Document AI architecture for enterprises: Hallucination mitigation, security, and auditability

Enterprise Document AI architectures are rapidly evolving beyond traditional OCR toward Vision Language Models (VLMs) and agentic AI systems that can interpret complex layouts, reason over multimodal inputs, and automate end-to-end document workflows. While these approaches unlock significantly higher accuracy and automation, they also introduce new architectural failure modes that are especially critical in enterprise and regulated environments.

In practice, three concerns dominate production deployments: hallucination mitigation, data security, and auditability. If a Document AI system cannot reliably ground outputs in source documents, enforce strong data isolation, and produce a verifiable chain of custody for every extraction, it fails core enterprise requirements—regardless of model quality. This article examines how modern Document AI architectures address these constraints through system-level design choices, not model selection alone.

1. What is hallucination & how to mitigate it in Document AI systems?

In document intelligence, a hallucination occurs when a model produces output that is syntactically correct and plausible, but not grounded in the source document.

Common manifestations include:

• fabricating values that are not visually present,

• inferring relationships that do not exist,

• producing structurally valid but incorrect tables or fields.

Hallucinations are especially common in documents with complex layouts, low-quality scans, mixed modalities, or ambiguous visual cues. As a result, mitigating hallucination risk is a prerequisite for deploying VLMs and agentic systems in real-world enterprise pipelines.

Hallucination mitigation cannot rely on a single technique, because failure modes differ across layouts, document quality, and extraction stages. Production systems typically combine prompt-level controls, architectural safeguards, and verification loops.

1.1 Expert prompt engineering 

Prompt design remains one of the most effective and lowest-latency methods to reduce hallucinations in VLM-based extraction.

Effective prompts typically:

• enforce strict output schemas (e.g., JSON with required fields),

• restrict outputs to visually verifiable information only,

• define format, range, and validation constraints explicitly.

In a documented TRM Labs deployment, expert prompt engineering improved usable extraction accuracy from ~80% to 98%, while achieving 100% structural consistency. While prompting alone is insufficient, it establishes a reliable baseline and significantly reduces downstream correction load.

1.2 Self-Correction and Verification loops

Single-pass extraction is fragile under layout variance and scan noise. Production systems therefore introduce multi-step verification and self-correction workflows.

These architectures allow the system to:

• detect broken or misaligned table structures,

• identify field–value mismatches,

• repair corrupted rows or columns,

• selectively re-run extraction on low-confidence regions.

A representative example is agentic OCR pipelines, such as Reducto’s Agentic OCR, which apply iterative correction loops to validate and refine outputs. Related approaches include self-reflective agents, where the system critiques its own reasoning and regenerates outputs when inconsistencies are detected.

While these mechanisms increase latency, they dramatically improve reliability and are essential for regulated environments.

1.3 Model-level architectural techniques

Some hallucination mitigation strategies operate directly at the inference architecture level, rather than through orchestration alone.

A notable example is Visual Contrastive Decoding (VCD). VCD compares model outputs generated from:

• the original document image, and

• a visually distorted version of the same image.

Predictions that are inconsistent across these visual conditions are suppressed, reducing object-level hallucinations and anchoring outputs more firmly in actual visual evidence. Techniques like VCD shift hallucination mitigation from heuristics toward model-aligned constraints.