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