# Source: https://docs.asapp.com/ai-productivity/ai-transcribe/product-guide.md

# Source: https://docs.asapp.com/ai-productivity/ai-compose/product-guide.md

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.asapp.com/llms.txt
> Use this file to discover all available pages before exploring further.

# AI Compose Product Guide

> Learn more about the features and insights of AI Compose

## Getting Started

This page provides an overview of the features and functionalities in AI Compose.

After you integrate AI Compose into your applications, you can use its features to scale up your agent responses.

<Note>
  The following UI descriptions are examples of AI Compose Integrations with LivePerson and Salesforce.
  API-based integrations do not include custom UIs.
</Note>

### Suggestions

AI Compose supports agents throughout the conversation with both complete response suggestions before they type and suggestions while typing to complete their sentence.

The machine learning models powering AI Compose suggestions use the entire conversation context (not just the last few responses) and personal agent response history to predict the most likely next agent message or phrase in the conversation.

<Frame>
  <img src="https://mintcdn.com/asapp/aK0YOQVZSULmKYJl/image/uuid-646b67c6-650e-2baf-b18b-31cb81ba966a.png?fit=max&auto=format&n=aK0YOQVZSULmKYJl&q=85&s=a7a3650f6b8dfa6128903008834d6ffb" data-og-width="1072" width="1072" data-og-height="749" height="749" data-path="image/uuid-646b67c6-650e-2baf-b18b-31cb81ba966a.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/asapp/aK0YOQVZSULmKYJl/image/uuid-646b67c6-650e-2baf-b18b-31cb81ba966a.png?w=280&fit=max&auto=format&n=aK0YOQVZSULmKYJl&q=85&s=93b98836fd20cafa1a97358bbc3851cb 280w, https://mintcdn.com/asapp/aK0YOQVZSULmKYJl/image/uuid-646b67c6-650e-2baf-b18b-31cb81ba966a.png?w=560&fit=max&auto=format&n=aK0YOQVZSULmKYJl&q=85&s=846c24275080e9bbfab76ea70a43058b 560w, https://mintcdn.com/asapp/aK0YOQVZSULmKYJl/image/uuid-646b67c6-650e-2baf-b18b-31cb81ba966a.png?w=840&fit=max&auto=format&n=aK0YOQVZSULmKYJl&q=85&s=d9f1152b9ca76bbbfe6235bce5afc317 840w, https://mintcdn.com/asapp/aK0YOQVZSULmKYJl/image/uuid-646b67c6-650e-2baf-b18b-31cb81ba966a.png?w=1100&fit=max&auto=format&n=aK0YOQVZSULmKYJl&q=85&s=7fd9dac6431bdd5705993e11541bd407 1100w, https://mintcdn.com/asapp/aK0YOQVZSULmKYJl/image/uuid-646b67c6-650e-2baf-b18b-31cb81ba966a.png?w=1650&fit=max&auto=format&n=aK0YOQVZSULmKYJl&q=85&s=772870e7346f00871b1f0223c084b8e1 1650w, https://mintcdn.com/asapp/aK0YOQVZSULmKYJl/image/uuid-646b67c6-650e-2baf-b18b-31cb81ba966a.png?w=2500&fit=max&auto=format&n=aK0YOQVZSULmKYJl&q=85&s=8294a683ecd29b4797c9e3abeaf0348c 2500w" />
</Frame>

### Response Library

AI Compose suggests responses from a library curated from a wide range of domain-specific conversation topics. The response library combines three lists:

<Frame>
  <img src="https://mintcdn.com/asapp/BoXlOITRW7VjgmOG/image/uuid-2ee5ac6b-e459-ad20-3eea-939ce44e089a.png?fit=max&auto=format&n=BoXlOITRW7VjgmOG&q=85&s=90c7e210b5dd27c3ea88d82cc5234fa3" data-og-width="1674" width="1674" data-og-height="739" height="739" data-path="image/uuid-2ee5ac6b-e459-ad20-3eea-939ce44e089a.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/asapp/BoXlOITRW7VjgmOG/image/uuid-2ee5ac6b-e459-ad20-3eea-939ce44e089a.png?w=280&fit=max&auto=format&n=BoXlOITRW7VjgmOG&q=85&s=bc30483c96df2fe5d75af28810e10870 280w, https://mintcdn.com/asapp/BoXlOITRW7VjgmOG/image/uuid-2ee5ac6b-e459-ad20-3eea-939ce44e089a.png?w=560&fit=max&auto=format&n=BoXlOITRW7VjgmOG&q=85&s=c83ddd0102a21478ae4b3193d2b1ff9f 560w, https://mintcdn.com/asapp/BoXlOITRW7VjgmOG/image/uuid-2ee5ac6b-e459-ad20-3eea-939ce44e089a.png?w=840&fit=max&auto=format&n=BoXlOITRW7VjgmOG&q=85&s=eae455fbce583099d93328d9500fbdd6 840w, https://mintcdn.com/asapp/BoXlOITRW7VjgmOG/image/uuid-2ee5ac6b-e459-ad20-3eea-939ce44e089a.png?w=1100&fit=max&auto=format&n=BoXlOITRW7VjgmOG&q=85&s=45926e21408b05c09a28efe63d0867b4 1100w, https://mintcdn.com/asapp/BoXlOITRW7VjgmOG/image/uuid-2ee5ac6b-e459-ad20-3eea-939ce44e089a.png?w=1650&fit=max&auto=format&n=BoXlOITRW7VjgmOG&q=85&s=c1ab56cabffd36db1dc4f36f0ac36d40 1650w, https://mintcdn.com/asapp/BoXlOITRW7VjgmOG/image/uuid-2ee5ac6b-e459-ad20-3eea-939ce44e089a.png?w=2500&fit=max&auto=format&n=BoXlOITRW7VjgmOG&q=85&s=59a5be7292c84d21262dbadcddd41db5 2500w" />
</Frame>

1. **Global response list:** Messages created and maintained by program administrators available to a designated full agent population.
2. **Custom response list:** Messages created and maintained directly in AI Compose by individual agents; only available to the agent that created the message.
3. **Organically growing response list:** Messages that ASAPP automatically creates for each agent based on their most commonly used messages that do not already exist in the global response list or the agent's curated custom response list.

<Tip>
  Agents use custom responses to make their favorite messages readily available for sending quickly: well-honed explanations for difficult processes and concepts, discovery questions, personal anecdotes, and greetings and farewells infused with their personal style.  Agents often curate their custom responses based on global responses, with a bit of their own personal touch.
</Tip>

### Agent Interface

AI Compose provides three complete response suggestions in the drawer above the composer both before typing begins and in the early stages of message composition; the system makes phrase completion suggestions directly in-line as the agent types more of a sentence.

Agents can also search for a response in two places:

1. **Composer:** As agents type, they can choose to search for their typed text in the global response list to see the full list of related messages with that term.
   By typing `/` in the empty composer, agents can also browse their custom response library by using either the message text or title of the custom response as a search term.
   <Frame>
     <img src="https://mintcdn.com/asapp/oWc-pd36yCvpD40u/image/uuid-fa73a5df-bb23-2a08-2b79-beff50effdc8.png?fit=max&auto=format&n=oWc-pd36yCvpD40u&q=85&s=1a05cd4e2e59536cba125b285dae538f" data-og-width="1085" width="1085" data-og-height="920" height="920" data-path="image/uuid-fa73a5df-bb23-2a08-2b79-beff50effdc8.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/asapp/oWc-pd36yCvpD40u/image/uuid-fa73a5df-bb23-2a08-2b79-beff50effdc8.png?w=280&fit=max&auto=format&n=oWc-pd36yCvpD40u&q=85&s=f5f58fb63398fa40a87e7a8fcf99e697 280w, https://mintcdn.com/asapp/oWc-pd36yCvpD40u/image/uuid-fa73a5df-bb23-2a08-2b79-beff50effdc8.png?w=560&fit=max&auto=format&n=oWc-pd36yCvpD40u&q=85&s=19aa0243a4f3192b112a317c16ac3cab 560w, https://mintcdn.com/asapp/oWc-pd36yCvpD40u/image/uuid-fa73a5df-bb23-2a08-2b79-beff50effdc8.png?w=840&fit=max&auto=format&n=oWc-pd36yCvpD40u&q=85&s=dfbad0691e93787c5a22fef46abc02d6 840w, https://mintcdn.com/asapp/oWc-pd36yCvpD40u/image/uuid-fa73a5df-bb23-2a08-2b79-beff50effdc8.png?w=1100&fit=max&auto=format&n=oWc-pd36yCvpD40u&q=85&s=b954fe11b47dd8cbefb081e7f348998a 1100w, https://mintcdn.com/asapp/oWc-pd36yCvpD40u/image/uuid-fa73a5df-bb23-2a08-2b79-beff50effdc8.png?w=1650&fit=max&auto=format&n=oWc-pd36yCvpD40u&q=85&s=91930a8997f622fe316cf9b0b5c05b76 1650w, https://mintcdn.com/asapp/oWc-pd36yCvpD40u/image/uuid-fa73a5df-bb23-2a08-2b79-beff50effdc8.png?w=2500&fit=max&auto=format&n=oWc-pd36yCvpD40u&q=85&s=2b2f3c23febf343971241ff3e8b46215 2500w" />
   </Frame>
2. **Response panel:** In the response panel, agents can browse both the global and custom response lists, either using a folder hierarchy or with the provided search field.
   <Frame>
     <img src="https://mintcdn.com/asapp/AXwA7-nbwQCJ8xte/image/uuid-eb6d75f8-8a20-6bd2-66e2-8933fef91fe0.png?fit=max&auto=format&n=AXwA7-nbwQCJ8xte&q=85&s=c5e97d65816e262768d18885ffe14ad5" data-og-width="735" width="735" data-og-height="724" height="724" data-path="image/uuid-eb6d75f8-8a20-6bd2-66e2-8933fef91fe0.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/asapp/AXwA7-nbwQCJ8xte/image/uuid-eb6d75f8-8a20-6bd2-66e2-8933fef91fe0.png?w=280&fit=max&auto=format&n=AXwA7-nbwQCJ8xte&q=85&s=59296a994c482c7a35f276ccfe2c64a0 280w, https://mintcdn.com/asapp/AXwA7-nbwQCJ8xte/image/uuid-eb6d75f8-8a20-6bd2-66e2-8933fef91fe0.png?w=560&fit=max&auto=format&n=AXwA7-nbwQCJ8xte&q=85&s=fcbebc2bbdbfbd9266493b51f7e7d6b6 560w, https://mintcdn.com/asapp/AXwA7-nbwQCJ8xte/image/uuid-eb6d75f8-8a20-6bd2-66e2-8933fef91fe0.png?w=840&fit=max&auto=format&n=AXwA7-nbwQCJ8xte&q=85&s=106ae7d1f8acf201b9694100b10c2234 840w, https://mintcdn.com/asapp/AXwA7-nbwQCJ8xte/image/uuid-eb6d75f8-8a20-6bd2-66e2-8933fef91fe0.png?w=1100&fit=max&auto=format&n=AXwA7-nbwQCJ8xte&q=85&s=3cd5da81a78a84411a1a075ed17d20ca 1100w, https://mintcdn.com/asapp/AXwA7-nbwQCJ8xte/image/uuid-eb6d75f8-8a20-6bd2-66e2-8933fef91fe0.png?w=1650&fit=max&auto=format&n=AXwA7-nbwQCJ8xte&q=85&s=db819bcfd39c00b30efde3cf9a31b194 1650w, https://mintcdn.com/asapp/AXwA7-nbwQCJ8xte/image/uuid-eb6d75f8-8a20-6bd2-66e2-8933fef91fe0.png?w=2500&fit=max&auto=format&n=AXwA7-nbwQCJ8xte&q=85&s=92f9779e63d0d96e0f5c7b0afd29ba3e 2500w" />
   </Frame>

<Note>
  The organically growing response list is not available for agents to browse - responses from this list only appear in suggestions.  Agents are encouraged to add these frequently used responses to their custom response list.
</Note>

### Autocomplete

Once the agent begins typing, AI Compose provides two forms of autocomplete suggestions at different stages in the message composition:

* As the agent begins typing a message, complete response suggestions are available. At this point, the agent is in the early stages of composing their response and several potential complete response options are relevant.
* After several words are typed, a high-confidence phrase completion can be recommended in-line to help agents finish their already well-formed thought.

<Frame>
  <img src="https://mintcdn.com/asapp/NE5s_J_rgoRPqQQt/image/uuid-4fc5f306-00fa-07a2-a43e-6b950b1bb065.png?fit=max&auto=format&n=NE5s_J_rgoRPqQQt&q=85&s=cf32971e470d9f0692ad5201fd43c9d6" data-og-width="1197" width="1197" data-og-height="728" height="728" data-path="image/uuid-4fc5f306-00fa-07a2-a43e-6b950b1bb065.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/asapp/NE5s_J_rgoRPqQQt/image/uuid-4fc5f306-00fa-07a2-a43e-6b950b1bb065.png?w=280&fit=max&auto=format&n=NE5s_J_rgoRPqQQt&q=85&s=a4373b095d722e9d849263e546dfb132 280w, https://mintcdn.com/asapp/NE5s_J_rgoRPqQQt/image/uuid-4fc5f306-00fa-07a2-a43e-6b950b1bb065.png?w=560&fit=max&auto=format&n=NE5s_J_rgoRPqQQt&q=85&s=5b44e423af04a78fdf074d224b6c82b6 560w, https://mintcdn.com/asapp/NE5s_J_rgoRPqQQt/image/uuid-4fc5f306-00fa-07a2-a43e-6b950b1bb065.png?w=840&fit=max&auto=format&n=NE5s_J_rgoRPqQQt&q=85&s=77bcf6123b52680a0f788f865dbff259 840w, https://mintcdn.com/asapp/NE5s_J_rgoRPqQQt/image/uuid-4fc5f306-00fa-07a2-a43e-6b950b1bb065.png?w=1100&fit=max&auto=format&n=NE5s_J_rgoRPqQQt&q=85&s=93fd50837bcad3b712157af8df107abe 1100w, https://mintcdn.com/asapp/NE5s_J_rgoRPqQQt/image/uuid-4fc5f306-00fa-07a2-a43e-6b950b1bb065.png?w=1650&fit=max&auto=format&n=NE5s_J_rgoRPqQQt&q=85&s=7a98634df2eecc54653260e1181ed0ec 1650w, https://mintcdn.com/asapp/NE5s_J_rgoRPqQQt/image/uuid-4fc5f306-00fa-07a2-a43e-6b950b1bb065.png?w=2500&fit=max&auto=format&n=NE5s_J_rgoRPqQQt&q=85&s=5a9c38cbf37235fe89092bb8b9a6bb88 2500w" />
</Frame>

AI Compose provides phrase completions from common, high-frequency phrases used in each implementation's production conversations. AI Compose only makes phrase suggestions when a sufficiently high-confidence phrase is available and only uses language found in the global and custom response library.

### Templated Responses

AI Compose can dynamically insert metadata into designated templated responses in the global response list.

For example, a customer's first name can be automatically populated into this templated response: "Hi *\{name}*, how can I help you today?".

By default, AI Compose supports inserting customer first name, agent first name and the customer's time of day (morning, afternoon, evening) into templated responses. Time of day can be set to a single zone or be dynamically determined for each conversation.

AI Compose also supports inserting custom conversation-specific metadata passed to ASAPP. For more information on custom inserts, reach out to your ASAPP account team.

<Note>
  If the needed metadata is unavailable for a particular templated response (e.g. there is no customer name available), that response will not be suggested by AI Compose.
</Note>

### Fluency and Profanity

**Fluency Boosting**

AI Compose automatically corrects commonly misspelled words once the space bar is pressed following a given word.

Corrections are underlined in the composer for agent-awareness and can be undone if needed by hovering over the corrected word.

**Profanity Blocking**

AI Compose checks for profanity in messages when the agent attempts to send the message. If any terms match ASAPP's profanity criteria, the system does not send the message and informs the agent.

## Customization

### Suggestion Model

The AI Compose suggestion model functions as a custom recommendation service for each agent. The model references the global response list, a library of custom responses created by each agent, and also learns from each agent's unique production message-sending behaviors to surface the best responses.

### Global Response List

Prior to deployment, ASAPP can generate a domain-relevant global response list using representative historical conversations as an input. This is a highly recommended customization to ensure agents receive useful, relevant suggestions as early as possible.

<Note>
  If historical conversation data is unavailable prior to deploying AI Compose, you can use production conversations after deployment to adapt the response list at a later date.
</Note>

| **Option**      | **Description**                                                                                            | **Requirements**                                                 |
| :-------------- | :--------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------- |
| Model-generated | Fully-custom global response list that extracts relevant terminology and sentences from real conversations | 200,000 historical transcripts to enable prior to implementation |

For more information on sending historical transcript files to ASAPP, see [Transmitting Data to ASAPP](/reporting/send-s3#historical-transcript-file-structure).

### Queue/Skill Response List Filtering

AI Compose can filter the global response list by agent queue/skill for a given conversation. For example, a subset of responses appropriate only for sales conversations can be labeled to be removed from technical troubleshooting conversations.

The system labels responses with applicable queue(s)/skill(s) and makes them unavailable for suggestion if their labels do not match the conversation.

| **Option**                                  | **Description**                                                                                              | **Requirements**                          |
| :------------------------------------------ | :----------------------------------------------------------------------------------------------------------- | :---------------------------------------- |
| Global Response List with filter attributes | Global responses are labeled with optional attributes for skills for which they are exclusively appropriate. | Review and labeling of specific responses |

<Tip>
  For technical information about implementing this service, refer to the deployment guides for AI Compose:

  * [AI Compose API](/ai-productivity/ai-compose/deploying-ai-compose-api "Deploying AI Compose API")
  * [AI Compose for LivePerson](/ai-productivity/ai-compose/deploying-ai-compose-for-liveperson "Deploying AI Compose for LivePerson")
  * [AI Compose for Salesforce](/ai-productivity/ai-compose/deploying-ai-compose-for-salesforce "Deploying AI Compose for Salesforce")
</Tip>

## Use Cases

### For Improved Agent Productivity

**Challenge**

Agents spend a lot of time manually crafting responses to similar customer problems.  Using scripts can make the conversation sound robotic, so agents who do use canned responses spend a lot of time adjusting the language to sound more like them or use them too rarely to impact their response time or ability to handle multiple conversations concurrently.

Average response crafting time and messaging concurrency, even with canned response library usage, remains high for most digital messaging programs.

**Using AI Compose**

AI Compose drastically reduces crafting time by not only serving up response suggestions from a much more exhaustive set of responses, but it also addresses the problem of canned responses sounding overly generic by empowering agents to craft messaging in their personal style.

This is why adoption, and therefore efficiency gains overall, are so impressive when AI Compose is deployed.

### For CX Quality and Consistency

**Challenge**

Agents have a lot of information to learn to become domain experts and are often handling issues with which they have limited experience or that they have trouble recalling the best way to handle.

Many companies use a variety of resources that agents have to search through to find answers on how to best handle a customer's problem.  This can be difficult for an agent to juggle while in a live conversation, especially if they are unsure where to begin their search.

**Using AI Compose**

AI Compose learns from the global population of agents over time, which is incredibly useful to newer agents or agents who are beginning to handle conversation in a newer domain.

While the model may not initially have much indication on language that a particular agent likes to use, it naturally adapts to this by surfacing up suggestions from the global response list and global history of how similar conversations have been handled in the past.  This helps ensure that agents follow consistent behaviors when handling issues that they are less certain about.

## FAQs

### Model Improvement

**How does the suggestion model improve over time?**

The system automatically trains the model weekly on the latest historical data, informed by agent interactions with AI Compose at given moments of conversations. As the system observes more situational agent behaviors, it surfaces better response suggestions at more relevant points in the conversation.

**Does the model adapt to topics not seen before?**

As a baseline, models are able to make inferences about what existing responses are most relevant even if the topic is new.

**Do new topics require new entries to be added to the global response list?**

Major new topics are best handled by updating the global response list with appropriate responses. If, for example, you want to prepare for a new product launch, our recommendation is to make additions and edits to the global response list in advance, then upload on the day of the launch.

Our models will immediately start making suggestions from the updated response list. As more agents use the system, the models will become even smarter at identifying when these new responses should be suggested.4.

### Response Lists

**How is the global response list updated?**

AI-Console gives program administrators full control to make targeted or bulk updates to the global response list and manage deployments of those changes. Once deployed to production, response list changes are immediately available to agents.

For more information, refer to the [AI Compose Tooling Guide](/ai-productivity/ai-compose/ai-compose-tooling-guide "AI Compose Tooling Guide").

**Does the global response list change automatically?**

The global response list does not automatically update. It is managed exclusively by product owners for a given implementation.

The organically growing list of commonly used responses updates automatically without need for manual updates.

**What is the difference between the global and custom response list?**

The global response list is managed by center leadership and contains a comprehensive list of responses available across the agent population. This list is intended to be the recommended wording for responding to customers.

The custom response list is managed by each agent and is exclusively accessible to them. Responses in the custom response list are suggested by AI Compose alongside entries in the global response list. Like the global response list, the custom response list also supports a folder structure and can be manually searched by the agent.

**Does the suggestion model act differently from one agent to the next?**

The suggestion model uses the agent's live conversation context and uses agent-specific response from both the custom response list and the organically-growing response list. AI Compose suggestion models are not unique to each agent, but have different inputs and potential responses that personalize their experience.

**Can the global response list be customized by queue/skill?**

Yes. When the global response list is being created or edited, ASAPP can add metadata to specific responses that make them eligible for specific queues or skills, and ineligible for suggestions for all others.
For example, a set of 40 responses may only be applicable for an escalation queue, and be tagged such that they don't appear as suggestions in any conversation that appears in another queue.