Provider Settings

The Provider tab controls which AI model your assistant uses and how responses are formatted. These settings directly impact assistant performance, cost, and capabilities.

Default Model: Claude Haiku 4.5

All assistants use Claude Haiku 4.5 by default. It’s included in every QuivaWorks plan — no API key or additional setup required. Your plan’s credits are consumed per interaction. Claude Haiku 4.5 is fast, cost-effective, and excellent for the vast majority of business workflows: customer service, research, content generation, data extraction, and analysis. It’s the right choice for most assistants.

Start with the default. Only consider switching models if you have a specific reason — like requiring a very large context window or needing a particular model’s behaviour.

Bring Your Own Keys

On Team and Enterprise plans, you can connect your own Anthropic API key to use specific Claude models directly. When using your own keys:

You’re billed directly by the provider, not through QuivaWorks credits
You have full control over which model version to use
You can access models that aren’t available on the default plan

Bring Your Own Keys is available on Team and Enterprise plans only. See plans →

Available Models

Anthropic’s Claude models — excellent instruction-following, long context, and strong reasoning. Best for:

Long document processing (200K+ token context)
Complex, nuanced instructions
High-accuracy analysis and research
Safety-critical applications

Current models:

claude-haiku-4-5 — Fast and cost-effective (same as default)
claude-sonnet-4-5 — Balanced performance and capability
claude-opus-4-5 — Most capable, highest cost

Get your Anthropic API key at console.anthropic.com

Setting Up Your API Key

Once you have an API key from a provider, add it to your assistant:

Open Provider Settings

Open your assistant configuration and click the Provider tab.

Select Provider and Model

Choose your provider from the dropdown, then select the specific model version.

Enter Your API Key

Paste your API key into the API Key field. The key is encrypted and securely stored. It will be masked immediately after entry.

Save and Test

Click Save, then open the assistant in chat and send a test message to verify the model responds correctly.

Getting an Anthropic API Key

Create an Account

Visit console.anthropic.com and sign up. New accounts receive free credits to get started.

Set Up Billing

In the Anthropic Console, go to Billing and add a payment method. Set a monthly budget to avoid unexpected charges.

Set a monthly budget limit immediately. Claude API costs can add up quickly with high-volume usage or large context windows.

Create Your API Key

Navigate to API Keys in the left sidebar, or go to console.anthropic.com/settings/keys
Click Create Key and give it a descriptive name (e.g., “QuivaWorks Production”)
Copy the key immediately — it starts with sk-ant- and cannot be retrieved after you close the dialog
Store it securely in a password manager

Never share your API key or commit it to a code repository. If it’s exposed, delete it in the Anthropic Console and create a new one immediately.

API Key Security:

Never share API keys or commit them to code repositories
Use separate keys for development and production
Set spending limits on each provider platform
Revoke and rotate keys if you suspect any exposure

Output Schema

Define the exact structure your assistant must return. When configured, the assistant validates its output against the schema and retries automatically if it doesn’t match.

When to Use Output Schemas

Use a Schema
Skip a Schema

When you need:

Structured data extraction from documents or conversations
Consistent field names and types for downstream processing
Integration with other systems or flow steps
Automated validation of assistant responses

Example use cases:

Lead qualification (return structured lead data)
Invoice processing (extract specific fields)
Customer service triage (categorise and route issues)
API responses (return JSON for webhooks)

Defining an Output Schema

Use natural language JSON Schema format — describe fields and their types in the description: Simple example:

{
  "lead_name": "The full name of the lead (string)",
  "lead_email": "Email address of the lead (string)",
  "company": "Company name (string)",
  "qualified": "Whether the lead matches our ICP (boolean)",
  "score": "Lead qualification score from 0-100 (number)"
}

Complex example with nested fields:

{
  "customer_name": "Full name of the customer (string or null)",
  "issue_category": "Category - one of: billing, shipping, product, technical, other (string)",
  "issue_summary": "One-sentence summary of the issue (string)",
  "sentiment": "Customer sentiment - one of: positive, neutral, negative, angry (string)",
  "requires_escalation": "Whether this needs human escalation (boolean)",
  "suggested_actions": "List of suggested actions (array of strings)",
  "next_steps": {
    "action": "What to do next (string)",
    "assigned_to": "Who to assign to - one of: bot, support_team, billing_team (string)",
    "priority": "Priority - one of: low, medium, high, urgent (string)"
  }
}

Schema Field Guide

Field Types

Specify the type in the description string:

{
  "name": "Full name (string)",
  "age": "Age in years (number)",
  "active": "Whether active (boolean)",
  "tags": "List of tags (array of strings)",
  "address": {
    "city": "City name (string)",
    "country": "Country code (string)"
  }
}

Optional Fields

Use “or null” to mark fields as optional:

{
  "required_field": "Always present (string)",
  "optional_field": "May be null if not found (string or null)",
  "order_number": "Order number if mentioned, null otherwise (string or null)"
}

Enum Values

Specify allowed values using “one of”:

{
  "status": "Current status - one of: pending, approved, rejected (string)",
  "priority": "Priority level - one of: low, medium, high (string)"
}

Arrays of Objects

For arrays of structured objects:

{
  "line_items": [
    {
      "description": "Item description (string)",
      "quantity": "Quantity (number)",
      "price": "Price per unit (number)"
    }
  ]
}

Schema Examples

Lead Qualification

{
  "lead_name": "Full name of the lead (string)",
  "company": "Company name (string)",
  "email": "Email address (string)",
  "company_size": "Estimated size - one of: 1-10, 11-50, 51-200, 201-500, 500+ (string)",
  "use_case": "What they want to use our product for (string)",
  "timeline": "Buying timeline - one of: immediate, 1-3 months, 3-6 months, exploring (string)",
  "qualified": "Whether lead matches our ICP (boolean)",
  "qualification_score": "Score from 0-100 (number)",
  "next_action": "Recommended action - one of: book_demo, send_info, nurture, disqualify (string)"
}

Invoice Processing

{
  "invoice_number": "Invoice number from document (string)",
  "invoice_date": "Date in YYYY-MM-DD format (string)",
  "due_date": "Payment due date in YYYY-MM-DD format (string)",
  "vendor_name": "Name of the vendor (string)",
  "total_amount": "Total invoice amount (number)",
  "currency": "Currency code e.g. USD, EUR (string)",
  "line_items": [
    {
      "description": "Item description (string)",
      "quantity": "Quantity (number)",
      "unit_price": "Price per unit (number)",
      "total": "Line item total (number)"
    }
  ],
  "tax_amount": "Tax amount if shown (number or null)",
  "validation_status": "Validation result - one of: valid, invalid, needs_review (string)"
}

Customer Service Triage

{
  "customer_name": "Customer name (string or null)",
  "issue_category": "Category - one of: order, shipping, return, billing, technical, other (string)",
  "issue_summary": "One-sentence summary (string)",
  "sentiment": "Customer sentiment - one of: happy, neutral, frustrated, angry (string)",
  "urgency": "Urgency - one of: low, medium, high, critical (string)",
  "can_automate": "Whether this can be handled automatically (boolean)",
  "requires_human": "Whether a human agent is needed (boolean)",
  "assign_to": "Team to assign - one of: bot, tier1, tier2, billing, technical (string)"
}

Validation and Auto-Correction

When an output schema is defined:

The assistant generates a response
QuivaWorks validates it against the schema
If invalid: the assistant retries automatically with correction instructions
If valid: the response is returned
After 3 failed attempts: the step is marked as an error

Validation and auto-correction happen automatically. No additional logic is needed in your flow.

Start simple. Add more fields as you discover what data you actually need — it’s easier to expand a schema than to debug an overly complex one.

Best Practices

Start with the default model

Claude Haiku 4.5 handles most business workflows extremely well and uses your included plan credits. Start here and only switch if you have a specific reason — like needing a 200K token context window or a particular model’s behaviour on complex tasks.

Use output schemas for structured data

Always use a schema when extracting specific fields, passing data between flow steps, or integrating with other systems. Skip schemas for natural language responses and conversational outputs.

Write descriptive schema field names

Don’t just name fields — guide the assistant on what to extract:❌ Vague:

{ "date": "Date (string)", "status": "Status (string)" }

✅ Descriptive:

{
  "order_date": "The date the order was placed in YYYY-MM-DD format (string)",
  "status": "Current order status - one of: pending, shipped, delivered, cancelled (string)"
}

Secure your API keys

Never commit keys to code repositories
Use separate keys for development and production environments
Set spending limits on provider platforms
Monitor usage regularly for unexpected spikes
Revoke and rotate keys if you suspect any exposure

Troubleshooting

API key not working

Verify the full key was copied (no extra spaces)
Confirm the key is active on the provider’s platform
Ensure billing is set up on the provider account
Check that you haven’t exceeded rate limits or quota
Try generating a new key if issues persist

Output doesn't match schema

Simplify the schema — start with fewer fields
Add clearer, more specific field descriptions
Make optional fields nullable (string or null)
Ensure the input data actually contains the information you’re asking for
Check that assistant instructions don’t conflict with the schema

Inconsistent responses

Make instructions more specific
Add an output schema to enforce structure
Include examples in the instructions
Provide more context in the prompt

Next Steps

Context Settings

Configure memory, token limits, and reasoning depth

Knowledge

Add documentation and context sources

Tools & Connectors

Connect your systems via MCP

Best Practices

Optimise assistant performance and cost

Get Started

Assistants

Flows

Account Management

Advanced

Default Model: Claude Haiku 4.5

Bring Your Own Keys

Available Models

Setting Up Your API Key

Getting an Anthropic API Key

Output Schema

When to Use Output Schemas

Defining an Output Schema

Schema Field Guide

Schema Examples

Validation and Auto-Correction

Best Practices

Troubleshooting

Next Steps

Context Settings

Knowledge

Tools & Connectors

Best Practices

Get Started

Assistants

Flows

Account Management

Advanced

Documentation Index

​Default Model: Claude Haiku 4.5

​Bring Your Own Keys

​Available Models

​Setting Up Your API Key

​Getting an Anthropic API Key

​Output Schema

​When to Use Output Schemas

​Defining an Output Schema

​Schema Field Guide

​Schema Examples

​Validation and Auto-Correction

​Best Practices

​Troubleshooting

​Next Steps

Context Settings

Knowledge

Tools & Connectors

Best Practices

Default Model: Claude Haiku 4.5

Bring Your Own Keys

Available Models

Setting Up Your API Key

Getting an Anthropic API Key

Output Schema

When to Use Output Schemas

Defining an Output Schema

Schema Field Guide

Schema Examples

Validation and Auto-Correction

Best Practices

Troubleshooting

Next Steps