Complete Setup Guide

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The full journey

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Step 1: Create your project

1. Go to the Projects section
2. Click Create Project
3. Enter a name (e.g., “Customer Analytics”)
4. Click Create

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Step 2: Add a connection

1. Click Add Connection in your new project
2. Choose Try Demo Data to explore with sample data, or connect your real database
3. For real databases, select your database type, enter credentials, and test the connection
4. Click Save

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Step 3: Create and configure your data model

1. Go to the Data Models tab
2. Click Create Data Model
3. Select your connection and enter a name
4. Click Create

1. Select tables — Uncheck tables that aren’t relevant to this agent’s use case
2. Select columns — Uncheck sensitive or irrelevant columns within each table
3. Add descriptions — Annotate tables and columns with plain-English context so the agent understands what the data means, not just what it’s named
4. Mark selectables — Flag categorical columns (region, status, owner) so the agent can enumerate valid filter values at query time
5. Click Save

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Configure data security

1. Click on a table in the data model
2. Go to the Data Security tab
3. Write a filter rule using {{organization.*}} or {{user.*}} placeholders:
   Copy

   Ask AI

   SELECT *
   FROM "orders"
   WHERE "company_id" = {{organization.company_id}}
   
4. Click Save

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Step 4: Set the data model to production

1. Open your data model and go to the Versions tab
2. Click Set as Production on the latest version
3. Wait for validation to pass

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Step 5: Create an agent or application

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Option A: Create an agent

1. Go to the Agents tab
2. Click Create Agent
3. Select your production data model
4. Click Create

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Option B: Create an application

1. Go to the Applications tab
2. Click Create Application
3. Select your production data model
4. Click Create

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Step 6: Encode your context

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For applications: build templates

1. Click Add Dashboard in your application
2. Name it and add charts by clicking Add Chart
3. Configure each chart with your data and arrange the layout
4. Click Save

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Step 7: Go to production

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For agents

1. Test the agent in admin view — check the observability trace on a few responses to confirm the SQL and logic are correct
2. Go to the Versions tab
3. Click Set as Production

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For applications

1. Preview each template as a test user to confirm RLS is working
2. Click Publish on each template — this propagates copies to all spaces

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Step 8: Add project organizations

1. Go to the Organizations tab
2. Click Add Organization
3. Enter the organization name (e.g., “Acme Corp”)
4. Add properties that your security rules reference:
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   Ask AI

   {
     "company_id": "acme-123"
   }
   
5. Click Create

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Step 9: Register end users

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Via the UI

1. Go to the Users tab
2. Click Add User
3. Enter the user name, select their organization and role
4. Add any user-level properties needed for security rules
5. Click Create

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Via API

curl -X POST https://api.upsolve.ai/api/v1/project/user/register \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "John Smith",
    "projectId": "YOUR_PROJECT_ID",
    "userRoleId": "READER_ROLE_ID",
    "projectOrganizationId": "ACME_ORG_ID",
    "properties": {
      "department": "sales"
    }
  }'

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Step 10: Generate user tokens

curl -X POST https://api.upsolve.ai/api/v1/project/user/token \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "userId": "USER_ID_FROM_REGISTRATION",
    "expiration": "24h"
  }'

{
  "token": "eyJhbGciOiJIUzI1NiIs..."
}

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You’re done!

• Chat with agents using natural language
• View dashboards with their filtered data
• Create their own charts and dashboards in their space

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Setup checklist

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Next steps