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A useful document-AI system separates three jobs: detecting the document’s structure, grounding domain entities and relationships in that structure, and drawing conclusions for a particular workflow. Keeping those layers explicit makes it easier to tell whether a wrong answer began with perception, grounding, or task-specific inference.

What are the three layers of document AI?

The model described by Janos Tolgyesi in his DEV Community article distinguishes layers by the question each answers and by how widely its output can be reused. It is an architectural framework, not a universal document schema: the domain layer should reflect the document family and the task.

Layer Question Typical output Reuse profile
1. Intrinsic structure and perception What is physically on the page? Pages, text blocks, tables, reading order, sections, signatures, and page geometry Fully reusable across domains and workflows, in the model
2. Domain entities and relations, or grounding Which domain concepts do these elements represent, and how are they related? Parties, dates, amounts, authorities, and cross-references Partially reusable; varies by document family
3. Workflow-specific knowledge and inference What does this particular workflow need to conclude? A task-bound judgment, answer, or summary Not reusable across workflows by design

What belongs in each layer?

Layer 1: perceive the document

Layer 1 represents the document’s observable structure: where content appears, how blocks relate spatially, how a table is arranged, and what order the text follows. Its purpose is not to decide what a figure means for a business process. The same structural representation can support different downstream tasks.

Layer 2: ground domain concepts

Layer 2 connects the perceived content to a domain vocabulary. It can identify parties, amounts, dates, and references, and resolve relationships among them. A generic upper ontology may provide reusable concepts, with extensions for a particular domain. In a contract, grounding can include resolving a legal reference to a canonical identity or linking a defined term to the clause that defines it.

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Layer 3: infer for a specific workflow

Layer 3 answers the question that prompted the processing: whether a payment appears duplicated, whether a clause is enforceable, or how a filing should be summarized for a board. These are task-shaped conclusions. In this model, they remain attached to the workflow that produced them instead of becoming shared domain facts.

How does the model change across document types?

Layer 2 is not a single vocabulary that fits every document. Its contents and complexity depend on what the document family expresses and what the application must resolve.

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Document type Possible Layer 2 grounding What varies
Invoice Issuer, recipient, line items, amounts, tax, dates, and reference number A relatively recognizable set of business entities and fields
Contract Parties, legal references, and document-defined terms The stable vocabulary may be thinner; reference resolution and linking terms to definitions can take more work
Novel Characters, places, events, coreference, and chronology Relationships and continuity across the narrative matter more than invoice-like fields

These examples illustrate how to shape grounding; they do not imply that every domain needs the same ontology or that one universal schema describes all documents.

Why keep the layers explicit?

Tolgyesi’s directive is “Never skip a layer.” Sending a raw PDF or text dump straight to a language model for a workflow answer compresses perception, grounding, and inference into one opaque step. If a table cell is misread, its amount could be attached to the wrong party, leading the workflow to a mistaken conclusion. The layered design gives the team distinct stages to inspect and test: did perception capture the cell, did grounding attach it to the right entity, or did inference apply the wrong task logic?

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The article proposes separate golden datasets for testing the layers. That is a design recommendation, not evidence of a measured accuracy or production-performance improvement: it reports no benchmark or head-to-head result. Explicit stages can make failures easier to localize, but the article does not quantify that benefit.

Layering also does not mean an inference must ignore the source. A later stage can retrieve the exact clause or passage identified by earlier stages. That is different from bypassing structure and grounding and asking a model to derive everything from a raw document in one step.

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What should stay out of shared grounding?

Keep Layer 2 for stable, task-independent facts and relationships; keep interpretations that depend on the question in Layer 3. The distinction matters when two workflows use the same source but evaluate it differently.

For example, due-diligence and litigation-risk reviews might both start from a contract’s termination clause but interpret “surviving obligations” in ways shaped by their separate purposes. The shared layer can preserve the clause and its grounded entities. Each workflow should keep its own judgment rather than promoting one interpretation into common domain data.

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Why do stable references matter?

Upper layers depend on references to lower-layer content. If identifiers for extracted spans change each time a document is reprocessed—for example, after an OCR or model update—groundings and conclusions may no longer point to the intended text. Any layered design therefore needs a way to maintain or reconcile references across re-extraction. The article flags this dependency but does not specify a document object model that solves it.

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