Getting Started Guide

Before we touch any configuration, it's important to align on a few key ideas that Operon.Cloud is built around.

Operon.Cloud has a few core building blocks you'll configure early on.

Once your subscription is active and you see the "Account created" confirmation:

1. Sign in to the Console using the email and password you used during onboarding.
2. If prompted, choose or confirm your first Workspace:
   • Give it a clear, business-relevant name (e.g. Payer – UM Pilot)
   • Optionally set the environment (e.g. Sandbox vs Production)
3. Confirm that the Pilot plan is active for this workspace.

You now have a container where you can safely configure identities, channels, and interactions.

Next, you'll create the digital identities that represent the actors in your initial workflow.

4.1 Identify the key participants

For your first Channel, list the main participants:

• Human roles – e.g. Nurse Reviewer, Physician Reviewer, Intake Specialist
• Systems – e.g. Care Management Platform, EHR, Integration Hub
• AI / Automation – e.g. PA Auto-Approval Model, LLM Triage Assistant, Routing Bot

4.2 Create DIDs in ID.Operon

In the Console, navigate to Identities or ID.Operon and:

1. Click New Digital Identity.
2. Choose the Type (Person, System, or AI Agent).
3. Provide key details, such as:
   • Display name (e.g. Nurse Reviewer, PA Auto-Approval Model)
   • Role or category
   • Owning team or department
4. (Optional) Associate the DID with your authentication system (e.g. email, OIDC client, service account ID) if you plan to sign events directly.
5. Save the DID.

Repeat for each participant you listed.

With your workspace and identities in place, you're ready to model the workflow you want to instrument.

5.1 Choose a pilot workflow

Pick one workflow where:

• AI is already in use or will be introduced soon
• You have a clear sense of current manual effort (time/cost per case)
• You have a small number of well-understood steps

Example: Prior Authorization – Auto Approval Pilot.

5.2 Create the Channel

In the Console, navigate to Channels and:

1. Click New Channel.
2. Enter a name and description, e.g.:
   • Name: Prior Authorization – Auto Approval Pilot
   • Description: Tracks PA cases where the auto-approval model triages and approves low-risk requests.
3. Set the case identifier strategy:
   • Choose the field that will carry your case ID in inbound events (e.g. caseId, paId).
4. Save the Channel.

5.3 Attach participants (DIDs) to the Channel

Within your new Channel:

1. Navigate to Participants.
2. Add the relevant DIDs you created earlier, for example:
   • Nurse Reviewer
   • Physician Reviewer
   • PA Auto-Approval Model
   • Care Management Platform
3. Assign roles/permissions (who can perform which interactions, if applicable).

This tells Operon who is allowed to act in this workflow.

Now you'll define the steps in your workflow and specify which ones have ROI impact.

6.1 List your core interactions

For the chosen Channel, map out the key steps, for example:

1. pa_request_received
2. ai_auto_approval_evaluated
3. ai_auto_approval_issued
4. nurse_manual_review_performed
5. final_decision_sent

6.2 Create Interactions

In the Channel's Interactions section:

For each interaction:

1. Click New Interaction.
2. Provide:
   • Key (machine-readable ID, e.g. ai_auto_approval_issued)
   • Display name (e.g. AI – Auto Approval Issued)
   • Description (what this step represents)
3. Define who can perform it by selecting allowed DIDs, for example:
   • ai_auto_approval_issued → allowed participant: PA Auto-Approval Model
   • nurse_manual_review_performed → allowed participant: Nurse Reviewer
4. Define the payload schema (at minimum):
   • caseId – string, used to tie events to a case
   • timestamp – ISO8601 time when the step occurred
   • Optional: durationMs, cost, and any business-specific fields
5. Mark interactions that represent AI ROI Events, e.g.:
   • ai_auto_approval_issued – this replaces a manual review and should be flagged as an ROI event.

Save each interaction.

6.3 Configure baseline time and cost

To compute ROI, Operon needs to know the baseline effort for key interactions or cases. In the ROI/Cost configuration (workspace or channel level):

1. Set baseline assumptions, such as:
   • Average manual review time per case (e.g. 30 minutes)
   • Average cost of a manual review (e.g. $50 per case)
2. Map those baselines to specific AI ROI Events:
   • ai_auto_approval_issued → baseline: 30 minutes & $50

Before going live, it's a good idea to send a few test events through the API or SDK.

7.1 Use the SDK or API

From your integration code (or a simple script):

1. Create a test case ID, e.g. CASE-12345.
2. Send a sequence of interactions for that case:
   • pa_request_received (performed by Care Management Platform DID)
   • ai_auto_approval_issued (performed by PA Auto-Approval Model DID)
   • final_decision_sent (performed by Care Management Platform DID)
3. Ensure each request includes the required fields:
   • caseId
   • interactionKey
   • performedByDid
   • timestamp

7.2 Verify in the Console

In the Console:

1. Open the ROI Impact Explorer for your Channel.
2. Filter by the time window where you sent test events.
3. Confirm you can see:
   • The AI Adoption Mix reflecting AI vs human vs hybrid cases
   • The Time & Cost Savings panel with baseline and estimated savings
   • Your test interactions showing up in Cost Efficiency Leaders and Time Efficiency Leaders (if configured as ROI events)

With your first workspace, identities, channel, and interactions configured, you've laid the foundation for measuring AI adoption and ROI.

From here, you can:

• Expand to additional Channels (e.g. Claims, Referrals, Care Management)
• Add more participants and interactions as new AI capabilities come online
• Configure Immutable Ledger Proofs for high-stakes workflows
• Integrate AI adoption and ROI dashboards into your internal reporting