diff --git a/.github/workflows/playwright.yml b/.github/workflows/playwright.yml
index b2d77f1c00fd..e70b7e42efae 100644
--- a/.github/workflows/playwright.yml
+++ b/.github/workflows/playwright.yml
@@ -36,7 +36,7 @@ jobs:
           python-version: 3.11
       - uses: actions/setup-node@v4
         with:
-          node-version: 16
+          node-version: 18
       - uses: actions/cache@v4
         with:
           path: ~/.cache/pip
diff --git a/src/dispatch/ai/enums.py b/src/dispatch/ai/enums.py
new file mode 100644
index 000000000000..baf607d0a47e
--- /dev/null
+++ b/src/dispatch/ai/enums.py
@@ -0,0 +1,13 @@
+from dispatch.enums import DispatchEnum
+
+
+class AIEventSource(DispatchEnum):
+    """Source identifiers for AI-generated events."""
+
+    dispatch_genai = "Dispatch GenAI"
+
+
+class AIEventDescription(DispatchEnum):
+    """Description templates for AI-generated events."""
+
+    read_in_summary_created = "AI-generated read-in summary created for {participant_email}"
diff --git a/src/dispatch/ai/models.py b/src/dispatch/ai/models.py
new file mode 100644
index 000000000000..d2f31df1abb5
--- /dev/null
+++ b/src/dispatch/ai/models.py
@@ -0,0 +1,36 @@
+from typing import List
+from pydantic import Field
+
+from dispatch.models import DispatchBase
+
+
+class ReadInSummary(DispatchBase):
+    """
+    Model for structured read-in summary output from AI analysis.
+
+    This model ensures the AI response is properly structured with timeline,
+    actions taken, and current status sections.
+    """
+
+    timeline: List[str] = Field(
+        description="Chronological list of key events and decisions", default_factory=list
+    )
+    actions_taken: List[str] = Field(
+        description="List of actions that were taken to address the security event",
+        default_factory=list,
+    )
+    current_status: str = Field(
+        description="Current status of the security event and any unresolved issues", default=""
+    )
+    summary: str = Field(description="Overall summary of the security event", default="")
+
+
+class ReadInSummaryResponse(DispatchBase):
+    """
+    Response model for read-in summary generation.
+
+    Includes the structured summary and any error messages.
+    """
+
+    summary: ReadInSummary | None = None
+    error_message: str | None = None
diff --git a/src/dispatch/ai/service.py b/src/dispatch/ai/service.py
index ccca6539e736..e1805c0b8c15 100644
--- a/src/dispatch/ai/service.py
+++ b/src/dispatch/ai/service.py
@@ -1,6 +1,7 @@
 import json
 import logging
 
+from dispatch.plugins.dispatch_slack.models import IncidentSubjects
 import tiktoken
 from sqlalchemy.orm import aliased, Session
 
@@ -9,16 +10,25 @@
 from dispatch.enums import Visibility
 from dispatch.incident.models import Incident
 from dispatch.plugin import service as plugin_service
+from dispatch.project.models import Project
 from dispatch.signal import service as signal_service
 from dispatch.tag.models import Tag, TagRecommendationResponse
 from dispatch.tag_type.models import TagType
 from dispatch.case import service as case_service
 from dispatch.incident import service as incident_service
+from dispatch.types import Subject
+from dispatch.event import service as event_service
+from dispatch.enums import EventType
 
 from .exceptions import GenAIException
+from .models import ReadInSummary, ReadInSummaryResponse
+from .enums import AIEventSource, AIEventDescription
 
 log = logging.getLogger(__name__)
 
+# Cache duration for AI-generated read-in summaries (in seconds)
+READ_IN_SUMMARY_CACHE_DURATION = 120  # 2 minutes
+
 
 def get_model_token_limit(model_name: str, buffer_percentage: float = 0.05) -> int:
     """
@@ -101,6 +111,18 @@ def truncate_prompt(
     return truncated_prompt
 
 
+def prepare_prompt_for_model(prompt: str, model_name: str) -> str:
+    """
+    Tokenizes and truncates the prompt if it exceeds the model's token limit.
+    Returns a prompt string that is safe to send to the model.
+    """
+    tokenized_prompt, num_tokens, encoding = num_tokens_from_string(prompt, model_name)
+    model_token_limit = get_model_token_limit(model_name)
+    if num_tokens > model_token_limit:
+        prompt = truncate_prompt(tokenized_prompt, num_tokens, encoding, model_token_limit)
+    return prompt
+
+
 def generate_case_signal_historical_context(case: Case, db_session: Session) -> str:
     """
     Generate historical context for a case stemming from a signal, including related cases and relevant data.
@@ -278,17 +300,10 @@ def generate_case_signal_summary(case: Case, db_session: Session) -> dict[str, s
     </historical_context>
     """
 
-    tokenized_prompt, num_tokens, encoding = num_tokens_from_string(
+    prompt = prepare_prompt_for_model(
         prompt, genai_plugin.instance.configuration.chat_completion_model
     )
 
-    # we check if the prompt exceeds the token limit
-    model_token_limit = get_model_token_limit(
-        genai_plugin.instance.configuration.chat_completion_model
-    )
-    if num_tokens > model_token_limit:
-        prompt = truncate_prompt(tokenized_prompt, num_tokens, encoding, model_token_limit)
-
     # we generate the analysis
     response = genai_plugin.instance.chat_completion(prompt=prompt)
 
@@ -372,23 +387,26 @@ def generate_incident_summary(incident: Incident, db_session: Session) -> str:
             {pir_doc}
         """
 
-        tokenized_prompt, num_tokens, encoding = num_tokens_from_string(
+        prompt = prepare_prompt_for_model(
             prompt, genai_plugin.instance.configuration.chat_completion_model
         )
 
-        # we check if the prompt exceeds the token limit
-        model_token_limit = get_model_token_limit(
-            genai_plugin.instance.configuration.chat_completion_model
-        )
-        if num_tokens > model_token_limit:
-            prompt = truncate_prompt(tokenized_prompt, num_tokens, encoding, model_token_limit)
-
         summary = genai_plugin.instance.chat_completion(prompt=prompt)
 
         incident.summary = summary
         db_session.add(incident)
         db_session.commit()
 
+        # Log the AI summary generation event
+        event_service.log_incident_event(
+            db_session=db_session,
+            source="Dispatch Core App",
+            description="AI-generated incident summary created",
+            incident_id=incident.id,
+            details={"summary": summary},
+            type=EventType.other,
+        )
+
         return summary
 
     except Exception as e:
@@ -529,17 +547,10 @@ def get_tag_recommendations(
 
     prompt += f"** Tags you can use: {tag_list} \n ** Security event details: {resources}"
 
-    tokenized_prompt, num_tokens, encoding = num_tokens_from_string(
+    prompt = prepare_prompt_for_model(
         prompt, genai_plugin.instance.configuration.chat_completion_model
     )
 
-    # we check if the prompt exceeds the token limit
-    model_token_limit = get_model_token_limit(
-        genai_plugin.instance.configuration.chat_completion_model
-    )
-    if num_tokens > model_token_limit:
-        prompt = truncate_prompt(tokenized_prompt, num_tokens, encoding, model_token_limit)
-
     try:
         result = genai_plugin.instance.chat_completion(prompt=prompt)
 
@@ -560,3 +571,132 @@ def get_tag_recommendations(
         log.exception(f"Error generating tag recommendations: {e}")
         message = "AI tag suggestions encountered an error. Please try again later."
         return TagRecommendationResponse(recommendations=[], error_message=message)
+
+
+def generate_read_in_summary(
+    *,
+    db_session,
+    subject: Subject,
+    project: Project,
+    channel_id: str,
+    important_reaction: str,
+    participant_email: str = "",
+) -> ReadInSummaryResponse:
+    """
+    Generate a read-in summary for a subject.
+
+    Args:
+        subject (Subject): The subject object for which the read-in summary is being generated.
+        project (Project): The project context.
+        channel_id (str): The channel ID to get conversation from.
+        important_reaction (str): The reaction to filter important messages.
+        participant_email (str): The email of the participant for whom the summary was generated.
+
+    Returns:
+        ReadInSummaryResponse: A structured response containing the read-in summary or error message.
+    """
+    subject_type = subject.type
+
+    # Check for recent summary event
+    if subject_type == IncidentSubjects.incident:
+        recent_event = event_service.get_recent_summary_event(
+            db_session, incident_id=subject.id, max_age_seconds=READ_IN_SUMMARY_CACHE_DURATION
+        )
+    else:
+        recent_event = event_service.get_recent_summary_event(
+            db_session, case_id=subject.id, max_age_seconds=READ_IN_SUMMARY_CACHE_DURATION
+        )
+
+    if recent_event and recent_event.details:
+        try:
+            summary = ReadInSummary(**recent_event.details)
+            return ReadInSummaryResponse(summary=summary)
+        except Exception as e:
+            log.warning(
+                f"Failed to parse cached summary from event {recent_event.id}: {e}. Generating new summary."
+            )
+
+    # Don't generate if no enabled ai plugin or storage plugin
+    genai_plugin = plugin_service.get_active_instance(
+        db_session=db_session, plugin_type="artificial-intelligence", project_id=project.id
+    )
+    if not genai_plugin:
+        message = f"Read-in summary not generated for {subject.name}. No artificial-intelligence plugin enabled."
+        log.warning(message)
+        return ReadInSummaryResponse(error_message=message)
+
+    conversation_plugin = plugin_service.get_active_instance(
+        db_session=db_session, plugin_type="conversation", project_id=project.id
+    )
+    if not conversation_plugin:
+        message = (
+            f"Read-in summary not generated for {subject.name}. No conversation plugin enabled."
+        )
+        log.warning(message)
+        return ReadInSummaryResponse(error_message=message)
+
+    conversation = conversation_plugin.instance.get_conversation(
+        conversation_id=channel_id, important_reaction=important_reaction
+    )
+    if not conversation:
+        message = f"Read-in summary not generated for {subject.name}. No conversation found."
+        log.warning(message)
+        return ReadInSummaryResponse(error_message=message)
+
+    system_message = """You are a cybersecurity analyst tasked with creating structured read-in summaries.
+    Analyze the provided channel messages and extract key information about a security event.
+    Focus on identifying:
+    1. Timeline: Chronological list of key events and decisions (skip channel join/remove messages)
+       - For all timeline events, format timestamps as YYYY-MM-DD HH:MM (no seconds, no 'T').
+    2. Actions taken: List of actions that were taken to address the security event
+    3. Current status: Current status of the security event and any unresolved issues
+    4. Summary: Overall summary of the security event
+
+    Only include the most relevant events and outcomes. Be clear and concise."""
+
+    prompt = f"""Analyze the following channel messages regarding a security event and provide a structured summary.
+
+    Channel messages: {conversation}
+    """
+
+    prompt = prepare_prompt_for_model(
+        prompt, genai_plugin.instance.configuration.chat_completion_model
+    )
+
+    try:
+        result = genai_plugin.instance.chat_parse(
+            prompt=prompt, response_model=ReadInSummary, system_message=system_message
+        )
+
+        # Log the AI read-in summary generation event
+        if subject.type == IncidentSubjects.incident:
+            # This is an incident
+            event_service.log_incident_event(
+                db_session=db_session,
+                source=AIEventSource.dispatch_genai,
+                description=AIEventDescription.read_in_summary_created.format(
+                    participant_email=participant_email
+                ),
+                incident_id=subject.id,
+                details=result.dict(),
+                type=EventType.other,
+            )
+        else:
+            # This is a case
+            event_service.log_case_event(
+                db_session=db_session,
+                source=AIEventSource.dispatch_genai,
+                description=AIEventDescription.read_in_summary_created.format(
+                    participant_email=participant_email
+                ),
+                case_id=subject.id,
+                details=result.dict(),
+                type=EventType.other,
+            )
+
+        return ReadInSummaryResponse(summary=result)
+
+    except Exception as e:
+        log.exception(f"Error generating read-in summary: {e}")
+        error_msg = f"Error generating read-in summary: {str(e)}"
+        return ReadInSummaryResponse(error_message=error_msg)
diff --git a/src/dispatch/case/type/models.py b/src/dispatch/case/type/models.py
index 9e6db2b12229..c3b44114ba9d 100644
--- a/src/dispatch/case/type/models.py
+++ b/src/dispatch/case/type/models.py
@@ -1,4 +1,5 @@
 """Models for case types and related entities in the Dispatch application."""
+
 from pydantic import field_validator, AnyHttpUrl
 
 from sqlalchemy import JSON, Boolean, Column, ForeignKey, Integer, String
@@ -19,6 +20,7 @@
 
 class CaseType(ProjectMixin, Base):
     """SQLAlchemy model for case types, representing different types of cases in the system."""
+
     __table_args__ = (UniqueConstraint("name", "project_id"),)
     id = Column(Integer, primary_key=True)
     name = Column(String)
@@ -30,6 +32,7 @@ class CaseType(ProjectMixin, Base):
     plugin_metadata = Column(JSON, default=[])
     conversation_target = Column(String)
     auto_close = Column(Boolean, default=False, server_default=false())
+    generate_read_in_summary = Column(Boolean, default=False, server_default=false())
 
     # the catalog here is simple to help matching "named entities"
     search_vector = Column(TSVectorType("name", regconfig="pg_catalog.simple"))
@@ -66,6 +69,7 @@ def get_meta(self, slug):
 # Pydantic models
 class Document(DispatchBase):
     """Pydantic model for a document related to a case type."""
+
     id: PrimaryKey
     description: str | None = None
     name: NameStr
@@ -76,6 +80,7 @@ class Document(DispatchBase):
 
 class IncidentType(DispatchBase):
     """Pydantic model for an incident type related to a case type."""
+
     id: PrimaryKey
     description: str | None = None
     name: NameStr
@@ -84,6 +89,7 @@ class IncidentType(DispatchBase):
 
 class Service(DispatchBase):
     """Pydantic model for a service related to a case type."""
+
     id: PrimaryKey
     description: str | None = None
     external_id: str
@@ -94,6 +100,7 @@ class Service(DispatchBase):
 
 class CaseTypeBase(DispatchBase):
     """Base Pydantic model for case types, used for shared fields."""
+
     case_template_document: Document | None = None
     conversation_target: str | None = None
     default: bool | None = False
@@ -108,6 +115,7 @@ class CaseTypeBase(DispatchBase):
     visibility: str | None = None
     cost_model: CostModelRead | None = None
     auto_close: bool | None = False
+    generate_read_in_summary: bool | None = False
 
     @field_validator("plugin_metadata", mode="before")
     @classmethod
@@ -118,19 +126,23 @@ def replace_none_with_empty_list(cls, value):
 
 class CaseTypeCreate(CaseTypeBase):
     """Pydantic model for creating a new case type."""
+
     pass
 
 
 class CaseTypeUpdate(CaseTypeBase):
     """Pydantic model for updating an existing case type."""
+
     id: PrimaryKey | None = None
 
 
 class CaseTypeRead(CaseTypeBase):
     """Pydantic model for reading a case type from the database."""
+
     id: PrimaryKey
 
 
 class CaseTypePagination(Pagination):
     """Pydantic model for paginated case type results."""
+
     items: list[CaseTypeRead] = []
diff --git a/src/dispatch/database/revisions/tenant/versions/2025-07-08_f63ad392dbbf.py b/src/dispatch/database/revisions/tenant/versions/2025-07-08_f63ad392dbbf.py
new file mode 100644
index 000000000000..951b51a7d742
--- /dev/null
+++ b/src/dispatch/database/revisions/tenant/versions/2025-07-08_f63ad392dbbf.py
@@ -0,0 +1,41 @@
+"""Adds settings to case and incident types for generating read-in summaries.
+
+Revision ID: f63ad392dbbf
+Revises: 5ed5defd1a55
+Create Date: 2025-07-08 13:56:35.033622
+
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "f63ad392dbbf"
+down_revision = "5ed5defd1a55"
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.add_column(
+        "case_type",
+        sa.Column(
+            "generate_read_in_summary", sa.Boolean(), server_default=sa.text("false"), nullable=True
+        ),
+    )
+    op.add_column(
+        "incident_type",
+        sa.Column(
+            "generate_read_in_summary", sa.Boolean(), server_default=sa.text("false"), nullable=True
+        ),
+    )
+    # ### end Alembic commands ###
+
+
+def downgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_column("incident_type", "generate_read_in_summary")
+    op.drop_column("case_type", "generate_read_in_summary")
+    # ### end Alembic commands ###
diff --git a/src/dispatch/event/service.py b/src/dispatch/event/service.py
index 88dd5125d610..1a0dad35b2e7 100644
--- a/src/dispatch/event/service.py
+++ b/src/dispatch/event/service.py
@@ -836,3 +836,47 @@ def export_case_timeline(
         raise Exception("No data to export, please check filter selection")
 
     return True
+
+
+def get_recent_summary_event(
+    db_session,
+    case_id: int | None = None,
+    incident_id: int | None = None,
+    max_age_seconds: int = 300,
+):  # 5 minutes default
+    """Get the most recent AI read-in summary event for this subject."""
+    from datetime import datetime, timedelta
+    from dispatch.ai.enums import AIEventSource, AIEventDescription
+
+    cutoff_time = datetime.utcnow() - timedelta(seconds=max_age_seconds)
+
+    if incident_id:
+        # This is an incident
+        return (
+            db_session.query(Event)
+            .filter(Event.incident_id == incident_id)
+            .filter(Event.source == AIEventSource.dispatch_genai)
+            .filter(
+                Event.description.like(
+                    f"{AIEventDescription.read_in_summary_created.format(participant_email='')}%"
+                )
+            )
+            .filter(Event.started_at >= cutoff_time)
+            .order_by(Event.started_at.desc())
+            .first()
+        )
+    else:
+        # This is a case
+        return (
+            db_session.query(Event)
+            .filter(Event.case_id == case_id)
+            .filter(Event.source == AIEventSource.dispatch_genai)
+            .filter(
+                Event.description.like(
+                    f"{AIEventDescription.read_in_summary_created.format(participant_email='')}%"
+                )
+            )
+            .filter(Event.started_at >= cutoff_time)
+            .order_by(Event.started_at.desc())
+            .first()
+        )
diff --git a/src/dispatch/incident/type/models.py b/src/dispatch/incident/type/models.py
index d147838203c5..a46dc79eda91 100644
--- a/src/dispatch/incident/type/models.py
+++ b/src/dispatch/incident/type/models.py
@@ -6,6 +6,7 @@
 from sqlalchemy.event import listen
 from sqlalchemy.ext.hybrid import hybrid_method
 from sqlalchemy.orm import relationship
+from sqlalchemy.sql import false
 from sqlalchemy.sql.schema import UniqueConstraint
 from sqlalchemy_utils import TSVectorType
 
@@ -19,8 +20,10 @@
 from dispatch.project.models import ProjectRead
 from dispatch.service.models import ServiceRead
 
+
 class IncidentType(ProjectMixin, Base):
     """SQLAlchemy model for incident type resources."""
+
     __table_args__ = (UniqueConstraint("name", "project_id"),)
     id = Column(Integer, primary_key=True)
     name = Column(String)
@@ -35,6 +38,7 @@ class IncidentType(ProjectMixin, Base):
     exclude_from_review = Column(Boolean, default=False)
     plugin_metadata = Column(JSON, default=[])
     task_plugin_metadata = Column(JSON, default=[])
+    generate_read_in_summary = Column(Boolean, default=False, server_default=false())
 
     incident_template_document_id = Column(Integer, ForeignKey("document.id"))
     incident_template_document = relationship(
@@ -99,6 +103,7 @@ def get_task_meta(self, slug):
 
 class Document(DispatchBase):
     """Pydantic model for a document related to an incident type."""
+
     id: PrimaryKey
     name: NameStr
     resource_type: str | None = None
@@ -110,6 +115,7 @@ class Document(DispatchBase):
 # Pydantic models...
 class IncidentTypeBase(DispatchBase):
     """Base Pydantic model for incident type resources."""
+
     name: NameStr
     visibility: str | None = None
     description: str | None = None
@@ -128,6 +134,7 @@ class IncidentTypeBase(DispatchBase):
     channel_description: str | None = None
     description_service: ServiceRead | None = None
     task_plugin_metadata: list[PluginMetadata] = []
+    generate_read_in_summary: bool | None = False
 
     @field_validator("plugin_metadata", mode="before")
     @classmethod
@@ -138,21 +145,25 @@ def replace_none_with_empty_list(cls, value):
 
 class IncidentTypeCreate(IncidentTypeBase):
     """Pydantic model for creating an incident type resource."""
+
     pass
 
 
 class IncidentTypeUpdate(IncidentTypeBase):
     """Pydantic model for updating an incident type resource."""
+
     id: PrimaryKey | None = None
 
 
 class IncidentTypeRead(IncidentTypeBase):
     """Pydantic model for reading an incident type resource."""
+
     id: PrimaryKey
 
 
 class IncidentTypeReadMinimal(DispatchBase):
     """Pydantic model for reading a minimal incident type resource."""
+
     id: PrimaryKey
     name: NameStr
     visibility: str | None = None
@@ -164,4 +175,5 @@ class IncidentTypeReadMinimal(DispatchBase):
 
 class IncidentTypePagination(Pagination):
     """Pydantic model for paginated incident type results."""
+
     items: list[IncidentTypeRead] = []
diff --git a/src/dispatch/plugins/bases/artificial_intelligence.py b/src/dispatch/plugins/bases/artificial_intelligence.py
index 247910be113e..a136122d0f03 100644
--- a/src/dispatch/plugins/bases/artificial_intelligence.py
+++ b/src/dispatch/plugins/bases/artificial_intelligence.py
@@ -15,5 +15,8 @@ class ArtificialIntelligencePlugin(Plugin):
     def chat_completion(self, items, **kwargs):
         raise NotImplementedError
 
+    def chat_parse(self, items, **kwargs):
+        raise NotImplementedError
+
     def list_models(self, items, **kwargs):
         raise NotImplementedError
diff --git a/src/dispatch/plugins/dispatch_openai/plugin.py b/src/dispatch/plugins/dispatch_openai/plugin.py
index 227b85f9d653..a7103fd12cf3 100644
--- a/src/dispatch/plugins/dispatch_openai/plugin.py
+++ b/src/dispatch/plugins/dispatch_openai/plugin.py
@@ -9,6 +9,7 @@
 import logging
 
 from openai import OpenAI
+from typing import TypeVar, Type
 
 from dispatch.decorators import apply, counter, timer
 from dispatch.plugins import dispatch_openai as openai_plugin
@@ -16,6 +17,7 @@
 from dispatch.plugins.dispatch_openai.config import (
     OpenAIConfiguration,
 )
+from pydantic import BaseModel
 
 logger = logging.getLogger(__name__)
 
@@ -56,3 +58,29 @@ def chat_completion(self, prompt: str) -> dict:
             raise
 
         return completion.choices[0].message
+
+    T = TypeVar("T", bound=BaseModel)
+
+    def chat_parse(self, prompt: str, response_model: Type[T]) -> T:
+        client = OpenAI(api_key=self.api_key)
+
+        try:
+            completion = client.chat.completions.parse(
+                model=self.model,
+                response_format=response_model,
+                messages=[
+                    {
+                        "role": "system",
+                        "content": self.system_message,
+                    },
+                    {
+                        "role": "user",
+                        "content": prompt,
+                    },
+                ],
+            )
+        except Exception as e:
+            logger.error(e)
+            raise
+
+        return completion.choices[0].message.parsed
diff --git a/src/dispatch/plugins/dispatch_slack/case/messages.py b/src/dispatch/plugins/dispatch_slack/case/messages.py
index 9061fd951c9d..c6baadc51a88 100644
--- a/src/dispatch/plugins/dispatch_slack/case/messages.py
+++ b/src/dispatch/plugins/dispatch_slack/case/messages.py
@@ -11,6 +11,7 @@
     Section,
 )
 from blockkit.surfaces import Block
+from dispatch.plugins.dispatch_slack.service import create_genai_message_metadata_blocks
 from sqlalchemy.orm import Session
 
 from dispatch.ai import service as ai_service
@@ -325,48 +326,6 @@ def create_action_buttons_message(
     return Message(blocks=signal_metadata_blocks).build()["blocks"]
 
 
-def json_to_slack_format(json_message: dict[str, str]) -> str:
-    """
-    Converts a JSON dictionary to Slack markup format.
-
-    Args:
-        json_dict (dict): The JSON dictionary to convert.
-
-    Returns:
-        str: A string formatted with Slack markup.
-    """
-    slack_message = ""
-    for key, value in json_message.items():
-        slack_message += f"*{key}*\n{value}\n\n"
-    return slack_message.strip()
-
-
-def create_genai_signal_message_metadata_blocks(
-    signal_metadata_blocks: list[Block], message: str | dict[str, str]
-) -> list[Block]:
-    """
-    Appends a GenAI signal analysis section to the signal metadata blocks.
-
-    Args:
-        signal_metadata_blocks (list[Block]): The list of existing signal metadata blocks.
-        message (str | dict[str, str]): The GenAI analysis message, either as a string or a dictionary.
-
-    Returns:
-        list[Block]: The updated list of signal metadata blocks with the GenAI analysis section appended.
-    """
-    if isinstance(message, dict):
-        message = json_to_slack_format(message)
-
-    # Truncate the text if it exceeds Block Kit's maximum length of 3000 characters
-    text = f":magic_wand: *GenAI Alert Analysis*\n\n{message}"
-    text = f"{text[:2997]}..." if len(text) > 3000 else text
-    signal_metadata_blocks.append(
-        Section(text=text),
-    )
-    signal_metadata_blocks.append(Divider())
-    return Message(blocks=signal_metadata_blocks).build()["blocks"]
-
-
 def create_genai_signal_analysis_message(
     case: Case,
     db_session: Session,
@@ -393,7 +352,9 @@ def create_genai_signal_analysis_message(
             "We encountered an error while generating the GenAI analysis summary for this case."
         )
         log.warning(f"Error generating GenAI analysis summary for case {case.id}. Error: {e}")
-    return summary, create_genai_signal_message_metadata_blocks(signal_metadata_blocks, summary)
+    return summary, create_genai_message_metadata_blocks(
+        title="GenAI Alert Analysis", blocks=signal_metadata_blocks, message=summary
+    )
 
 
 def create_signal_engagement_message(
diff --git a/src/dispatch/plugins/dispatch_slack/incident/interactive.py b/src/dispatch/plugins/dispatch_slack/incident/interactive.py
index 8bed0b649b65..427882c6ce99 100644
--- a/src/dispatch/plugins/dispatch_slack/incident/interactive.py
+++ b/src/dispatch/plugins/dispatch_slack/incident/interactive.py
@@ -1,5 +1,6 @@
 import logging
 import re
+import time
 import uuid
 from functools import partial
 from datetime import datetime, timedelta
@@ -27,6 +28,8 @@
 from slack_sdk.web.client import WebClient
 from sqlalchemy.orm import Session
 
+from dispatch.ai import service as ai_service
+from dispatch.ai.models import ReadInSummary
 from dispatch.auth.models import DispatchUser
 from dispatch.case import service as case_service
 from dispatch.case import flows as case_flows
@@ -780,6 +783,52 @@ def replace_slack_users_in_message(client: Any, message: str) -> str:
     return re.sub(r"<@([^>]+)>", lambda x: f"@{get_user_name_from_id(client, x.group(1))}", message)
 
 
+def create_read_in_summary_blocks(summary: ReadInSummary) -> list:
+    """Creates Slack blocks from a structured read-in summary."""
+    blocks = []
+
+    # Add AI disclaimer at the top
+    blocks.append(
+        Context(
+            elements=[
+                MarkdownText(
+                    text=":sparkles: *This entire block is AI-generated and may contain errors or inaccuracies. Please verify the information before relying on it.*"
+                )
+            ]
+        ).build()
+    )
+
+    # Add AI-generated summary if available
+    if summary.summary:
+        blocks.append(
+            Section(text=":magic_wand: *AI-Generated Summary*\n{0}".format(summary.summary)).build()
+        )
+
+    # Add timeline events if available
+    if summary.timeline:
+        timeline_text = "\n".join([f"• {event}" for event in summary.timeline])
+        blocks.append(
+            Section(
+                text=":alarm_clock: *Timeline* _(times in UTC)_\n{0}".format(timeline_text)
+            ).build()
+        )
+
+    # Add actions taken if available
+    if summary.actions_taken:
+        actions_text = "\n".join([f"• {action}" for action in summary.actions_taken])
+        blocks.append(
+            Section(text=":white_check_mark: *Actions Taken*\n{0}".format(actions_text)).build()
+        )
+
+    # Add current status if available
+    if summary.current_status:
+        blocks.append(
+            Section(text=":bar_chart: *Current Status*\n{0}".format(summary.current_status)).build()
+        )
+
+    return blocks
+
+
 def handle_timeline_added_event(
     ack: Ack, client: Any, context: BoltContext, payload: Any, db_session: Session
 ) -> None:
@@ -1122,6 +1171,13 @@ def handle_member_joined_channel(
             "Unable to handle member_joined_channel Slack event. Dispatch user unknown."
         )
 
+    # sleep for a second to allow the participant to be added to the incident
+    time.sleep(1)
+
+    generate_read_in_summary = False
+    subject_type = "incident"
+    project = None
+
     if context["subject"].type == IncidentSubjects.incident:
         participant = incident_flows.incident_add_or_reactivate_participant_flow(
             user_email=user.email, incident_id=int(context["subject"].id), db_session=db_session
@@ -1136,6 +1192,12 @@ def handle_member_joined_channel(
         incident = incident_service.get(
             db_session=db_session, incident_id=int(context["subject"].id)
         )
+        project = incident.project
+        generate_read_in_summary = getattr(
+            incident.incident_type, "generate_read_in_summary", False
+        )
+        if incident.visibility == Visibility.restricted:
+            generate_read_in_summary = False
 
         # If the user was invited, the message will include an inviter property containing the user ID of the inviting user.
         # The property will be absent when a user manually joins a channel, or a user is added by default (e.g. #general channel).
@@ -1176,6 +1238,7 @@ def handle_member_joined_channel(
         db_session.commit()
 
     if context["subject"].type == CaseSubjects.case:
+        subject_type = "case"
         case = case_service.get(db_session=db_session, case_id=int(context["subject"].id))
 
         if not case.dedicated_channel:
@@ -1191,6 +1254,11 @@ def handle_member_joined_channel(
             # Participant is already in the case channel.
             return
 
+        project = case.project
+        generate_read_in_summary = getattr(case.case_type, "generate_read_in_summary", False)
+        if case.visibility == Visibility.restricted:
+            generate_read_in_summary = False
+
         participant.user_conversation_id = context["user_id"]
 
         # If the user was invited, the message will include an inviter property containing the user ID of the inviting user.
@@ -1228,6 +1296,41 @@ def handle_member_joined_channel(
         db_session.add(participant)
         db_session.commit()
 
+    if not generate_read_in_summary:
+        return
+
+    # Generate read-in summary for user
+    summary_response = ai_service.generate_read_in_summary(
+        db_session=db_session,
+        subject=context["subject"],
+        project=project,
+        channel_id=context["channel_id"],
+        important_reaction=context["config"].timeline_event_reaction,
+        participant_email=user.email,
+    )
+
+    if summary_response and summary_response.summary:
+        blocks = create_read_in_summary_blocks(summary_response.summary)
+        blocks.append(
+            Context(
+                elements=[
+                    MarkdownText(
+                        text="NOTE: The block above was AI-generated and may contain errors or inaccuracies. Please verify the information before relying on it."
+                    )
+                ]
+            ).build()
+        )
+        dispatch_slack_service.send_ephemeral_message(
+            client=client,
+            conversation_id=context["channel_id"],
+            user_id=context["user_id"],
+            text=f"Here is a summary of what has happened so far in this {subject_type}",
+            blocks=blocks,
+        )
+    elif summary_response and summary_response.error_message:
+        # Log the error but don't show it to the user to avoid confusion
+        log.warning(f"Failed to generate read-in summary: {summary_response.error_message}")
+
 
 @app.event(
     "member_left_channel",
diff --git a/src/dispatch/plugins/dispatch_slack/plugin.py b/src/dispatch/plugins/dispatch_slack/plugin.py
index 5e10e0fafc2d..906fb5b6bf09 100644
--- a/src/dispatch/plugins/dispatch_slack/plugin.py
+++ b/src/dispatch/plugins/dispatch_slack/plugin.py
@@ -51,6 +51,7 @@
     create_slack_client,
     does_user_exist,
     emails_to_user_ids,
+    get_channel_activity,
     get_user_avatar_url,
     get_user_info_by_id,
     get_user_profile_by_email,
@@ -442,9 +443,34 @@ def fetch_events(
             activity = event_instance.fetch_activity(client, subject, oldest)
             return activity
         except Exception as e:
-            logger.exception("An error occurred while fetching incident or case events from the Slack plugin.", exc_info=e)
+            logger.exception(
+                "An error occurred while fetching incident or case events from the Slack plugin.",
+                exc_info=e,
+            )
             raise
 
+    def get_conversation(
+        self, conversation_id: str, oldest: str = "0", important_reaction: str | None = None
+    ) -> list:
+        """
+        Fetches the top-level posts from a Slack conversation.
+
+        Args:
+            conversation_id (str): The ID of the Slack conversation.
+            oldest (str): The oldest timestamp to fetch messages from.
+
+        Returns:
+            list: A list of tuples containing the timestamp and user ID of each message.
+        """
+        client = create_slack_client(self.configuration)
+        return get_channel_activity(
+            client,
+            conversation_id,
+            oldest,
+            include_message_text=True,
+            important_reaction=important_reaction,
+        )
+
     def get_conversation_replies(self, conversation_id: str, thread_ts: str) -> list[str]:
         """
         Fetches replies from a specific thread in a Slack conversation.
diff --git a/src/dispatch/plugins/dispatch_slack/service.py b/src/dispatch/plugins/dispatch_slack/service.py
index f3f7fedd2094..41fb529ee104 100644
--- a/src/dispatch/plugins/dispatch_slack/service.py
+++ b/src/dispatch/plugins/dispatch_slack/service.py
@@ -3,7 +3,8 @@
 import logging
 from datetime import datetime
 
-from blockkit import Message, Section
+from blockkit.surfaces import Block
+from blockkit import Divider, Message, Section
 from requests import Timeout
 from slack_sdk.errors import SlackApiError
 from slack_sdk.web.client import WebClient
@@ -595,11 +596,27 @@ def get_thread_activity(
     return heapq.nsmallest(len(result), result)
 
 
-def get_channel_activity(client: WebClient, conversation_id: str, oldest: str = "0") -> list:
+def has_important_reaction(message, important_reaction):
+    if not important_reaction:
+        return False
+    for reaction in message.get("reactions", []):
+        if reaction["name"] == important_reaction:
+            return True
+    return False
+
+
+def get_channel_activity(
+    client: WebClient,
+    conversation_id: str,
+    oldest: str = "0",
+    include_message_text: bool = False,
+    important_reaction: str | None = None,
+) -> list:
     """Gets all top-level messages for a given Slack channel.
 
     Returns:
-        A sorted list of tuples (utc_dt, user_id) of each message in the channel.
+        A sorted list of tuples (utc_dt, user_id) of each message in the channel,
+        or (utc_dt, user_id, message_text), depending on include_message_text.
     """
     result = []
     cursor = None
@@ -622,10 +639,59 @@ def get_channel_activity(client: WebClient, conversation_id: str, oldest: str =
             # Resolves users for messages.
             if "user" in message:
                 user_id = resolve_user(client, message["user"])["id"]
-                heapq.heappush(result, (datetime.utcfromtimestamp(float(message["ts"])), user_id))
+                utc_dt = datetime.utcfromtimestamp(float(message["ts"]))
+                if include_message_text:
+                    message_text = message.get("text", "")
+                    if has_important_reaction(message, important_reaction):
+                        message_text = f"IMPORTANT!: {message_text}"
+                    heapq.heappush(result, (utc_dt, user_id, message_text))
+                else:
+                    heapq.heappush(result, (utc_dt, user_id))
 
         if not response["has_more"]:
             break
         cursor = response["response_metadata"]["next_cursor"]
 
     return heapq.nsmallest(len(result), result)
+
+
+def json_to_slack_format(json_message: dict[str, str]) -> str:
+    """
+    Converts a JSON dictionary to Slack markup format.
+
+    Args:
+        json_dict (dict): The JSON dictionary to convert.
+
+    Returns:
+        str: A string formatted with Slack markup.
+    """
+    slack_message = ""
+    for key, value in json_message.items():
+        slack_message += f"*{key}*\n{value}\n\n"
+    return slack_message.strip()
+
+
+def create_genai_message_metadata_blocks(
+    title: str, blocks: list[Block], message: str | dict[str, str]
+) -> list[Block]:
+    """
+    Appends a GenAI section to any existing metadata blocks.
+
+    Args:
+        blocks (list[Block]): The list of existing  metadata blocks.
+        message (str | dict[str, str]): The GenAI message, either as a string or a dictionary.
+
+    Returns:
+        list[Block]: The updated list of metadata blocks with the GenAI section appended.
+    """
+    if isinstance(message, dict):
+        message = json_to_slack_format(message)
+
+    # Truncate the text if it exceeds Block Kit's maximum length of 3000 characters
+    text = f":magic_wand: *{title}*\n\n{message}"
+    text = f"{text[:2997]}..." if len(text) > 3000 else text
+    blocks.append(
+        Section(text=text),
+    )
+    blocks.append(Divider())
+    return Message(blocks=blocks).build()["blocks"]
diff --git a/src/dispatch/static/dispatch/src/case/type/NewEditSheet.vue b/src/dispatch/static/dispatch/src/case/type/NewEditSheet.vue
index 6767f6d60023..952b1f812f65 100644
--- a/src/dispatch/static/dispatch/src/case/type/NewEditSheet.vue
+++ b/src/dispatch/static/dispatch/src/case/type/NewEditSheet.vue
@@ -139,6 +139,13 @@
                   hint="Check if this case type should be excluded from all metrics."
                 />
               </v-col>
+              <v-col cols="12">
+                <v-checkbox
+                  v-model="generate_read_in_summary"
+                  label="Enable AI-Generated Read-In Summaries"
+                  hint="If checked, AI-generated read-in summaries will be automatically generated for cases of this type."
+                />
+              </v-col>
               <v-col cols="12">
                 <plugin-metadata-input v-model="plugin_metadata" :project="project" />
               </v-col>
@@ -199,6 +206,7 @@ export default {
       "selected.description",
       "selected.enabled",
       "selected.exclude_from_metrics",
+      "selected.generate_read_in_summary",
       "selected.id",
       "selected.incident_type",
       "selected.loading",
diff --git a/src/dispatch/static/dispatch/src/case/type/store.js b/src/dispatch/static/dispatch/src/case/type/store.js
index 7badfa11dde2..d8040ad07715 100644
--- a/src/dispatch/static/dispatch/src/case/type/store.js
+++ b/src/dispatch/static/dispatch/src/case/type/store.js
@@ -23,6 +23,7 @@ const getDefaultSelectedState = () => {
     project: null,
     slug: null,
     visibility: null,
+    generate_read_in_summary: false,
   }
 }
 
diff --git a/src/dispatch/static/dispatch/src/incident/type/NewEditSheet.vue b/src/dispatch/static/dispatch/src/incident/type/NewEditSheet.vue
index de7fb680420a..8cefd9af507a 100644
--- a/src/dispatch/static/dispatch/src/incident/type/NewEditSheet.vue
+++ b/src/dispatch/static/dispatch/src/incident/type/NewEditSheet.vue
@@ -118,6 +118,13 @@
                   hint="Check if this incident type should be excluded from all metrics."
                 />
               </v-col>
+              <v-col cols="12">
+                <v-checkbox
+                  v-model="generate_read_in_summary"
+                  label="Enable AI-Generated Read-In Summaries"
+                  hint="If checked, AI-generated read-in summaries will be automatically generated for incidents of this type."
+                />
+              </v-col>
               <v-col cols="12">
                 <v-checkbox
                   v-model="exclude_from_reminders"
@@ -243,6 +250,7 @@ export default {
       "selected.channel_description",
       "selected.description_service",
       "selected.task_plugin_metadata",
+      "selected.generate_read_in_summary",
     ]),
     ...mapFields("incident_type", {
       default_incident_type: "selected.default",
diff --git a/src/dispatch/static/dispatch/src/incident/type/store.js b/src/dispatch/static/dispatch/src/incident/type/store.js
index b33f4a437e83..ce708956ef61 100644
--- a/src/dispatch/static/dispatch/src/incident/type/store.js
+++ b/src/dispatch/static/dispatch/src/incident/type/store.js
@@ -24,6 +24,7 @@ const getDefaultSelectedState = () => {
     default: false,
     project: null,
     cost_model: null,
+    generate_read_in_summary: false,
   }
 }
 
diff --git a/tests/ai/__init__.py b/tests/ai/__init__.py
new file mode 100644
index 000000000000..92025db97f3c
--- /dev/null
+++ b/tests/ai/__init__.py
@@ -0,0 +1 @@
+# AI tests package
diff --git a/tests/ai/test_ai_service.py b/tests/ai/test_ai_service.py
new file mode 100644
index 000000000000..4e9d996702c2
--- /dev/null
+++ b/tests/ai/test_ai_service.py
@@ -0,0 +1,474 @@
+import pytest
+from unittest.mock import Mock, patch
+
+from dispatch.ai.service import generate_read_in_summary, READ_IN_SUMMARY_CACHE_DURATION
+from dispatch.ai.models import ReadInSummary, ReadInSummaryResponse
+from dispatch.ai.enums import AIEventSource, AIEventDescription
+from dispatch.plugins.dispatch_slack.models import IncidentSubjects, CaseSubjects
+from dispatch.enums import EventType
+from dispatch.types import Subject
+
+
+class TestGenerateReadInSummary:
+    """Test suite for generate_read_in_summary function."""
+
+    @pytest.fixture
+    def mock_subject(self):
+        """Create a mock subject for testing."""
+        subject = Mock(spec=Subject)
+        subject.id = 123
+        subject.name = "Test Incident"
+        subject.type = IncidentSubjects.incident
+        return subject
+
+    @pytest.fixture
+    def mock_project(self):
+        """Create a mock project for testing."""
+        project = Mock()
+        project.id = 456
+        return project
+
+    @pytest.fixture
+    def mock_conversation(self):
+        """Create mock conversation data."""
+        return [
+            {"user": "user1", "text": "Alert received", "timestamp": "2024-01-01 10:00"},
+            {"user": "user2", "text": "Investigating the issue", "timestamp": "2024-01-01 10:05"},
+            {"user": "user1", "text": "Issue resolved", "timestamp": "2024-01-01 10:30"},
+        ]
+
+    @pytest.fixture
+    def mock_read_in_summary(self):
+        """Create a mock ReadInSummary object."""
+        return ReadInSummary(
+            timeline=["2024-01-01 10:00: Alert received", "2024-01-01 10:30: Issue resolved"],
+            actions_taken=["Investigated the alert", "Applied fix"],
+            current_status="Resolved",
+            summary="Security alert was investigated and resolved successfully",
+        )
+
+    def test_generate_read_in_summary_success_incident(
+        self, session, mock_subject, mock_project, mock_conversation, mock_read_in_summary
+    ):
+        """Test successful read-in summary generation for an incident."""
+        with (
+            patch("dispatch.ai.service.event_service.get_recent_summary_event") as mock_get_event,
+            patch("dispatch.ai.service.plugin_service.get_active_instance") as mock_get_plugin,
+            patch("dispatch.ai.service.event_service.log_incident_event") as mock_log_event,
+        ):
+
+            # Mock no recent event (cache miss)
+            mock_get_event.return_value = None
+
+            # Mock AI plugin
+            mock_ai_plugin = Mock()
+            mock_ai_plugin.instance.configuration.chat_completion_model = "gpt-4"
+            mock_ai_plugin.instance.chat_parse.return_value = mock_read_in_summary
+
+            # Mock conversation plugin
+            mock_conv_plugin = Mock()
+            mock_conv_plugin.instance.get_conversation.return_value = mock_conversation
+
+            # Configure plugin service to return our mocks
+            def get_plugin_side_effect(db_session, plugin_type, project_id):
+                if plugin_type == "artificial-intelligence":
+                    return mock_ai_plugin
+                elif plugin_type == "conversation":
+                    return mock_conv_plugin
+                return None
+
+            mock_get_plugin.side_effect = get_plugin_side_effect
+
+            # Call the function
+            result = generate_read_in_summary(
+                db_session=session,
+                subject=mock_subject,
+                project=mock_project,
+                channel_id="test-channel",
+                important_reaction=":white_check_mark:",
+                participant_email="test@example.com",
+            )
+
+            # Assertions
+            assert isinstance(result, ReadInSummaryResponse)
+            assert result.summary is not None
+            assert result.error_message is None
+            assert result.summary.timeline == mock_read_in_summary.timeline
+            assert result.summary.actions_taken == mock_read_in_summary.actions_taken
+            assert result.summary.current_status == mock_read_in_summary.current_status
+            assert result.summary.summary == mock_read_in_summary.summary
+
+            # Verify event logging
+            mock_log_event.assert_called_once_with(
+                db_session=session,
+                source=AIEventSource.dispatch_genai,
+                description=AIEventDescription.read_in_summary_created.format(
+                    participant_email="test@example.com"
+                ),
+                incident_id=mock_subject.id,
+                details=mock_read_in_summary.dict(),
+                type=EventType.other,
+            )
+
+    def test_generate_read_in_summary_success_case(
+        self, session, mock_subject, mock_project, mock_conversation, mock_read_in_summary
+    ):
+        """Test successful read-in summary generation for a case."""
+        # Change subject type to case
+        mock_subject.type = CaseSubjects.case
+
+        with (
+            patch("dispatch.ai.service.event_service.get_recent_summary_event") as mock_get_event,
+            patch("dispatch.ai.service.plugin_service.get_active_instance") as mock_get_plugin,
+            patch("dispatch.ai.service.event_service.log_case_event") as mock_log_event,
+        ):
+
+            # Mock no recent event (cache miss)
+            mock_get_event.return_value = None
+
+            # Mock AI plugin
+            mock_ai_plugin = Mock()
+            mock_ai_plugin.instance.configuration.chat_completion_model = "gpt-4"
+            mock_ai_plugin.instance.chat_parse.return_value = mock_read_in_summary
+
+            # Mock conversation plugin
+            mock_conv_plugin = Mock()
+            mock_conv_plugin.instance.get_conversation.return_value = mock_conversation
+
+            # Configure plugin service to return our mocks
+            def get_plugin_side_effect(db_session, plugin_type, project_id):
+                if plugin_type == "artificial-intelligence":
+                    return mock_ai_plugin
+                elif plugin_type == "conversation":
+                    return mock_conv_plugin
+                return None
+
+            mock_get_plugin.side_effect = get_plugin_side_effect
+
+            # Call the function
+            result = generate_read_in_summary(
+                db_session=session,
+                subject=mock_subject,
+                project=mock_project,
+                channel_id="test-channel",
+                important_reaction=":white_check_mark:",
+                participant_email="test@example.com",
+            )
+
+            # Assertions
+            assert isinstance(result, ReadInSummaryResponse)
+            assert result.summary is not None
+            assert result.error_message is None
+
+            # Verify case event logging
+            mock_log_event.assert_called_once_with(
+                db_session=session,
+                source=AIEventSource.dispatch_genai,
+                description=AIEventDescription.read_in_summary_created.format(
+                    participant_email="test@example.com"
+                ),
+                case_id=mock_subject.id,
+                details=mock_read_in_summary.dict(),
+                type=EventType.other,
+            )
+
+    def test_generate_read_in_summary_cache_hit(
+        self, session, mock_subject, mock_project, mock_read_in_summary
+    ):
+        """Test read-in summary generation with cache hit."""
+        with (
+            patch("dispatch.ai.service.event_service.get_recent_summary_event") as mock_get_event,
+            patch("dispatch.ai.service.plugin_service.get_active_instance") as mock_get_plugin,
+        ):
+
+            # Mock recent event (cache hit)
+            mock_event = Mock()
+            mock_event.id = 789
+            mock_event.details = mock_read_in_summary.dict()
+            mock_get_event.return_value = mock_event
+
+            # Call the function
+            result = generate_read_in_summary(
+                db_session=session,
+                subject=mock_subject,
+                project=mock_project,
+                channel_id="test-channel",
+                important_reaction=":white_check_mark:",
+                participant_email="test@example.com",
+            )
+
+            # Assertions
+            assert isinstance(result, ReadInSummaryResponse)
+            assert result.summary is not None
+            assert result.error_message is None
+            assert result.summary.timeline == mock_read_in_summary.timeline
+
+            # Verify no plugins were called (cache hit)
+            mock_get_plugin.assert_not_called()
+
+    def test_generate_read_in_summary_cache_invalid_data(
+        self, session, mock_subject, mock_project, mock_conversation, mock_read_in_summary
+    ):
+        """Test read-in summary generation with invalid cached data."""
+        with (
+            patch("dispatch.ai.service.event_service.get_recent_summary_event") as mock_get_event,
+            patch("dispatch.ai.service.plugin_service.get_active_instance") as mock_get_plugin,
+            patch("dispatch.ai.service.log") as mock_log,
+        ):
+
+            # Mock recent event with invalid data
+            mock_event = Mock()
+            mock_event.id = 789
+            mock_event.details = {"timeline": "not a list"}  # This will cause validation error
+            mock_get_event.return_value = mock_event
+
+            # Mock AI plugin
+            mock_ai_plugin = Mock()
+            mock_ai_plugin.instance.configuration.chat_completion_model = "gpt-4"
+            mock_ai_plugin.instance.chat_parse.return_value = mock_read_in_summary
+
+            # Mock conversation plugin
+            mock_conv_plugin = Mock()
+            mock_conv_plugin.instance.get_conversation.return_value = mock_conversation
+
+            # Configure plugin service to return our mocks
+            def get_plugin_side_effect(db_session, plugin_type, project_id):
+                if plugin_type == "artificial-intelligence":
+                    return mock_ai_plugin
+                elif plugin_type == "conversation":
+                    return mock_conv_plugin
+                return None
+
+            mock_get_plugin.side_effect = get_plugin_side_effect
+
+            # Call the function
+            generate_read_in_summary(
+                db_session=session,
+                subject=mock_subject,
+                project=mock_project,
+                channel_id="test-channel",
+                important_reaction=":white_check_mark:",
+                participant_email="test@example.com",
+            )
+
+            # Verify warning was logged
+            assert mock_log.warning.called
+
+    def test_generate_read_in_summary_no_ai_plugin(self, session, mock_subject, mock_project):
+        """Test read-in summary generation when no AI plugin is available."""
+        with (
+            patch("dispatch.ai.service.event_service.get_recent_summary_event") as mock_get_event,
+            patch("dispatch.ai.service.plugin_service.get_active_instance") as mock_get_plugin,
+            patch("dispatch.ai.service.log") as mock_log,
+        ):
+
+            # Mock no recent event
+            mock_get_event.return_value = None
+
+            # Mock no AI plugin
+            mock_get_plugin.return_value = None
+
+            # Call the function
+            result = generate_read_in_summary(
+                db_session=session,
+                subject=mock_subject,
+                project=mock_project,
+                channel_id="test-channel",
+                important_reaction=":white_check_mark:",
+                participant_email="test@example.com",
+            )
+
+            # Assertions
+            assert isinstance(result, ReadInSummaryResponse)
+            assert result.summary is None
+            assert result.error_message is not None
+            assert "No artificial-intelligence plugin enabled" in result.error_message
+
+            # Verify warning was logged
+            mock_log.warning.assert_called()
+
+    def test_generate_read_in_summary_no_conversation_plugin(
+        self, session, mock_subject, mock_project
+    ):
+        """Test read-in summary generation when no conversation plugin is available."""
+        with (
+            patch("dispatch.ai.service.event_service.get_recent_summary_event") as mock_get_event,
+            patch("dispatch.ai.service.plugin_service.get_active_instance") as mock_get_plugin,
+            patch("dispatch.ai.service.log") as mock_log,
+        ):
+
+            # Mock no recent event
+            mock_get_event.return_value = None
+
+            # Mock AI plugin but no conversation plugin
+            mock_ai_plugin = Mock()
+            mock_get_plugin.side_effect = lambda db_session, plugin_type, project_id: (
+                mock_ai_plugin if plugin_type == "artificial-intelligence" else None
+            )
+
+            # Call the function
+            result = generate_read_in_summary(
+                db_session=session,
+                subject=mock_subject,
+                project=mock_project,
+                channel_id="test-channel",
+                important_reaction=":white_check_mark:",
+                participant_email="test@example.com",
+            )
+
+            # Assertions
+            assert isinstance(result, ReadInSummaryResponse)
+            assert result.summary is None
+            assert result.error_message is not None
+            assert "No conversation plugin enabled" in result.error_message
+
+            # Verify warning was logged
+            mock_log.warning.assert_called()
+
+    def test_generate_read_in_summary_no_conversation(self, session, mock_subject, mock_project):
+        """Test read-in summary generation when no conversation is found."""
+        with (
+            patch("dispatch.ai.service.event_service.get_recent_summary_event") as mock_get_event,
+            patch("dispatch.ai.service.plugin_service.get_active_instance") as mock_get_plugin,
+            patch("dispatch.ai.service.log") as mock_log,
+        ):
+
+            # Mock no recent event
+            mock_get_event.return_value = None
+
+            # Mock AI plugin
+            mock_ai_plugin = Mock()
+            mock_ai_plugin.instance.configuration.chat_completion_model = "gpt-4"
+
+            # Mock conversation plugin that returns no conversation
+            mock_conv_plugin = Mock()
+            mock_conv_plugin.instance.get_conversation.return_value = None
+
+            # Configure plugin service to return our mocks
+            def get_plugin_side_effect(db_session, plugin_type, project_id):
+                if plugin_type == "artificial-intelligence":
+                    return mock_ai_plugin
+                elif plugin_type == "conversation":
+                    return mock_conv_plugin
+                return None
+
+            mock_get_plugin.side_effect = get_plugin_side_effect
+
+            # Call the function
+            result = generate_read_in_summary(
+                db_session=session,
+                subject=mock_subject,
+                project=mock_project,
+                channel_id="test-channel",
+                important_reaction=":white_check_mark:",
+                participant_email="test@example.com",
+            )
+
+            # Assertions
+            assert isinstance(result, ReadInSummaryResponse)
+            assert result.summary is None
+            assert result.error_message is not None
+            assert "No conversation found" in result.error_message
+
+            # Verify warning was logged
+            mock_log.warning.assert_called()
+
+    def test_generate_read_in_summary_ai_error(
+        self, session, mock_subject, mock_project, mock_conversation
+    ):
+        """Test read-in summary generation when AI plugin throws an error."""
+        with (
+            patch("dispatch.ai.service.event_service.get_recent_summary_event") as mock_get_event,
+            patch("dispatch.ai.service.plugin_service.get_active_instance") as mock_get_plugin,
+            patch("dispatch.ai.service.log") as mock_log,
+        ):
+
+            # Mock no recent event
+            mock_get_event.return_value = None
+
+            # Mock AI plugin that throws an error
+            mock_ai_plugin = Mock()
+            mock_ai_plugin.instance.configuration.chat_completion_model = "gpt-4"
+            mock_ai_plugin.instance.chat_parse.side_effect = Exception("AI service error")
+
+            # Mock conversation plugin
+            mock_conv_plugin = Mock()
+            mock_conv_plugin.instance.get_conversation.return_value = mock_conversation
+
+            # Configure plugin service to return our mocks
+            def get_plugin_side_effect(db_session, plugin_type, project_id):
+                if plugin_type == "artificial-intelligence":
+                    return mock_ai_plugin
+                elif plugin_type == "conversation":
+                    return mock_conv_plugin
+                return None
+
+            mock_get_plugin.side_effect = get_plugin_side_effect
+
+            # Call the function
+            result = generate_read_in_summary(
+                db_session=session,
+                subject=mock_subject,
+                project=mock_project,
+                channel_id="test-channel",
+                important_reaction=":white_check_mark:",
+                participant_email="test@example.com",
+            )
+
+            # Assertions
+            assert isinstance(result, ReadInSummaryResponse)
+            assert result.summary is None
+            assert result.error_message is not None
+            assert "Error generating read-in summary" in result.error_message
+
+            # Verify error was logged
+            mock_log.exception.assert_called()
+
+    def test_generate_read_in_summary_cache_duration_constant(self):
+        """Test that the cache duration constant is set correctly."""
+        assert READ_IN_SUMMARY_CACHE_DURATION == 120  # 2 minutes
+
+    def test_generate_read_in_summary_event_query_incident(
+        self, session, mock_subject, mock_project
+    ):
+        """Test that the correct event query is made for incidents."""
+        with patch("dispatch.ai.service.event_service.get_recent_summary_event") as mock_get_event:
+            mock_get_event.return_value = None
+
+            # Call the function
+            generate_read_in_summary(
+                db_session=session,
+                subject=mock_subject,
+                project=mock_project,
+                channel_id="test-channel",
+                important_reaction=":white_check_mark:",
+                participant_email="test@example.com",
+            )
+
+            # Verify the correct query was made
+            mock_get_event.assert_called_once_with(
+                session, incident_id=mock_subject.id, max_age_seconds=READ_IN_SUMMARY_CACHE_DURATION
+            )
+
+    def test_generate_read_in_summary_event_query_case(self, session, mock_subject, mock_project):
+        """Test that the correct event query is made for cases."""
+        # Change subject type to case
+        mock_subject.type = CaseSubjects.case
+
+        with patch("dispatch.ai.service.event_service.get_recent_summary_event") as mock_get_event:
+            mock_get_event.return_value = None
+
+            # Call the function
+            generate_read_in_summary(
+                db_session=session,
+                subject=mock_subject,
+                project=mock_project,
+                channel_id="test-channel",
+                important_reaction=":white_check_mark:",
+                participant_email="test@example.com",
+            )
+
+            # Verify the correct query was made
+            mock_get_event.assert_called_once_with(
+                session, case_id=mock_subject.id, max_age_seconds=READ_IN_SUMMARY_CACHE_DURATION
+            )