What DEEP Ops is: a complete guide to the platform that manages document AI change, verification and operation

DEEP Ops is Korea Deep Learning's document AI operations platform, designed so that extraction criteria can be changed as the work changes, performance can be verified before and after each change, and versions and operating history can be managed.

Document AI gets difficult again in operation, not at build

Document automation works best when it is first built.

The system was made to fit the document forms, extraction fields and business criteria in use at the time. The real working environment, however, does not stay still.

A change in insurance review criteria may require a new field to be checked on existing medical certificates. A financial institution may revise its application form, or a public body's legislation and notices may change, and the extraction criteria change with them.

In the conventional approach, even a small change like this repeats a cycle: send a revision request to the vendor, prepare the data, modify the model or system, verify again and deploy.

Applying the change takes longer than the change itself.

DEEP Ops focuses on treating document AI not as a system built once and finished, but as something to be managed continuously as the work changes.

What DEEP Ops actually is: what it operates

It manages the criteria for change and the verification process

The core of DEEP Ops is not simply deploying an AI model.

In real document work, what matters is which fields to extract, which conditions to apply, whether changed criteria produce better results than before, and whether a change applied to operations can be reversed.

In DEEP Ops, new fields or conditions to extract can be defined in natural language, and the changed result confirmed on real documents.

The criteria in operation and newly created criteria can also be applied to the same documents and compared. Field-level extraction results, accuracy and processing latency are examined together to confirm whether the change was a real improvement.

Changes are managed by version. What changed and when is recorded, differences from the previous version can be checked, and the previous version can be restored where needed.

In short, DEEP Ops is a platform that manages the change, evaluation, approval, operation and history of document AI as a single operating flow.

The four functions that make up DEEP Ops

First, changing extraction criteria in natural language

A business owner can define in natural language the fields and conditions newly required from a document.

If only claim amount and contract number were extracted before, and a new business rule requires a particular date or review item as well, new extraction criteria can be defined and the result confirmed.

Not every change requires retraining. Where a change can be handled by adjusting extraction criteria and schema rather than model parameters, it can be applied without a separate training project.

Second, performance comparison on the same documents

Applying a change to operations as soon as it is made is risky.

DEEP Ops applies the existing version and the new version to the same evaluation documents so the results can be compared.

Beyond the aggregate result, extraction results can be checked by document and by field, and differences in accuracy, processing outcome and latency compared, to judge whether the new criteria are genuinely better.

Before and after are then confirmed by evaluation results under identical conditions rather than by intuition.

Third, version and change history management

In operating document AI, knowing what changed in the past matters as much as knowing what is applied now.

DEEP Ops records changes to extraction fields and conditions by version.

A new version can be compared with the previous one, and if a problem appears after deployment the previous state can be restored.

Keeping a record of which criteria changed, when and why, even as the people in charge change, also matters for operations.

Fourth, composing document work as a workflow

Document work does not end with extracting information.

A document is received and classified, the necessary information extracted, values reconciled or calculated, and the next step handled differently according to conditions.

In DEEP Ops, the stages required, from document input and OCR through classification, extraction, value reconciliation and calculation, conditional branching, and result judgment and generation, can be connected into a flow for each process.

The scope therefore extends beyond managing individual models to managing how document AI behaves inside the actual work.

How DEEP Ops is applied in practice

When business criteria change, start with the extraction criteria

Suppose fields A, B and C were being checked in a document, and a new regulation requires field D to be added.

First, define the new extraction field and its conditions.

Record clearly which field is extracted on what basis. Managing the value format and exception conditions as well, rather than only adding a field name, makes the change history easier to understand later.

Compare against the existing version on the same documents

Changed criteria are compared with the existing version on the same evaluation documents.

Confirm that the new field is extracted correctly, and that performance on fields previously extracted well has not fallen.

If only the changed field improved while errors rose elsewhere, the change is hard to apply to operations as it stands.

Only comparing the existing and new versions under the same conditions shows the effect of a change accurately.

Apply only verified versions to operations

When evaluation results meet the standard, the new version is applied to operations.

In environments such as finance and the public sector, where internal control over change matters, the reviewer and approver can be separated and the version applied to operations, and when, recorded.

If a problem occurs, it has to be possible to return to the previous version.

A change to document AI then becomes a manageable operating process rather than a one-off edit.

Manage the work flow as well

Extracting values well from a document does not by itself complete the automation of the work.

Processing an application form, for example, may require confirming the document type, extracting the necessary fields, then comparing values with another document or routing to an approval or review step according to conditions.

In DEEP Ops these downstream stages can be composed as a work flow, so that the result of document AI carries through to the actual handling of the work.

DEEP Ops and MLOps

MLOps is the methodology that systematises training, deployment, version management and performance monitoring across machine learning models.

At the centre of what it manages are training data and models, experiment results and model versions.

DEEP Ops focuses more specifically on the real operating process of document AI.

In document work, extraction fields, schema, natural language instructions, evaluation documents, business criteria and workflows bear directly on the operating result, not model weights alone.

DEEP Ops is therefore less a replacement for general MLOps than a platform for the operating territory document AI needs in order to respond to real changes in the work.

DEEP Ops and VLMOps

VLMOps refers to the concept and operating framework for running vision language models continuously in a real working environment.

Its core is managing extraction criteria and schema, evaluation criteria, versions and change history so that document AI keeps working as the work changes.

DEEP Ops is Korea Deep Learning's operations platform that implements this document AI operating concept so it can be carried out in real work.

Put simply, VLMOps is the concept that describes the operating approach and framework, and DEEP Ops is the product for actually performing that operation.

DEEP Agent and DEEP Ops

DEEP Agent and DEEP Ops have different roles.

DEEP Agent focuses on reading and classifying real documents, extracting and validating the necessary information, and handling the work.

DEEP Ops covers the operating side: changing the document AI thus built as the work changes, verifying the performance of new criteria, and managing versions and change history.

If DEEP Agent is the AI Worker that performs document work, DEEP Ops can be understood as the operating environment that keeps that Worker doing its job properly as the work changes.

DEEP Ops in the Korean finance and public sector environment

In finance and the public sector, document forms and business criteria are not fixed.

Legislation or notices are revised, internal review criteria change, and new application forms or certificates are added.

Some changes arrive at a scheduled time, and others arise when unforeseen documents come in during operation.

Handling every change as another development project opens a gap between the pace of business change and the pace at which the AI system can respond.

In document AI operations, how quickly criteria can be revised, verified and returned to operation when the work changes is therefore an important standard alongside the accuracy achieved at first build.

Frequently asked questions