Go High Level AI Chatbot & Conversation AI Guide | GHLCRM.US

GoHighLevel Conversation AI is an AI chatbot and messaging system designed to communicate with leads and customers across supported channels, answer questions, collect contact information, qualify prospects, book appointments, trigger workflows, and hand conversations to human team members when needed.

The strongest implementation is not simply:

Message → AI Reply

It is:

Message → Understand Intent → Answer → Collect Data → Qualify → Take Action → Book/Route → Update CRM → Human Handoff When Needed

That difference turns a basic chatbot into a practical customer-conversation system.

For a dedicated overview of the feature and its broader capabilities, see our GoHighLevel Conversation AI guide.

Conversation AI is HighLevel's customer-facing AI communication product.

It can respond to incoming conversations and guide contacts toward defined business outcomes.

Common use cases include:

The bot works with information and instructions configured by the business.

The basic model is:

Customer Question

↓

Conversation AI

↓

Knowledge + Instructions + Conversation Context

↓

Response

↓

Business Action

HighLevel has several AI products, and their roles should not be confused.

Communicates with:

Leads and Customers

Helps:

HighLevel Users and Operators

Ask AI is an internal copilot for supported HighLevel tasks.

Conversation AI is designed for external customer conversations.

Think:

Ask AI → Employee Assistant

Conversation AI → Customer-Facing Agent

Conversation AI primarily handles supported messaging conversations.

Voice AI handles AI-powered telephone and voice interactions.

A business could therefore use:

Conversation AI → Messaging

and:

Voice AI → Calls

The products can support related customer journeys, but they should not be treated as the same feature.

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Conversation AI currently supports multiple communication channels at the broader bot level.

These include:

Availability depends on account configuration, connected channels, bot type, and the specific Conversation AI feature being used.

Not every Conversation AI action supports every channel identically.

Without a centralized AI layer, a business may need separate chatbot systems for:

Website

SMS

Facebook

Instagram

WhatsApp

A supported HighLevel setup can instead centralize more of that activity around:

Conversation AI + Conversations + CRM

The goal is not simply convenience.

It is customer context.

A lead who begins a conversation should remain connected with the appropriate CRM record and business process.

HighLevel currently provides three major setup approaches.

Designed for users who want a simpler configuration process.

Provides greater control over the AI instructions, models, goals, and actions.

Uses a visual builder for structured multi-step conversation paths.

Choose the builder based on the complexity of the customer journey.

A Guided Form setup is useful when the desired conversation is relatively straightforward.

For example:

Welcome

↓

Identify Need

↓

Collect Details

↓

Book Appointment

This approach can reduce the complexity of writing a sophisticated prompt manually.

It is a useful starting point for businesses deploying their first AI chatbot.

Prompt-Based bots provide greater control.

The business can define areas such as:

This approach is useful when the AI needs more flexibility.

For example:

Customer asks unpredictable questions

while the bot still needs to:

Answer → Qualify → Route

Prompt quality becomes especially important.

Flow Builder is designed for more structured conversational journeys.

Instead of relying primarily on one broad prompt, the business can create a visual sequence.

For example:

Start

↓

Identify Service

↓

Service A? → Path A

Service B? → Path B

↓

Collect Details

↓

Qualification

↓

Calendar

↓

Human Handoff

This provides more control over complex conversations.

These are related but different.

Controls a structured conversation inside Conversation AI.

Allows a HighLevel Workflow to:

Send AI Message → Wait for Reply → Evaluate Response → Branch

Use Flow Builder when the chatbot itself needs a structured conversation.

Use the workflow action when AI conversation should be one component inside a broader automation.

Conversation AI supports Suggestive mode.

In this mode, AI helps prepare responses rather than automatically sending every response directly to the contact.

The process is closer to:

Customer Message

↓

AI Suggests Reply

↓

Human Reviews

↓

Human Sends

This is useful when human oversight is important.

Consider Suggestive mode for:

It allows AI to increase response speed while keeping a person involved.

Auto-Pilot allows Conversation AI to respond automatically.

The model becomes:

Customer Message

↓

AI Understands Context

↓

AI Responds

↓

Conversation Continues

↓

Goal Reached / Handoff / Bot Sleeps

Auto-Pilot is useful for repetitive and well-defined conversations.

Businesses should still define boundaries.

For example, the bot should understand:

The objective is controlled automation.

Current Auto-Pilot configuration allows the business to control the maximum number of AI-generated messages the bot can send in a conversation.

The current configurable range is:

1–100 Messages

New bots currently default to:

100 Messages

When the configured limit is reached, the bot goes to sleep.

A simple FAQ bot probably does not need a 100-message conversation.

For example:

Question

↓

Answer

↓

Follow-Up

↓

Resolution

A lower limit can reduce unnecessary AI usage and prevent unproductive conversations.

More complex qualification or troubleshooting journeys may require a higher limit.

Monitor actual transcripts before deciding.

Conversation AI can enter a sleeping or inactive state.

This can happen because of conditions such as:

Incoming customer messages can still be received while the bot is sleeping.

The AI simply does not continue responding until it is reactivated according to the configured process.

A team member can reactivate an appropriate sleeping bot from the Conversations environment.

This is useful when a human temporarily took over but wants automation to resume later.

HighLevel also provides:

Update Conversation AI Bot and Status

as a workflow action.

This allows automation to:

for an individual contact.

For example:

Payment Received

↓

Assign Customer Support Bot

↓

Set Bot Active

This makes Conversation AI responsive to the customer's lifecycle.

Conversation AI can also be controlled at the individual contact level.

One customer can have AI:

Active

while another is:

Inactive

or:

Sleeping

This is important for human handoff.

Turning AI off for one complex customer does not require disabling the chatbot for every other lead.

A good chatbot needs an exit path.

Conversation AI can be configured so certain conversations move toward a human.

Examples include:

Customer Requests Human

Bot Cannot Resolve Question

High-Value Lead

Complex Situation

Special Case

The flow can become:

AI Conversation

↓

Handoff Condition

↓

Bot Sleeps

↓

Team Notification

↓

Human Continues

This prevents automation from becoming a barrier to service.

One risk in automated messaging is:

Human Sends Reply

and:

AI Sends Another Reply

almost simultaneously.

HighLevel includes bot-status and sleep controls designed to reduce this overlap.

Configure human handoff behavior intentionally.

A customer should feel like they are having one coherent conversation.

Conversation AI bots can be configured around specific goals.

A goal defines what the bot should accomplish.

Common goals include:

Do not give one chatbot every possible responsibility unless the use case genuinely requires it.

For a local service business, the goal might be:

Book Qualified Appointments

For SaaS:

Answer Product Questions + Book Demo

For a course business:

Answer Enrollment Questions

For support:

Resolve Common Questions or Escalate

A focused bot is easier to test.

Conversation AI can use HighLevel's Knowledge Base functionality to ground responses in business information.

The knowledge layer can contain relevant information about:

The principle is:

Better Knowledge → Better Grounded Answers

But only when the source material itself is accurate.

If the Knowledge Base says:

Service Price = $100

but the real price is now:

$150

the chatbot may provide outdated information.

AI does not automatically fix inaccurate business data.

Create a maintenance process:

Update Business Information

↓

Update Knowledge

↓

Test Bot

Knowledge should be:

Avoid adding:

Treat chatbot knowledge as customer-facing content.

One of the strongest Conversation AI use cases is answering repetitive questions.

Examples:

What are your hours?

Where are you located?

Do you offer this service?

How does pricing work?

How do I book?

Automating common questions can reduce repetitive staff work.

Conversation AI can collect information during a conversation.

For example:

AI: What service are you interested in?

↓

Lead Responds

↓

AI: What is your name?

↓

AI: What is the best email?

↓

Contact Updated

This is more conversational than presenting a large form immediately.

Current Conversation AI Bot Goals can collect missing contact information and save it into selected CRM fields.

This can include information such as:

Use only fields relevant to the conversation.

Do not interrogate the customer.

Lead qualification is where Conversation AI becomes more useful than a simple FAQ bot.

Suppose a roofing company wants to know:

Property Type

Service Needed

Location

Timeline

The AI can ask appropriate questions.

Then the business can use the responses to determine the next action.

A simplified journey:

Lead: I need a new roof.

↓

AI: Is this for a residential or commercial property?

↓

Residential

↓

AI: Which area is the property located in?

↓

Service Area Confirmed

↓

AI: Would you like to schedule an inspection?

↓

Calendar

The AI moves the conversation toward an outcome.

A chatbot should not turn into a 20-question survey unless necessary.

Every additional question adds friction.

Ask:

Do We Need This Information Before the Next Step?

If not, collect it later.

Appointment booking is one of Conversation AI's major use cases.

The AI can:

The journey becomes:

Conversation

↓

Intent Detected

↓

Qualification

↓

Available Times

↓

Customer Selects

↓

Appointment Booked

This removes the need for the lead to leave the conversation and manually navigate several pages.

Avoid instructions such as:

“Tell everyone Tuesday at 3 PM is available.”

Calendar availability changes.

Use the appropriate booking functionality so the bot works with current availability rather than static text.

Conversation AI can support multiple-calendar booking configurations.

This is useful when a business has different:

For example:

Customer Intent

↓

Sales Demo → Sales Calendar

Support Session → Support Calendar

Consultation → Consultant Calendar

Intent-based routing can send the contact toward the appropriate calendar.

Current Conversation AI booking functionality can also work with supported rescheduling and cancellation experiences.

This helps the bot support more of the appointment lifecycle.

Instead of:

AI Only Books

the broader experience can include:

Book → Reschedule → Cancel → Follow-Up

according to configuration.

Conversation AI can trigger a HighLevel Workflow when a defined conversational condition occurs.

For example:

Customer Says They Want Pricing

↓

Conversation AI Detects Condition

↓

Trigger Workflow

↓

Apply Tag

↓

Notify Sales

↓

Update Opportunity

This connects conversation intelligence with operational automation.

Suppose the bot asks:

“Would you like to schedule a consultation?”

The customer replies:

“Yes.”

That intent can trigger the configured process.

For example:

Qualified Interest

↓

Workflow

↓

Sales Notification

↓

Pipeline Update

↓

Booking Process

This is much more useful than merely generating another AI response.

A workflow can accidentally execute twice if:

and:

Review automation architecture carefully.

Duplicate workflows can cause:

One event should have a clearly defined automation path.

HighLevel also provides a Conversation AI action inside Workflow Builder.

This allows the workflow to:

Send AI-Generated Message

↓

Wait for Contact Reply

↓

Evaluate Response

↓

Choose Branch

This is useful when AI needs to participate inside an existing automation sequence.

A workflow could become:

Old Lead

↓

Conversation AI: Are you still interested in a consultation?

↓

Wait for Reply

↓

Yes → Booking Path

No → Close/Nurture Path

No Response → Follow-Up Path

This creates more flexible automation than a fixed yes/no button.

Do not assume the Conversation AI Workflow Action supports every channel available to Conversation AI generally.

Current workflow-action documentation lists selectable channels including:

Broader bot-level channel availability does not automatically guarantee identical support inside every workflow action.

This distinction matters during implementation.

Conversation AI can generate conversation summaries.

Instead of requiring a salesperson to read a long thread, the system can create a condensed overview after the configured conditions are met.

A summary can help answer:

Who Is This Lead?

What Do They Want?

What Happened?

What Should We Know?

This is useful during human handoff.

HighLevel can also work with conversation transcripts.

A transcript provides the fuller interaction history.

Summaries and transcripts can be sent into workflows for further CRM actions.

For example:

Conversation Ends

↓

Generate Summary

↓

Workflow

↓

Add Summary to Contact Note

↓

Notify Assigned User

The salesperson begins with context.

Businesses can configure an inactivity period before the system considers the conversation ready for summary generation.

This prevents a summary from being generated while the customer is still actively messaging.

A minimum-message requirement can also prevent unnecessary summaries for extremely short interactions.

Conversation summaries can be emailed to relevant users.

Potential recipients include:

This can make human follow-up faster.

Conversation AI can also support follow-up behavior.

Suppose the bot asks:

“Would you like to book?”

The lead disappears.

Instead of abandoning the conversation immediately, an appropriate follow-up process can attempt to re-engage the lead.

Use follow-up carefully.

Too many automated messages can damage the customer experience.

Conversation AI capabilities have expanded beyond plain text in supported scenarios.

Where supported, AI can process customer-provided media such as:

This can be useful when customer context arrives in a richer format.

For example, a contact might send an image relevant to a service inquiry.

The bot can use supported AI processing to better understand the interaction.

AI interpretation should not automatically be treated as a professional determination.

For example, an image can provide context.

It should not automatically replace a qualified professional when the situation requires expert judgment.

Use multimedia understanding to support routing and conversation—not to make unsupported high-stakes determinations.

A local service business can use Conversation AI for:

Website Visitor

↓

Service Question

↓

Qualification

↓

Contact Information

↓

Appointment

↓

CRM

For example, an HVAC company might ask:

What service do you need?

Which area are you in?

Would you like an appointment?

The bot can then route the lead appropriately.

An agency can use Conversation AI for:

Example:

Agency Website

↓

Lead Asks About SEO

↓

AI Answers

↓

Collects Company Information

↓

Qualifies Budget/Need

↓

Books Strategy Call

↓

Creates/Updates Opportunity

This reduces manual pre-sales work.

A SaaS company might use:

Product Question

↓

AI Answer

↓

Use-Case Qualification

↓

Demo Booking

or:

Existing Customer Question

↓

AI Answers Common Issue

↓

Unresolved?

↓

Human Handoff

Separate sales and support goals when appropriate.

A real estate business could use Conversation AI to gather information such as:

Then:

Qualified Lead → Appropriate Agent / Calendar

The AI should collect useful information without pretending to replace licensed professional advice where such expertise is required.

Businesses built around appointments can create a very direct journey:

Question

↓

Answer

↓

Qualification

↓

Calendar

↓

Appointment

↓

Reminder Workflow

This is one of the clearest use cases for Conversation AI.

A support bot can answer repetitive questions from an approved Knowledge Base.

For example:

How do I access my account?

Where can I find my invoice?

What are your business hours?

If the question exceeds the bot's scope:

Escalate → Human

A good support chatbot knows when to stop.

Do not force one bot to perform every function.

A business may use different bots for different contexts.

Goal:

Qualify + Book

Goal:

Resolve + Escalate

Goal:

Guide Existing Customers

HighLevel workflows can dynamically assign bots to contacts based on relevant conditions.

The:

Update Conversation AI Bot and Status

workflow action can assign different bots to contacts.

For example:

New Lead

↓

Sales Bot

After purchase:

Payment Received

↓

Customer Bot

If support issue:

Support Bot

This allows AI behavior to change with the customer lifecycle.

A mature architecture can become:

Visitor

↓

Lead Bot

↓

Qualified

↓

Sales Bot

↓

Customer

↓

Customer Success Bot

↓

Support Need

↓

Support Bot / Human

The CRM determines who the contact is.

The appropriate bot handles the relevant conversation.

HighLevel currently offers Conversation AI through its broader AI pricing structure.

The major options include:

No monthly AI subscription.

Supported usage is charged according to applicable token or usage pricing.

$50/month per enabled location

Current Conversation AI allowance:

1,000 Agent Responses per Month

Additional applicable usage can create overage charges.

$97/month per enabled location

Conversation AI is currently included on an unlimited basis subject to applicable fair-use terms.

Pricing and allowances can change, so verify current HighLevel pricing before making long-term cost assumptions.

Suppose one location runs a relatively low-volume chatbot.

Growth may provide sufficient included Conversation AI responses.

A location with substantial:

may find the Unlimited structure easier to forecast.

Do not choose only from the headline price.

Estimate actual conversation volume.

Growth and Unlimited AI Employee pricing currently applies per enabled location.

That distinction matters for agencies.

For example:

1 Location × $97

is different from:

50 Locations × $97

if every location independently enables the same plan.

Model costs before deploying AI across a large client base.

Eligible agencies can also use HighLevel's AI rebilling functionality.

Current documentation requires the appropriate Agency Pro-level setup for AI Employee rebilling.

HighLevel has also expanded AI rebilling with fixed-rate options.

An agency can potentially charge sub-accounts using predictable per-unit pricing for supported AI products.

For Conversation AI, an agency could configure an appropriate fixed charge per message rather than relying only on percentage markup models.

Before reselling Conversation AI, calculate:

Underlying AI Cost

Support

Setup

Monitoring

Agency Margin

The client is not simply paying for tokens.

They may also be paying for:

Price the complete service appropriately.

A practical design process begins with five questions.

Examples:

Examples:

Build the appropriate Knowledge Base.

Examples:

Define human-handoff and sleep conditions.

This creates controlled AI behavior.

A strong bot prompt should define areas such as:

Role

Goal

Tone

Business Context

Allowed Actions

Boundaries

Handoff Conditions

Avoid vague instructions such as:

“Be helpful and sell.”

More specific instructions usually create more predictable behavior.

An enormous prompt is not automatically better.

Use the appropriate HighLevel tools for the appropriate job.

For example:

Knowledge Base → Facts

Bot Goals → Actions

Workflow → Automation

Prompt → Behavior

Do not attempt to place the entire business infrastructure into one instruction field.

Before launch, create a test set.

For example:

Review every response.

Also test:

Real customers rarely follow the ideal script.

A chatbot can sound excellent while the automation behind it is broken.

Verify:

Did Contact Field Update?

Did Calendar Book?

Did Workflow Trigger?

Did Opportunity Move?

Did Human Receive Notification?

Did Bot Stop When Required?

Operational testing matters as much as conversational quality.

Do not launch and forget.

Review actual conversations regularly.

Look for:

Then improve the bot.

Useful metrics can include:

Conversations Started

Qualified Leads

Appointments Booked

Human Handoffs

Resolved Conversations

Average AI Messages

Conversion Rate

Cost per Qualified Lead

Do not evaluate the bot only by how many messages it sends.

A simple metric is:

Appointments Booked ÷ Qualified Conversations × 100

Suppose:

200 Qualified Conversations

produce:

50 Appointments

Conversion rate:

25%

Track changes after improving prompts, knowledge, qualification, or booking flow.

Calculate:

Human Handoffs ÷ AI Conversations × 100

A high handoff rate is not automatically bad.

For complex or high-value conversations, human involvement may be desirable.

The question is:

Did the bot hand off at the right time?

For support use cases, teams can also estimate how many eligible conversations are resolved without human involvement.

But do not optimize containment at the expense of customer experience.

A bot that refuses to escalate can produce a high containment rate and terrible service.

The AI needs current information.

Every bot needs boundaries.

Reduce unnecessary friction.

Separate use cases where appropriate.

Use live booking functionality.

Avoid firing the same process twice.

Configure Auto-Pilot intentionally.

Real conversations reveal weaknesses.

Track bookings, qualified leads, resolutions, and revenue-related outcomes.

Maintain human oversight.

A practical implementation sequence is:

1. Define Use Case

↓

2. Define Primary Goal

↓

3. Select Bot Type

↓

4. Configure Channels

↓

5. Build Knowledge Base

↓

6. Configure Personality and Instructions

↓

7. Configure Goals

↓

8. Add Contact Information Collection

↓

9. Configure Booking if Needed

↓

10. Connect Workflows

↓

11. Configure Human Handoff

↓

12. Configure Message Limits

↓

13. Test Conversations

↓

14. Test Actions

↓

15. Launch on Limited Traffic

↓

16. Review Transcripts

↓

17. Optimize

Do not launch across every channel before validating the bot.

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GoHighLevel Conversation AI is a customer-facing AI messaging system that can answer questions, collect lead information, qualify contacts, book appointments, trigger workflows, and support human handoff across supported communication channels.

Conversation AI powers HighLevel's more advanced AI chatbot capabilities. It extends beyond basic scripted chat by using AI, account knowledge, goals, actions, and conversation context.

At the broader bot level, current HighLevel documentation lists SMS, Email, Facebook, Instagram, WhatsApp, Chat Widget/SMS Chat, and Live Chat, with channel availability depending on configuration and feature context.

Yes. Auto-Pilot allows the bot to respond automatically and work toward configured conversational goals.

Yes. Suggestive mode allows AI to prepare responses that a human can review and send.

Current Auto-Pilot settings allow a maximum message limit between 1 and 100 AI-generated messages per conversation, with new bots currently defaulting to 100.

The bot goes to sleep after reaching the configured limit. It can later be reactivated manually or through supported workflow automation.

Yes. HighLevel provides contact-level bot controls so AI can be active, inactive, or temporarily sleeping for an individual conversation without disabling the bot globally.

Yes. Conversation AI can collect required information, work with configured calendar availability, offer times, and book appointments.

Yes. Current appointment-booking functionality can support multiple-calendar configurations and route contacts according to relevant intent and setup.

Yes, in supported booking configurations. Current HighLevel functionality includes broader appointment-management capabilities such as booking, rescheduling, cancellation, and post-booking automation.

Yes. Bot Goals can collect missing information and save supported values into selected contact fields.

Yes. A bot can trigger a selected HighLevel Workflow when configured conversational conditions are met.

Yes. The Conversation AI Workflow Action can send an AI-generated message, wait for the contact's reply, evaluate the response, and route the workflow through configured branches.

No. Flow Builder structures a bot's conversational journey, while the Conversation AI Workflow Action inserts AI-powered conversation and reply evaluation into a broader HighLevel Workflow.

Yes. Businesses can configure bot behavior so appropriate conversations stop or pause AI responses and move toward human assistance.

Yes. Conversation AI can generate summaries and transcripts according to configured conditions and pass that information into workflows or notifications.

Yes. A workflow can receive Conversation AI summary and transcript data and use it in actions such as adding a contact note.

Yes. Current settings can send summaries to selected recipients such as admins, users, assigned users, specific users, or a configured email recipient.

Conversation AI supports multimedia processing in supported scenarios, including relevant audio and image inputs. Businesses should still use human review for situations requiring professional or high-stakes judgment.

Conversation AI communicates with customers and leads, while Ask AI is primarily a copilot for HighLevel users operating the platform.

Conversation AI primarily handles supported messaging conversations, while Voice AI handles AI-powered voice and telephone interactions.

Current HighLevel AI pricing provides Pay-Per-Use, AI Employee Growth at $50 per enabled location per month, and AI Employee Unlimited at $97 per enabled location per month. Growth currently includes 1,000 Conversation AI agent responses monthly, while Unlimited provides Conversation AI usage subject to applicable fair-use terms.

No. Growth and Unlimited pricing applies per enabled location, so agencies should calculate deployment costs across the number of locations using the plan.

Yes, in eligible agency configurations. HighLevel supports AI rebilling and has also introduced fixed-rate rebilling options for Conversation AI.

Not completely. It can automate repetitive qualification, FAQs, follow-up, and booking, but complex, high-value, unusual, or sensitive conversations can still benefit from human judgment.

A focused FAQ, lead-qualification, or appointment-booking workflow is usually a practical starting point because its objective can be clearly defined, tested, and measured.

Go High Level Conversation AI becomes much more valuable when it is designed as a customer-journey system rather than simply a chatbot that produces AI-generated replies.

The basic version is:

Customer Message → AI Reply

The stronger architecture is:

Customer Message → Intent → Knowledge → Response → Qualification → Action → CRM → Booking/Workflow → Human Handoff

HighLevel currently gives businesses several ways to build that experience.

Guided Form configuration provides a simpler entry point.

Prompt-Based bots provide greater flexibility.

Flow Builder supports structured multi-step conversations.

Suggestive mode keeps humans involved in sending responses.

Auto-Pilot can handle appropriate conversations automatically.

Knowledge Base functionality helps ground answers in approved business information.

Bot Goals can collect contact data, book appointments, trigger workflows, and move conversations toward useful outcomes.

Multiple-calendar functionality can route different customer intents toward the appropriate booking destination.

Workflow integration allows conversation outcomes to update the broader business process.

Conversation summaries and transcripts can give human staff useful context.

Contact-level bot controls and human-handoff functionality help prevent AI from continuing when a person should take over.

And AI Employee pricing gives businesses a choice between usage-based billing, Growth, and Unlimited structures depending on conversation volume.

But the technology still requires careful design.

A successful chatbot needs:

Accurate Knowledge

Focused Goals

Clear Instructions

Appropriate Automation

Human Handoff

Testing

and:

Continuous Review

Do not measure success by how many messages the AI sends.

Measure whether Conversation AI helps the business:

Respond Faster

Capture More Qualified Leads

Book More Appropriate Appointments

Reduce Repetitive Work

Route Customers Correctly

and:

Create a Better Customer Experience

The best AI conversation is not necessarily the longest one.

It is the conversation that understands what the customer needs, gives an accurate response, takes the correct next action, and knows when a human should continue the relationship.

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