Business and customer data for language operators
Language operators can use external data to better understand your customers and your business, enabling more personalized and accurate conversation analysis.
When you connect your intelligence configuration to these data sources, language operators gain awareness of:
- Who the customer is, based on prior interactions and known traits
- What business-specific information should guide how the conversation is interpreted
Using external data improves result quality beyond what the conversation text alone provides.
Conversation Memory and Enterprise Knowledge required
To enable business and customer awareness for Conversation Intelligence, you must activate Conversation Memory and Enterprise Knowledge for your Twilio account.
Language operators can draw from different types of external data to enrich conversation analysis. These data sources help language operators understand both the customer and the business beyond what the conversation text alone provides.
Conversation Memory and Enterprise Knowledge are the two categories of external data available to language operators. Language operators can access this data in two ways:
- Parameters (deterministic): Explicit inputs selected by you and resolved before the language operator prompt is sent to the AI model
- Context (dynamic): Dynamic inputs retrieved by the AI model at runtime based on the live conversation
The table below summarizes the available data sources and how each is delivered to language operators.
| Data source | What it provides | How it's used by language operators | How data is provided | Examples |
|---|---|---|---|---|
| Conversation Memory | Customer-specific traits and observations from past conversations | Enables awareness of who the customer is and prior interactions | N/A |
|
| Enterprise Knowledge | Enterprise-specific information, policies, and procedures | Grounds analysis in business rules and factual information | Parameters or context | FAQs, agent scripts, product documentation, support articles, internal policy guides |
A language operator doesn't directly embed Conversation Memory or Enterprise Knowledge. Instead, the operator defines a schema for the external data it expects, and the intelligence configuration controls how to supply that data at execution time.
Decide how to deliver external data to language operators based on your use cases and requirements.
Parameters are resolved outside the AI model, producing predictable and repeatable language operator behavior.
Use parameters when:
- You know the data in advance
- You explicitly select the data
- You need consistent, auditable behavior
Language operators declare which context types they can access. The AI model determines what information to retrieve and when to retrieve it during execution.
Use Conversation Memory or Enterprise Knowledge data as context when:
- You don't know the data in advance
- The data depends on the live conversation state
- You want the AI model to select data dynamically at runtime
For a custom language operator, you define the operator schema yourself. This includes:
- Declaring parameters and their expected types
- Enabling context capabilities and specifying how external data may be accessed
This gives you full control over how Conversation Memory and Enterprise Knowledge are incorporated into the language operator's logic, so you can design operators tailored to your specific use cases.
| Data type | As parameters | As context |
|---|---|---|
| Conversation Memory | ❌ | ✅ |
| Enterprise Knowledge | ✅ (for example, substitute agent scripts or other knowledge base content into the model prompt) | ✅ (available for retrieval) |
- Learn how to create a custom language operator that uses Conversation Memory and Enterprise Knowledge.
- Learn how to create intelligence configurations and define rules.