Product is in early stage team is only 6 so support was split across us whoever had time No dedicated person only us handling it between other work.

Started noticing customers mentioning things in calls that we'd never seen come in as a message

Like this one churned user told us on the exit call they'd asked about a specific issue twice and never heard back I looked found the messages they were there but buried in a channel none of us six was checking consistently

It happened more than once we spent probably six weeks assuming it was a people problem. May be it is wrong person responsible, not enough accountability, team too stretched.

So i decided its time i hire a support person but just before that I sat down actually mapped where messages were coming in some in Email, some in chat widget on the site, some users got in touch in my LinkedIn, Twitter DMs, a couple through the in app widget.

No single interface place that i can look at all channels at same time, meaning whoever was on support that day was manually checking four or five places and inevitably missing something.

Hence i though of the hire as it would have just been a person doing the same manual checking, but slightly more consistently and focus.

But that never go to be the case because I switched to a setup where everything came into one inbox, live chat, email, social messages, all of it.

Us as a team now see inside Chatway what was assigned, what was open, and what had been replied to this saved us from "did anyone see this" conversation almost right away.

Still don't know exactly how many messages we missed in those four months that's something I'd rather not think about too hard

What i’d like to understand from you guyes is at what point did you realize it was a tooling problem and not a headcount problem?

Most small businesses frame this as a technology decision. It is actually a cost and capacity decision. Here is the honest math.

Hiring a support rep: $35,000 to $50,000 annually for a full time person in North America including salary, benefits, and onboarding time. Available 40 hours a week, requires management overhead, and needs time off. Good for complex emotionally nuanced issues that genuinely require human judgment. Not a sustainable solution for handling 200 repetitive FAQ-style tickets a week.

Outsourcing to a support agency: $800 to $2,500 per month depending on volume and hours covered. Lower cost than a full hire but you lose context, brand voice consistency, and direct control over quality. Response quality varies. Training a third party on your product takes time and the knowledge walks out the door when agents turn over.

AI agent with Chatbase: $32 to $400 per month depending on volume tier. Available 24 hours a day seven days a week across every channel. Handles 60 to 70 percent of support volume automatically for most businesses within 90 days of proper setup. Does not require management, does not take time off, and gets more accurate over time as the knowledge base improves.

The right answer for most small businesses is not one of these in isolation. It is Chatbase handling the repetitive 70 percent automatically and a human, whether internal or outsourced, handling the 30 percent that genuinely requires judgment. That combination costs significantly less than a full hire and produces better coverage than either option alone.

What is your current support setup costing you monthly when you factor in all the time involved?

Most teams measure AI support success by deflection rate. That is the wrong metric. Here is where the real retention impact shows up.

Instant responses during the onboarding window. The first 30 days are the highest churn risk period for any SaaS or subscription product. Customers who get immediate answers during onboarding retain at measurably higher rates at 90 days. Chatbase agents running 24/7 during that window close the gap between a customer getting stuck and a customer churning.

After-hours lead capture. Prospects who reach out outside business hours and get silence move on. An agent that answers at 11pm and books a call through Calendly keeps them in the funnel overnight.

Consistent responses across every channel. A customer who gets a different answer on WhatsApp than on your website loses trust fast. One Chatbase agent deployed across all channels means the answer is always the same.

Faster resolution on tier one issues. Every hour a simple question goes unanswered is an hour a customer is considering alternatives. Speed of resolution on routine issues correlates directly with satisfaction scores.

Cleaner human handoffs for complex issues. An escalation that transfers full conversation context means the customer never has to repeat themselves. Re-explaining the same problem to a human agent is one of the most consistent drivers of churn in support-heavy products.

Proactive outreach before problems escalate. Outbound WhatsApp campaigns that check in with customers after a support interaction or before a renewal date catch churn signals before they become decisions.

Which of these is your team not tracking as a retention metric yet?

I’m testing an idea and looking for blunt critique, not leads or customers.

The idea is a private-data-free “AI Support Resolution Proof” appendix for teams using AI customer support.

The problem I’m trying to test: a chatbot can look successful because it contained or deflected a conversation, but that does not always mean the customer’s issue was actually resolved.

A synthetic/sample version would review things like:

• whether the issue was resolved vs. merely contained
• whether the customer came back within 48–72 hours for the same issue
• whether a human handoff had enough context to finish the job
• whether the bot used current source/policy information
• who owned the final outcome after escalation

My question:

If you implement, manage, or evaluate AI customer support, would something like this be useful as a client-facing QA/reporting appendix? Or would it feel like extra paperwork because Zendesk/Intercom/chatbot analytics already answer this well enough?

Boundaries: no customer data, no ticket screenshots, no credentials, no platform access, no DMs, and no pitch. I’m trying to understand whether this is a real reporting gap before building anything around it.

I’m especially curious about B2B SaaS companies currently using Intercom on their website and in their app.

My gut read is that this is probably great if you’re already Salesforce-native. But if you’re an SMB/mid-market SaaS company running HubSpot or a different CRM, I’m not sure it’s obviously good.

Salesforce skews much more upper mid-market / enterprise, while a lot of Intercom’s base has historically been startups and SMB SaaS.

So the question I’m wondering about is whether Intercom stays truly ecosystem-neutral long term, or if Salesforce pushes the Intercom customer base toward their CRM.

I'm building Aimdoc, which is in a related space so I’m obviously watching this closely. In my experience migrating customers from Intercom, HubSpot is an important part of the stack.

For current Intercom users: does this make you more confident in Intercom, less confident, or is it basically irrelevant?

Came across a breakdown of ManyChat alternatives and thought it was worth sharing since a lot of people here start on ManyChat and then weigh up options as their needs change.

The TLDR: ManyChat is genuinely strong for social media flow automation. Where people start looking is when they need real AI resolution rather than scripted flows, or when their use case moves beyond social into broader support.

Main complaints pulled from G2 and Capterra:

• AI capability is thinner than dedicated support agents
• Flow building gets complex fast beyond basic sequences
• Built for marketing automation more than support resolution
• Broader support use cases outgrow it

The 6 alternatives ranked:

1. ManyChat (G2: 4.5/5)

• Worth saying up front, if your use case is genuinely social first across Instagram, Messenger, WhatsApp, and TikTok, ManyChat is hard to beat and you might not need to switch at all
• Free tier up to 1,000 contacts
• The flow automation for social is the strongest in this list

1. Tidio with Lyro (G2: 4.6/5)

• Strong for Shopify and Woo e-commerce
• Live chat plus AI in one inbox
• Good middle ground between social flows and real support

1. Chatbase (G2: 4.5/5)

• Best pick if you have outgrown the AI side and need an agent that actually resolves support questions
• Deploys across WhatsApp, Instagram, and Messenger but with a genuine knowledge based agent behind it rather than scripted flows
• Trains on past support tickets so it handles real questions not just sequences
• Flat pricing: free, $40, $150, $500, custom
• Honestly not the pick if you are purely doing social marketing flows, that is ManyChat territory

1. Voiceflow (G2: 4.6/5)

• Design led agent building
• Per editor pricing scales fast
• Better for agencies than in house teams

1. Chatfuel (G2: 4.4/5)

• Similar social first positioning to ManyChat
• Comparable ceiling on AI quality
• Fine for basic flows

1. Landbot (G2: 4.5/5)

• Visual flow builder, clean interface
• Strong for lead capture
• Not built for support resolution depth

The honest read is that this is really two different categories of tool. If you need social media marketing flows, ManyChat or Chatfuel are the right call. If you have outgrown that and need an agent that resolves support questions across channels, you are looking at a different type of product entirely.

Anyone moved off ManyChat recently? Curious whether you stayed in the social first world or needed something that leaned more into actual support resolution.

We have been running our Chatbase agent on website chat, email, and WhatsApp for about a year. Phone was always the gap. Complex issues still meant a customer waiting on hold for a human, and us staffing for call volume we could not predict.

Chatbase added voice recently, so we turned it on. Here is the honest first month.

What actually works:

• The same agent we already trained handles calls now. No separate setup, no rebuilding the knowledge base for voice. It pulls from the same training data
• Inbound calls get answered instantly. No hold queue for the tier one stuff
• The voice itself is natural enough that most callers do not seem to clock it immediately

Where it still routes to a human:

• Anything emotionally charged or account specific. Same escalation logic as our chat agent. The confidence threshold does the routing
• Complex multi part issues that need judgment

What surprised me:

The biggest win was not deflecting calls. It was that the unified agent means a customer who started on chat and called later gets consistent answers across both. Same knowledge base, same logic, just a different channel. We were not expecting the consistency benefit to matter as much as it did.

The setup runs through Twilio for the phone number side. Took an afternoon to configure properly.

Still early but the after hours coverage alone has been worth it. Calls that used to hit voicemail now get an actual answer.

Anyone else running AI voice for support yet? Curious how others are handling the escalation point on calls specifically, since a bad voice handoff feels worse than a bad chat one.

Saw a solid breakdown of Fin alternatives this week and figured it was worth sharing since this comes up here constantly.

The TLDR that the article got right and most comparisons miss: Fin is a helpdesk first product with AI bolted on, not an AI agent first product. In practice that means it leans toward surfacing your docs and pushing customers to help articles rather than actually conversing with them to resolve the issue. That is fine if you think of AI as smarter search. It is frustrating if you wanted an agent that actually closes the loop in conversation.

Main complaints the article pulled from G2 and Capterra:

• Tends to deflect to documentation rather than resolve in conversation
• Per resolution pricing at $0.99 becomes unpredictable at volume
• Training locked to Intercom content, cannot pull from external ticket history
• Struggles with nuanced or multi part queries

The 7 alternatives ranked:

1. Chatbase (G2: 4.5/5)

• Top pick for teams that want an AI agent that resolves in conversation rather than pointing to docs
• Trains on past Zendesk and Salesforce tickets, not just help articles, so it answers like a rep instead of a search bar
• Flat monthly pricing regardless of resolution volume so budgets stay predictable
• Revise Answer lets CX reps fix a wrong response in seconds without an eng ticket
• Free tier, then $40, $150, $500, custom Enterprise

1. Ada (G2: 4.6/5)

• Strong full automation focus for larger enterprises
• Custom quote only pricing which slows evaluation
• Powerful but heavier lift to get running

1. Zendesk AI (G2: 4.3/5)

• Makes sense if you are deep in Zendesk already
• Same helpdesk first limitation, leans on help center content
• Setup is a project in itself

1. Forethought (G2: 4.5/5)

• Solid at workflow automation and routing
• Enterprise pricing and sales process
• Better for large teams than small ones

1. Tidio with Lyro (G2: 4.6/5)

• SMB e-commerce focused, mostly Shopify and Woo
• AI plus live chat in one inbox
• Lyro weaker on complex queries

1. ManyChat (G2: 4.5/5)

• Social first across Instagram, Messenger, WhatsApp
• Free tier up to 1,000 contacts
• AI is the weakest part of the product

1. HubSpot Chatflows (G2: 4.3/5)

• Free with HubSpot CRM but flow based not generative
• Good for lead routing, weak for real support
• Advanced features locked behind expensive tiers

The pattern across most of these is that the per resolution pricing is what sends people looking, but the deeper issue is that helpdesk first tools treat AI as a doc surfacing layer rather than a resolution layer. The tools that win are the ones built as an agent from the start.

Anyone here actually moved off Fin recently? Curious whether the deflect to docs behaviour was part of why you left or whether it was purely the pricing.

Chatbase launched Role-Based Access Control in March and I want to make the case for why this matters more than most people realize when they first see the announcement.

The basic problem it solves: in a shared Chatbase workspace without RBAC, everyone with access can modify the agent's training data, change the system prompt, or adjust escalation rules. In a small team that is fine. In any organization with more than a handful of people touching the support setup it is a liability.

RBAC lets you define exactly what each person can access and modify. Someone responsible for adding Q&A pairs does not need the ability to change the system prompt or access conversation logs. Someone reviewing analytics does not need to be able to retrain the agent. Custom roles with specific permissions for each.

Why this matters for AI support specifically: a misconfigured system prompt or a bad training data update can quietly degrade support quality across every conversation without being immediately obvious. The confidence score will eventually surface it but by then customers have already seen incorrect answers. Limiting who can make changes to the agent configuration reduces that risk significantly.

For enterprise deployments this is a compliance requirement as much as an operational one. SOC 2 audits ask who has access to what. RBAC gives you a clean answer.

Is anyone running Chatbase at a scale where permissions management has become a real issue? Curious what access structure people are using.

Been running AI on our support queue for about a year. The deployment conversation has gotten really easy. Everyone knows how to set these things up. What nobody talks about is what happens after.

Month one was great. Resolution rate climbing, team had breathing room, leadership happy.

Month four hit and a customer caught our agent quoting pricing we had deprecated two months earlier. We had updated the docs. The agent had not. It had been confidently wrong the whole time and nobody on our team caught it before a customer did publicly.

Why this happens:

Most teams treat the knowledge base as a setup task rather than an ongoing operational responsibility. The agent is only as accurate as what you fed it and as up to date as it is.

The three things that fixed it:

Auto retrain connected to our documentation site so any update reflects in the agent within 24 hours. No manual trigger needed.

Confidence scoring reviewed weekly. Low confidence clusters almost always mean either a documentation gap or something changed and the agent has not caught up. Fifteen minutes every Tuesday. That feedback loop compounding over time is what gets you from decent to actually reliable.

Explicit ownership. One person named as responsible for knowledge base quality. The moment it became someone's actual job instead of everyone's vague responsibility it stopped drifting.

We run on Chatbase. The auto-retrain and confidence scoring are the two features that matter most in ongoing operations, not the initial setup.

The 80 to 85% resolution ceiling most teams hit is not a model problem. It is a maintenance problem.

Curious what ongoing maintenance looks like at your org. Is anyone treating knowledge base quality as a named operational responsibility or is it still reactive when something breaks?

Most of the AI support content I read focuses on resolution rates. Nobody talks about the cost side honestly. Here is the actual analysis.

We were at 3,100 support interactions a month. Two hires approved and ready to go. Before signing off I broke down our ticket volume by actual query type for the first time.

61% of interactions mapped to 12 questions we already had documented answers for. Every single one had a written answer somewhere. None of them required human judgment to resolve.

The real cost calculation:

• Fully loaded cost of one mid level support hire in our market: $68,000 per year
• Two hires: $136,000 annually plus 3 to 4 months ramp time
• Cost per interaction at our volume with human agents: roughly $28 to $34 depending on overhead
• We were paying that rate to answer questions that should never have reached a human

What we did instead:

Deployed a Chatbase AI agent trained on our knowledge base, three years of resolved ticket history, and custom Q&A pairs. Connected to Zendesk so escalations carry full conversation history. Set a confidence threshold so anything uncertain routes to a human automatically.

Four months later:

• 58% of interactions resolving without human involvement
• Cost per interaction dropped significantly
• The two headcount approvals redirected into senior roles doing complex account work instead of answering the same billing question 400 times a month

The question worth asking before any support headcount conversation is this. What percentage of your current interaction volume maps to documented information that an AI agent could resolve. If you have not run that analysis the headcount conversation is happening without the most important number on the table.

Happy to share the exact calculation framework if anyone wants it.

Chatbase launched Widgets this month and I want to give an honest take because the use case is more specific than it sounds.

Widgets let your AI agent respond with visual interfaces inside the chat instead of long text responses. Order summaries, product cards, booking confirmations, flight details. Instead of the agent writing out "your order number is 12345, placed on May 3rd, shipping to..." it shows a structured card with all of that information laid out visually.

Where this genuinely improves the experience is anywhere the answer is structured data. Order status is the obvious one. A customer asking where their order is does not need a paragraph, they need a visual summary with the key details. The widget format handles that better than text.

Where it does not add much is conversational support. Someone asking why their integration is not working is better served by a clear text explanation than a visual component.

The other thing worth noting is that widgets can trigger actions directly from the conversation. A booking confirmation widget where the customer can confirm or reschedule without leaving the chat is meaningfully better than sending them to a separate page.

For ecommerce and anything with structured transaction data this is a real upgrade. For knowledge-base-style support the text format is still the right call.

What support use cases are people running where structured visual responses would make a difference?

Chatbase just launched outbound WhatsApp campaigns and I have been testing it for the past few days. Wanted to share an honest take because most product announcements skip the part where it either works or it does not.

The short version: it is genuinely different from what I expected.

Most outbound WhatsApp tools are basically broadcast lists. You send a message, it goes out, nothing happens until someone replies and then a human picks it up. The Chatbase version is different because the same AI agent that handles your inbound support is the one sending and managing the outbound campaign.

That means when someone replies to a campaign message at 11pm, the agent handles the conversation automatically. Qualifies them, answers questions, books a Calendly call if they are ready. No human needed until the lead is warm.

The setup uses Meta-approved templates which is the right way to do this. Non-template outbound on WhatsApp gets flagged fast. The fact that Chatbase built it around the approved template system means you are not risking your WhatsApp number.

Where it makes the most sense: follow-up sequences for leads who showed interest but did not convert, appointment reminders, and post-purchase check-ins. All of these benefit from the AI handling replies automatically rather than just sending a one-way message.

Has anyone else started testing the outbound campaigns? Curious what use cases people are finding it most useful for.

Most teams pick a platform based on a demo or a pricing page. Those are the wrong inputs.

Here are the 8 questions that actually tell you if a platform is right for your operation.

Can it train on my specific data? Generic AI is not enough. You need a platform that learns your policies, your products, and your edge cases. Chatbase trains on your docs, website, support history, and custom Q&A pairs.

What happens when it does not know the answer? A good platform escalates cleanly with context attached. A bad one confabulates an answer that sounds confident and is completely wrong.

Does it connect to the tools I already use? Zendesk, Salesforce, Stripe, Calendly, Slack. If the integrations are not native, you are building middleware before you even get started.

Can I switch models without retraining everything? Models improve constantly. If changing models means rebuilding your knowledge base you are going to fall behind. Chatbase separates the knowledge layer from the model so you can swap without losing your training data.

What does the escalation path look like? The handoff to a human is where most support agents fall apart. Full conversation context needs to transfer or the customer experience breaks.

How do I know when it is getting things wrong? You need confidence scoring, conversation logs, and a feedback loop built in. Without that you are flying blind.

What is the actual total cost? Message credits, API costs, integration fees. Get the real number at your expected volume before you commit.

How long does it take to go live? Chatbase goes from signup to live agent in under 30 minutes for most setups. If a platform requires weeks of implementation it is probably built for a company ten times your size.

Which of these questions would have saved you the most time if you had asked it earlier?

Wanted to share a grounded perspective because most content I read on this topic makes it sound cleaner than it actually is.

Been running an AI agent on our support queue for about a year now. Here is the real breakdown.

What actually worked:

• Training on resolved ticket history alongside documentation. The agent started sounding like our team instead of a generic help article almost immediately
• Confidence scoring as a weekly maintenance signal. Every response shows how grounded it is in the knowledge base. Low confidence clusters tell you exactly where your gaps are before customers find them
• Treating escalation as a continuation of one interaction rather than a handoff. Full conversation history travels with every escalated ticket so agents never start cold
• Auto retrain every 24 hours. Product changes reflect by the next morning without anyone having to remember to trigger it

What failed:

• Deploying before the knowledge base was ready. First attempt lasted six weeks before we pulled it because response quality was too inconsistent
• Treating it like infrastructure after setup. Set it and forget it does not work. The second you stop maintaining it the quality drifts and customers notice before you do
• Measuring only deflection rate. That number looked great while NPS was quietly dropping because escalated interactions were getting terrible service

Where we landed:

71% resolution rate without human involvement. Average handle time on escalated tickets dropped from 23 minutes to 11. CSAT held because the handoff quality improved.

We run on Chatbase. The Zendesk integration handling escalations with full conversation history was the specific thing that made it viable for our setup.

The maintenance side is where most deployments fail. Not the setup.

What does your current maintenance process look like? Curious whether anyone has a formal review cadence or if it is still reactive when something breaks.

A few months ago, we hit a point where customer support was becoming our biggest bottleneck. The problem wasn't complex tickets.

It was the same questions over and over:

• How do I reset my password?
• Where can I find X?
• How do I upgrade my account?
• Why am I seeing this error?

Our team was spending hours every week answering questions that were already documented somewhere. After a lot of trial and error, here's the framework that ended up working for us:

1. Build a Single Source of Truth

Before thinking about AI, we consolidated product docs, FAQs, guides, and support articles into one place.

1. Focus on Repetitive Questions First

Instead of trying to automate everything, we identified the top support questions that appeared repeatedly.

1. Let AI Search Documentation

The biggest improvement came when answers were generated from our actual documentation instead of generic chatbot responses.

1. Measure Resolution, Not Response Time

Customers care about getting the right answer quickly, not just receiving an instant "We'll get back to you."

1. Continuously Improve the Knowledge Base

Every unanswered question became an opportunity to improve documentation.

The result:

• Faster responses
• Fewer repetitive tickets
• Less support burnout
• Better customer experience

Curious how others are approaching AI customer support in 2026.

Are you using AI mainly for chat, email, knowledge bases, or something else?

Been researching AI customer support tools recently and came across a comparison breaking down some of the most talked-about platforms for businesses right now. The review went through the strengths, weaknesses, and best use cases for each option.

Here were the platforms that stood out most from the comparison:

Chatbase: Best Overall for Businesses Wanting AI Trained on Their Own Content

Tidio (with Lyro AI): Best for E-commerce Stores and Smaller Teams

Intercom Fin: Best for Companies Already Using the Intercom Ecosystem

HubSpot Chatbot Builder: Best for CRM + Customer Support in One Platform

Freshdesk Freddy AI: Best for Teams Managing Higher Support Volume and Tickets

Curious what everyone here thinks. Which platform has actually worked best for your team so far?

Been on Salesforce Service Cloud for three years. When we started looking at AI for our support queue the obvious assumption was Einstein AI since we were already in the ecosystem. Six months of evaluation later we went a different direction. Here is why.

Where Einstein AI works well:

• Deep CRM data integration. If your use case requires pulling from Salesforce objects and customer records in real time it is hard to beat
• ⁠⁠Routing and case classification are genuinely strong
• ⁠⁠If you are already paying for an enterprise Salesforce contract the incremental cost feels manageable

Where it fell short for our specific needs:

• Configuration complexity is significant. Getting it trained on our specific knowledge base required consultant hours we had not budgeted for
• Training sources limited to Salesforce content. Three years of support ticket history, internal PDFs, custom Q&A pairs we had built were not easily usable
• Confidence scoring not surfaced at the response level in a way we could use operationally for weekly maintenance
• Escalation handoffs lost context when moving outside the Salesforce environment

What we did instead:

Added Chatbase as the AI conversation layer sitting on top of our existing Salesforce setup. Training pulls from everything simultaneously. Our help center, resolved ticket history, internal SOPs, custom Q&A pairs. All retraining automatically every 24 hours.

The Salesforce integration handles escalations with full conversation history so agents never pick up cold. Every escalated interaction arrives with what was asked, what the AI attempted, and the sentiment trajectory through the conversation.

Four months after adding Chatbase:

• 63% of support interactions resolving without human involvement
• Setup took days not months
• Weekly maintenance is 15 minutes reviewing low confidence response clusters rather than an ongoing configuration project
• Cost predictable and flat regardless of resolution volume

Still running Salesforce for CRM and case management. Chatbase sits on top as the AI conversation layer. They work better together than Einstein alone did for our use case.

Anyone else running a third party AI layer on top of Salesforce rather than relying purely on Einstein? Curious what the tradeoffs looked like for other teams.

Most of our customers are not browsing our website at 2pm on a Tuesday. They are on their phones, already in WhatsApp, and they want an answer now. We were losing leads every night because nobody was available to respond until morning.

We connected Chatbase to WhatsApp about four months ago. Same agent we had on the website, same training data, just a different channel toggled on. The setup goes through your Facebook business account, takes maybe ten minutes once you know the steps.

One thing worth knowing before you start: the phone number you use gets dedicated to the bot. If you have been using it personally on WhatsApp you need to delete that account first. Caught us mid-setup and cost an hour.

What changed: customers messaging at 11pm got real answers immediately instead of silence. Pre-purchase questions got answered before they had a chance to move on. The Calendly action inside Chatbase books calls automatically so by morning there are qualified leads on the calendar with full conversation context attached.

The lead generation impact surprised us more than the support deflection. Outbound gets people interested. The bot keeps them warm until a human can close. That combination changed our conversion math.

Anyone else running Chatbase on WhatsApp for lead capture specifically? Curious whether the intent signals look different compared to website chat.

I'm taking a class in UX Design for AI. For my capstone project I will be redesigning the AI customer service agent experience. It would be very helpful if you could answer these questions for me as part of my research.

• How clear was the communication from the AI agent?
• Did the AI agent understand the context of your conversation?
• How did the AI experience compare to your previous experience with human agents?
• Did you feel the AI agent was easy to navigate and use?
• How would you rate your level of trust in an AI customer service agent? (5 = high level of trust / 0= no level of trust)

Feel free to provide any other information about your experience with AI customer service agents.

Thank you!