How Is Omni Actually Used? A Guide to the AI Analysis Workflow for First-Time Users

Services Apr 23, 2026 · ARGOS Identity, Suyeon Yang

​

Why Does Omni Start with Workflow Rather Than Features?

When companies begin exploring AI adoption, the first thing they usually focus on is functionality.
Questions often start with whether the system can read documents, detect risks, or deliver results quickly. However, in real operational environments, what matters more than individual features is how those features are connected within a workflow. Even if an OCR engine is available, operations do not become truly efficient unless there is a clear structure for where data is uploaded, how decisions are made, and how results are reviewed. Omni was designed to solve exactly this issue.
It allows users to define the purpose of analysis from the beginning, choose the necessary engines, and design the output structure as part of one integrated process. Today, we will walk through the basic sequence a first-time user follows when starting analysis in Omni.

​

Analysis Begins with Creating a Workflow

When opening Omni for the first time, the first step is creating a new workflow. What matters here is that this is not simply creating a project it is defining what the analysis is intended to achieve. For example, if the goal is to review corporate documents, users must first determine which documents will be analyzed and under what criteria they will be evaluated. Entering the workflow name and description may look like a simple starting screen, but in practice it defines the entire analysis structure that follows. Omni is designed so that each analysis unit can be clearly organized from this first stage.

​

Select Only the Engines You Need and Connect Them into One Flow

After creating a workflow, the next step is selecting the analysis engines required for the task. Different engines such as document analysis, AML screening, and text verification can be combined within a single workflow depending on operational needs. The advantage of this structure is that users do not need to activate every function at once.
Instead, only the engines relevant to the current objective are selected. For example, corporate review may require both document extraction and risk screening, while other cases may only need text verification. In this sense, Omni is not about listing features it is about designing the right analysis structure based on operational goals.

​

Define the Output Structure Before Running Analysis

One of Omni’s key strengths is that users can define how results should be structured before analysis begins. Using JSON Schema, users can specify which fields to extract, what format results should follow, and how outputs should be organized. This matters because strong AI analysis alone is not enough to make operations practical. Operational teams still need to review results, pass them into other systems, or trigger follow-up actions based on internal criteria. That is why consistent output structure is critical. Omni allows this output design to be built directly into the workflow.

​

Profiles and Folders Organize the Actual Analysis Targets

Once the analysis structure is set, users register the actual targets for review. A profile represents one analysis entity, while folders organize documents and data related to that entity. For example, in a corporate review case, one company becomes a profile, and business licenses, corporate documents, and supporting materials are stored within folders. In addition to file uploads, text input is also supported, allowing multiple data types to be managed together. After data is uploaded, analysis is executed. From the user’s perspective, this is a single click. Internally, however, all selected engines run in parallel and perform their respective analysis tasks. Document extraction, validation, and risk screening all happen within one connected flow. This allows operators to avoid switching between multiple systems just to review separate outputs.

​

Results Are Immediately Available as Structured Data and Reports

Once analysis is completed, results appear in Analysis Results and Report formats. Rather than simply showing raw AI responses, Omni presents organized outputs with status-based information and decision-support details. This becomes especially valuable in repetitive review tasks or operations requiring consistent comparison against defined criteria. It reduces reading time and allows teams to continue reviews under the same standard.

​

Omni Is Not a Tool for Adding AI Features : It Is a Platform for Designing Analysis Operations

Many AI tools are introduced through feature lists.
But in real operations, workflow matters more than features themselves. Omni is built so that policy definition, execution, and result review can all be designed within one connected structure. Even first-time users can begin analysis immediately by following a step-by-step workflow, while operational teams can continuously adjust the flow according to changing needs. In many cases, slow operations after AI adoption are caused not by a lack of features, but by the absence of structure. Omni is a platform designed to change that structure itself. Would you like to explore more features of Omni? Request a consultation today.