The hidden bottleneck

The LLM problem you don't realise you have

Most people assume: "If I upload a file to ChatGPT / Claude, it will read it all and reason over the whole thing." In practice, LLMs are excellent at reasoning, but they are not a magical "unlimited document digester". They need a well-prepared, machine-readable representation of your data to be reliably data-based.

The easy explanation

Think of an LLM as a brilliant analyst with a very small desk.

  • You can hand them a stack of documents, but they can only keep a limited amount open on the desk at once.
  • If the documents are messy (scans, PDFs with odd layouts, tables, columns, footers, mixed languages), parts get misread, dropped, or flattened.
  • Some documents, especially older or rarer formats, simply cannot be opened at all.
  • If you ask a question, they may respond confidently using what's currently visible on the desk, rather than what's in the whole stack.
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What you actually need is a filing system:

  • Every page is digitised properly (OCR if needed)
  • Structure is preserved (tables, headings, sections, key-value fields)
  • You can pull the right pieces at the moment you ask the question

That's what "unstructured → LLM-ready" unlocks.

Who needs this (even if they don't realise it yet)

If any of these sound familiar, you're already hitting the wall.

Developers building anything "document-backed"

If you're building:

  • A RAG chatbot over docs
  • An internal search / knowledge base
  • An agent that drafts from a corpus
  • Analytics, extraction, or automation

…you need structured ingestion. Without it, your LLM app becomes "smart-sounding", but not reliably grounded.

Anyone with large or messy corpora

You need this the moment you have:

  • More than a handful of files
  • Long PDFs (dozens/hundreds of pages)
  • Scans/images
  • Tables/forms
  • Inconsistent formats and languages

This is exactly where "upload to ChatGPT" starts producing surprises.

People who care about traceability

If you need:

  • Citations to exact pages
  • Reproducibility ("why did you say that?")
  • An audit trail of how data was extracted
  • Confidence that the answer comes from your data

…you need a pipeline that preserves provenance and makes retrieval auditable.

What it unlocks

"Data-based" LLM answers (not vibes)

When your corpus is canonicalised, the LLM can answer with:

  • The right snippets, from the right sources, consistently
  • Explicit references (page/section/table)
  • Less hallucination (and you can Reverse RAG to double check)

Coverage and fewer "silent misses"

The real failure mode isn't the model saying "I don't know". It's the model missing a relevant part of your files and not realising it.

A proper structuring pipeline lets you:

  • Process every page deterministically
  • Record the steps taken
  • Keep a manifest of what was ingested and any issues that arose

That's how you avoid surprises.

Agents that can actually do things

"Agentic" workflows (multi-step LLM tools) need state and structure:

  • Don't read the whole document, just query the specific JSON field
  • Someone scanned a document to .tiff? Here's the list of pages (and not just the first)
  • Or perhaps an rtf file that will burn tokens each time it's read — unless we extract the text once and store it

Agents become dramatically more reliable when they operate on structured objects, not raw, bloated text blobs.

Make it make sense technically

(without going full ML)

Why "ChatGPT can digest everything" is false in practice

Context window limits

There are hard limits to how much data an LLM can read at once. Even within those are soft limits, where text in the middle gets "lost" as the model focuses on beginnings and ends.

Document parsing is imperfect

Drop a .tiff scan into ChatGPT and it can only read the first page. Upload a complex PDF and you'll never be sure if everything was read correctly.

What you see isn't what it sees

ChatGPT doesn't show you what it read from the document. Tables, columns, footers, images, and even checkboxes often get misinterpreted or lost, and if you can't audit what was read, you can't trust the answers.

What canonicalising provides

Data is extracted into standard, LLM-ready formats (markdown, png, JSON)
Preserves hierarchy (document → section → paragraph → table → cell)
Outputs consistent schemas (e.g., JSON with stable keys)
Extract embedded data, such as images
Provides an audit trail of what processing occurred
Know in advance how the LLM is going to see the data

An LLM is a reasoning engine — it needs to reason over the right material.