Large language models make it look easy to “chat with your documents.” In demos, you upload a PDF, ask a question, and get a confident answer with citations.

In real enterprise environments, that confidence is exactly the problem.

This post defines the core research problem behind Zocuments Research: why LLM-based document systems fail in practice, and how we can measure and reduce those failures using repeatable evaluation—across heterogeneous documents (PDF, DOCX, OCR), strict permission boundaries, and workflow context.

What Makes Enterprise Document AI Hard

Enterprise documents are not just “more text.” They combine multiple sources of uncertainty that compound under automated reasoning:

  • Heterogeneous formats: PDFs, DOCX, spreadsheets, scans, and emails encode meaning differently. Tables, footnotes, pagination, and layout often matter.
  • OCR uncertainty: scanned documents introduce probabilistic errors before reasoning even starts. Most systems discard those uncertainty signals.
  • Versioning and supersession: policies change; contracts get amended; addenda override prior clauses. “What’s true” may depend on which version is in force.
  • Implicit context: documents reference internal roles, systems, and exceptions that aren’t self-contained.
  • Permission boundaries: not all users can see all documents. “Helpful” answers that cross access boundaries are failures, even if factually correct.

These conditions are common in enterprise settings—and they are exactly where naive “chat with docs” breaks down.

Failure Modes We See in Practice

Across document QA, retrieval, and extraction systems, the same failure patterns repeat:

1) Hallucinated answers

The model produces a plausible response that is unsupported by any source document.

2) Citation drift

A citation is provided, but the cited text does not actually justify the answer. Citations become decoration instead of evidence.

3) Partial grounding

Individual facts are correct, but the final conclusion is not logically warranted by the sources.

4) Overconfident non-refusal

The system answers despite insufficient information, when the correct behavior would be to refuse or ask for clarification.

5) Retrieval bias

Semantic similarity is treated as relevance. The system retrieves “related” text rather than the authoritative clause, definition, or controlling policy.

6) Permission leakage

Answers are influenced by documents the user should not be allowed to access, or the system reveals restricted information implicitly.

The dangerous part: many of these failures are invisible to end users because the output is fluent and confident.

Why RAG Alone Doesn’t Solve This

Most document AI systems rely on variations of retrieval-augmented generation (RAG). RAG improves recall, but it does not guarantee truthfulness.

Common failure reasons:

  • Embeddings optimize for similarity, not authority
  • More context can increase hallucination, not reduce it
  • Citations are often attached after generation, not enforced during reasoning
  • Refusal is poorly calibrated and inconsistent
  • Permissions are implemented as filters, not as first-class reasoning constraints

In other words, many systems optimize for producing an answer—not for producing a verifiable answer.

The Research Problem

Zocuments exists to research and develop reliable, citation-grounded LLM systems for enterprise documents and workflows.

Our core research problem:

Can large language models be adapted to reliably extract, retrieve, and reason over private enterprise documents while preserving traceability, permission boundaries, and workflow context?

Our working research thesis:

Workflow-aware constraints and structured metadata may improve factuality and trustworthiness of LLM-based document reasoning.

Core Research Questions

We’re investigating applied, testable questions:

  • How do hallucination and citation error rates vary by document type (PDF vs DOCX vs OCR)?
  • When does structured extraction outperform semantic retrieval?
  • Can workflow context constrain reasoning in a measurable way?
  • Can correct refusal behavior be reliably induced and evaluated?
  • What are the accuracy vs latency vs cost tradeoffs across architectures?
  • How do permission constraints change reasoning outcomes?

This is engineering-driven research: measurable failure modes, controlled experiments, and repeatable evaluation.

Experimental Approach

We will compare multiple architecture variants under a shared evaluation framework, such as:

  • Baseline RAG
  • RAG + structured extraction
  • RAG + permission-aware retrieval
  • Workflow-constrained reasoning

Rather than optimizing for “best demo,” the goal is to understand where each approach fails, and what constraints reduce failures.

What We Intend to Produce

We aim to produce a public, reusable evaluation foundation:

  • A repeatable test suite
    • Document sets
    • Question sets
    • Expected behaviors (answer, refuse, cite)
  • Metrics and evaluation code
    • Hallucination rate
    • Citation accuracy
    • Correct refusal rate
    • Permission compliance checks
    • Latency / cost measurements
  • Comparison reports
    • Architecture-level results
    • Failure-mode analysis
    • Design implications and recommendations

The long-term goal is to help drive a practical standard for reliability in document intelligence systems—grounded in real-world constraints, not just benchmark performance.

Why This Matters

Incorrect answers in document-centric systems can affect:

  • compliance and audits
  • legal exposure
  • financial decisions
  • operational safety
  • organizational trust in AI

If we want LLM systems to be used responsibly inside organizations, we need more than “it usually works.” We need measurable trustworthiness.

Conclusion

LLM-based document intelligence will continue to improve—but reliability cannot be assumed. It must be designed, constrained, and measured.

Zocuments Research is focused on making these systems safer and more dependable by studying their failures directly and building repeatable evaluation harnesses that reflect enterprise reality.

If you’re building document AI in production, these failures aren’t hypothetical. They are the baseline—and they’re solvable, but only if we measure them honestly.