Over the past few months, Dhalia Tomlinson has been researching AI customer support platforms to better understand how they're being used in real business environments.

Rather than comparing marketing claims, she focused on how these platforms handle deployment, automation, integrations, scalability, and day-to-day operations. This is her story.

Dhalia's Experience with AI Customer Service

Two platforms that caught my attention were Chatbase and Netomi. Both aim to improve customer support with AI, but they approach the problem differently.

Chatbase

Chatbase stood out because it's built as an AI-native customer service platform, not just a chatbot builder. Instead of focusing only on answering customer questions, it provides tools for building, testing, deploying, and continuously improving AI agents throughout their lifecycle.

Businesses can train a single AI agent using websites, help centers, PDFs, documentation, Notion, and other business knowledge, then deploy that same agent across web chat, email, voice, WhatsApp, Slack, and other supported channels.

Some capabilities that stood out during my research include:

• AI-native Help Desk where AI and human agents work together
• Actions for connecting with Shopify, Stripe, Salesforce, Zendesk, and custom APIs
• Procedures for creating structured customer workflows
• Widgets for interactive customer experiences
• Built-in Testing before deployment
• Analytics, Suggestions, and Backstage for ongoing optimization
• Enterprise-grade security with SOC 2, GDPR, HIPAA support, audit trails, and role-based access controls

One thing I found interesting is that Chatbase doesn't seem limited to one type of business. The no-code setup makes it approachable for startups and growing companies, while its enterprise security, governance, workflow automation, integrations, and AI-native Help Desk also make it suitable for enterprise customer support teams managing AI at scale. It feels like a platform that can grow with a business instead of being replaced as support operations become more complex.

Netomi

Netomi is another platform that frequently appears in enterprise customer service discussions. From what I found, its primary focus is helping large organizations automate repetitive customer inquiries while integrating with existing enterprise support environments.

It appears to be well suited for companies handling high support volumes that want to improve automation without significantly changing their existing customer service infrastructure.

What I Learned

The biggest takeaway for me wasn't which platform has the "smartest" AI.

It was how differently they approach customer support.

Chatbase takes an AI-native approach by bringing AI agents, workflow automation, testing, analytics, optimization, and an AI-native Help Desk into a single platform. The goal seems to be managing the complete customer support lifecycle from one place.

Netomi, on the other hand, appears to focus on enterprise automation within established support operations, helping organizations increase efficiency while working alongside their existing systems.

After comparing both, I think the questions that matter most are:

• Can the AI handle real customer requests instead of only answering FAQs?
• How easily does it connect with existing business systems?
• Can support teams improve it without relying heavily on engineering?
• Does it provide a practical workflow between AI and human agents?
• Will it remain manageable as customer support volume grows?

Those operational capabilities seem far more important than simply comparing response quality or AI models.

If you've used either Chatbase or Netomi in production, I'd be interested to hear how they've performed over time, particularly around maintenance, scalability, workflow automation, and the overall customer support experience.

When we were evaluating CRMs, I remember being impressed by some of the feature lists.

Advanced forecasting. Lead scoring. Custom dashboards. Workflow builders. AI recommendations.

At the time, they all seemed essential.

But after actually using a CRM for a while, I realized that a lot of the day-to-day value came from much simpler things:

• Keeping contact data organized
• Tracking conversations
• Managing pipelines
• Setting reminders
• Basic reporting

Meanwhile, some of the features that influenced the buying decision barely got touched after setup.

It got me wondering how often SaaS buyers pay for capabilities they never end up using.

For those who use a CRM regularly:

• Which feature sounded valuable during the evaluation process but ended up collecting dust?
• Was it because the feature was too complex, poorly implemented, or just unnecessary?
• Did your team stop using it, or did nobody adopt it from the start?
• If you could remove one feature from your CRM today, what would it be?

Interested in hearing both startup and enterprise perspectives. Sometimes the features that look impressive in demos don't always survive contact with real workflows.

When you're building a new SaaS, signups are usually the first number everyone watches.

They're easy to measure, easy to celebrate, and often become the default way to judge whether things are moving in the right direction.

But as a product grows, I imagine that focus changes.

A high signup count doesn't necessarily mean people are finding value, sticking around, or becoming paying customers.

For founders and product teams who've been through that transition:

• At what point did another metric become more important than signups?
• Was it retention, activation, trial-to-paid conversion, churn, daily or weekly active users, customer lifetime value, or something else entirely?

I'm interested in hearing which metric ended up having the biggest impact on your decisions, and what made you realize it was a better indicator of your SaaS's health than simply tracking new user growth.

I'd especially appreciate examples where focusing on a different metric changed your roadmap, marketing, or product priorities.

I've been looking at how AI customer support platforms are evolving, and one thing I've noticed is that many businesses are trying to simplify their support stack rather than keep adding new tools.

That got me thinking about Chatbase Help Desk.

Traditionally, the workflow looked something like this:

• AI chatbot for answering FAQs
• Separate ticketing platform
• Shared inbox for agents
• Analytics tool
• Workflow automation
• Knowledge base

As AI becomes capable of resolving more customer requests, I'm wondering whether that setup is still necessary.

From what I've read, Chatbase takes a different approach by including an AI-native Help Desk as part of the platform instead of treating it as a separate integration.

It includes capabilities like:

• Omnichannel inbox for AI and human agents
• AI-generated ticket summaries and reply drafts
• Ticket assignment and status management
• Smart tagging and custom inboxes
• Workforce management and schedules
• AI-to-human handoffs with full conversation context

Combined with features like Actions, Procedures, Testing, Analytics, and Backstage, it feels like the goal is to manage customer support from one platform rather than stitching together several different products.

That said, I don't think every business can immediately replace a long-established enterprise help desk.

Organizations with highly customized ticketing workflows, legacy integrations, or complex governance requirements may still need dedicated support platforms.

But for companies building an AI-first support operation, the idea of an AI-native Help Desk seems like an interesting direction.

I'm curious how others see it.

• Has anyone replaced a traditional help desk with Chatbase?
• Which third-party tools, if any, were you able to remove?
• Was the AI-native workflow easier for your team to manage?
• What capabilities do you still rely on external support tools for?

I'd love to hear real experiences from teams using Chatbase Help Desk in production, especially whether it simplified your support stack or if you still needed additional tools alongside it.

For years, "omnichannel support" sounded great in theory.

In reality, it usually meant maintaining separate experiences across different channels.

Your website chatbot behaved one way.

Email automation worked differently.

WhatsApp had its own setup.

Voice support was another project entirely.

Keeping everything aligned often became just as much work as supporting customers manually.

That's why I've been paying more attention to platforms that build around one AI agent instead of one bot per channel.

Chatbase is one example that caught my attention.

From what I've seen, the idea isn't to create separate AI experiences for chat, email, voice, WhatsApp, or Slack. Instead, you build a single AI agent, train it on your business knowledge, define its instructions and workflows, then deploy it across supported channels.

What interests me isn't simply the number of channels it supports.

It's whether that approach makes long-term maintenance easier.

For example, if you:

• Update your help center
• Change your return policy
• Add a new product
• Modify an onboarding workflow
• Improve an AI Procedure

...those improvements can benefit the same AI agent wherever it's deployed, rather than requiring separate updates for every channel.

It also seems like the surrounding tools become more valuable when everything feeds into one place.

Instead of reviewing conversations from different systems, you can use features like:

• Analytics to understand customer trends
• Suggestions to identify knowledge gaps
• Testing before rolling out changes
• Help Desk for AI-to-human collaboration
• Backstage to manage and improve the agent over time

To me, that's a more interesting definition of omnichannel than simply saying, "we support multiple channels."

The real question is whether one AI agent can deliver a consistent customer experience regardless of where the conversation starts.

For teams already using Chatbase:

• Are you actually running the same AI agent across multiple channels?
• Has it reduced the amount of duplicate configuration and maintenance?
• Which channel has been the easiest to launch?
• Have you noticed any limitations when supporting customers across web, email, voice, or messaging apps?

I'm curious whether omnichannel AI has finally become practical, or if most teams are still managing separate support experiences behind the scenes.

I've noticed Turso showing up in more SaaS tech stacks lately, especially for products that prioritize low latency and edge deployments.

What I'm trying to understand isn't whether Turso is technically impressive; it's why teams are choosing it over a standard PostgreSQL setup in the first place.

On paper, PostgreSQL has a huge ecosystem, years of proven reliability, and countless hosting options. That's a tough combination to move away from.

So if you decided to build with Turso, what was the turning point?

Was it performance? Simpler operations? Lower infrastructure costs? Better developer experience? Or was there another reason entirely?

I'm especially interested in hearing from people who have been running it in production for a while.

• Has it lived up to your expectations?
• Were there any limitations that only became obvious after launch?
• Did it simplify your architecture, or did it introduce new trade-offs?
• If you were starting the same SaaS today, would you make the same choice?

I'm not looking for benchmark numbers or marketing claims, I'd much rather hear the practical reasons behind the decision and whether they still hold true after months of building on it.

I've been seeing Trigger.dev mentioned more often, especially by teams building AI features that involve long-running jobs, background workflows, or multiple API calls.

On paper, it seems like a good fit for those kinds of workloads. But I'm more interested in how it's held up in production.

For those who've been using it for a while:

• Has it been reliable under real traffic?
• How have debugging and monitoring been when something goes wrong?
• Have retries and error handling worked as expected?
• Did it simplify your architecture, or did it introduce its own complexity?

I'm also curious whether anyone moved to Trigger.dev from another approach (cron jobs, serverless functions, queues, or a custom solution).

What pushed you to switch, and was it worth it?

Sometimes a tool looks great in demos but feels very different after months of running customer-facing workloads. I'd love to hear experiences from people who've used it beyond a proof of concept, both the good and the bad.

If you were starting a new AI-powered SaaS today, would Trigger.dev still be your choice for background jobs, or would you go with something else?

Over the past year, it feels like the time between having an idea and shipping a product has become incredibly short.

With AI coding tools, no-code platforms, and managed APIs, founders can build and launch much faster than they could a few years ago.

That's exciting, but it also raises a question.

Are we optimizing for speed to launch, or are we skipping the part where we learn whether people actually need the product?

I've seen a growing number of launches that look polished on day one, but after trying them, they often have:

• Limited workflows beyond the core AI feature
• Little customer feedback built into the product
• Weak onboarding for first-time users
• No clear differentiation from similar tools

On the other hand, launching early has always been one of the best ways to validate an idea, collect feedback, and avoid spending months building something nobody wants.

So maybe the issue isn't launching quickly, it's launching before you've found a real problem worth solving.

For founders who have launched recently:

• Did you feel pressure to ship because the AI market is moving so fast?
• How much customer validation did you have before launching?
• Have you seen more AI products disappear just a few months after launch?
• Where do you draw the line between moving fast and launching too soon?

I'm interested in hearing experiences from both founders and early adopters. Has the faster pace of AI development improved the quality of SaaS launches, or just increased the amount of noise?

I’ve been looking into multiple enterprise AI voice deployments lately, and there’s a recurring issue that doesn’t get discussed much in vendor demos or case studies.

Most conversations around AI voice automation focus on model quality, latency, or how “human-like” the voice sounds.

But in real production environments, those are rarely the things that break first.

Here’s what actually shows up once these systems go live.

1. The system works fine in isolation, but fails in real customer journeys

Most AI voice systems are tested as standalone interactions:

• one call
• one intent
• one resolution path

But real customers don’t behave like that.

In production, calls often include:

• multiple issues in a single conversation
• switching topics mid-call
• incomplete or conflicting account details
• repeated clarification loops

The system can technically respond, but it struggles to complete the full journey.

2. “Success” is usually measured too early

A lot of deployments declare success based on:

• demo performance
• pilot phase metrics
• controlled test environments

But those conditions don’t reflect real call volume.

Once traffic increases, patterns shift:

• edge cases become normal cases
• escalation frequency increases
• resolution consistency drops

The early metrics start to lose meaning.

3. The missing layer is not AI; it’s decision logic

Most teams assume the AI layer is the hardest part.

In practice, the harder problem is:

• when to answer vs when to escalate
• when to continue vs when to stop
• when to act vs when to verify

Without clear decision boundaries, even strong AI systems become inconsistent in production.

4. Backend systems silently define success or failure

Another overlooked issue is system connectivity.

Voice AI is often expected to:

• pull customer data
• update accounts
• trigger workflows
• validate transactions

But if backend systems are:

• partially integrated
• inconsistent across services
• missing real-time access

Then the AI becomes informational rather than functional.

It can talk, but it can’t resolve.

5. Escalation design becomes the real customer experience

One of the biggest gaps in enterprise deployments is escalation flow design.

When escalation is poorly handled:

• customers repeat information to human agents
• conversations lose context during handoff
• users get stuck between AI and support teams

At scale, escalation isn’t a fallback; it is part of the product experience.

6. Knowledge quality quietly becomes the bottleneck

Even when everything else works, the system still depends heavily on underlying knowledge.

Common issues include:

• outdated policies still being referenced
• inconsistent answers across sources
• missing documentation for edge cases

The AI doesn’t invent problems, it exposes gaps in the knowledge system.

The real problem

What most teams miss is this:

Enterprise AI voice systems don’t fail in obvious ways.

They fail gradually, as small gaps in workflows, data, and escalation logic compound under real usage.

By the time it becomes visible in metrics, the system is already behaving inconsistently across different customer scenarios.

Key takeaway

The biggest enterprise AI voice problem isn’t speech quality or model capability.

It’s the lack of a complete operational design around the AI, especially how it connects to real workflows, backend systems, and escalation paths.

That’s the part nobody talks about in vendor demos, but it’s usually what determines whether the system actually works at scale or not.

I sometimes wonder if anyone's first SaaS launch actually goes according to plan.

Before launch, it's easy to picture how things will unfold.

• People sign up.
• A few bugs show up.
• You fix them.
• Word starts spreading.

Reality seems to have a habit of rewriting that script.

Maybe you expected your biggest challenge to be building the product, but it turned out to be getting people to notice it.

Maybe the feature you spent weeks perfecting barely got used, while something you almost didn't ship became the reason customers stuck around.

Or maybe your launch wasn't a big event at all, it was just a series of small improvements that slowly turned into a real business.

Looking back, I think the gap between what we expect and what actually happens is where most of the valuable lessons come from.

So I'm curious:

Did your first SaaS launch unfold the way you imagined it would?

If not, what was the biggest surprise, good or bad, that changed your expectations of building a SaaS?

I used to think the most valuable thing after launching a SaaS would be seeing the first payment come through.

The more founder stories I read, the more I think it's actually the first piece of feedback that makes you stop and say, "I hadn't thought about it that way."

Not every comment is useful.

Some people ask for features that don't fit your product.

Some report bugs you'll fix in a day.

But every now and then, someone explains how they're using your product in a way you never expected, or points out a problem that's been quietly costing you users.

Those are the comments that can change the direction of a product.

They're often more valuable than another dashboard full of metrics because they reveal something numbers can't.

I'd love to hear one of those moments.

What's a piece of feedback you received after launching that genuinely changed how you thought about your SaaS or influenced what you built next?

I've been reading through the Chatbase Actions documentation recently, and it feels like this is one of the features that changes an AI agent from "something that answers questions" into "something that actually gets work done."

Instead of only generating responses, the agent can trigger Actions to interact with external systems or execute workflows. Depending on the use case, Actions can:

• Call external APIs and use the response in a conversation.
• Display interactive widgets inside the chat.
• Run client-side code in the user's browser.
• Trigger internal workflows or third-party integrations.

The built-in Actions also seem pretty practical.

Some examples include:

• Looking up Stripe subscriptions or invoices.
• Updating billing information.
• Booking meetings with Calendly or Cal.com.
• Sending Slack notifications.
• Collecting leads.
• Escalating conversations to CRMs like Zendesk, Salesforce, Intercom, Freshdesk, or Zoho Desk.
• Performing real-time web searches when the answer isn't in the knowledge base.

What I found especially interesting are Custom Actions.

From what I understand, you can define exactly when the AI should use an action, collect any required information from the user, and connect it to your own API. Chatbase also supports different action types, including server-side actions, client-side actions, and widget-based actions, depending on how you want the interaction to work.

If you're already using Chatbase in production, I'm curious:

• What's the most useful Action you've built?
• Did you use one of the built-in Actions or create a Custom Action?
• What business process did it replace?
• Was it easier than building the same workflow yourself?

Personally, if I were implementing it today, I'd probably start with things like:

• Order status lookup
• Subscription management
• Appointment booking
• Creating support tickets
• CRM lookups

Those seem like the kinds of repetitive tasks where an AI agent can save the most time.

I'd love to hear real examples from production. Sometimes the most valuable use cases are the ones that never appear in product demos.

One thing I've noticed is that founders often spend months talking about how they're going to launch, but much less time talking about what surprised them after launch.

Not the metrics.

Not the revenue.

The lessons they wish they'd known beforehand.

Some founders realize they built features people didn't actually need.

Others discover that getting users is much harder than building the product.

Sometimes customer support ends up taking far more time than expected.

Sometimes the opposite happens, the feature everyone worried about barely gets mentioned, while users keep asking for something that seemed like a low priority.

I've also heard founders say their biggest mistake wasn't technical at all.

It was things like:

• Waiting too long to talk to customers.
• Underestimating onboarding.
• Pricing is too low.
• Launching before having a clear distribution strategy.
• Assuming early users would naturally become paying customers.

On the flip side, there are also things that turn out much better than expected.

A feature you almost cut becomes the reason people sign up.

A niche audience becomes your ideal customer.

A small launch generates valuable feedback that shapes the product.

For those who've already launched a SaaS:

What's the biggest lesson you learned that you couldn't have learned before launch?

If you were starting over today, what's the one thing you'd do differently from day one?

I noticed that Chatbase recently launched its native Shopify integration, and I'm curious whether anyone has been running it in production yet.

On paper, it looks like a pretty meaningful update because it's no longer just about answering product questions.

From what I've read, once Shopify is connected, the AI agent can:

• Answer questions using your product catalog and store content.
• Recommend products based on what shoppers are looking for.
• Look up live order status and tracking information.
• Retrieve customer cart information.
• Update customer profile details (for authenticated customers).
• Escalate conversations to a human when needed.

For ecommerce stores, that seems more useful than a chatbot that simply links customers to FAQ articles.

The feature I'm most interested in is the ability to work with live Shopify data instead of relying only on training documents.

For example, instead of responding with:

"Please check your confirmation email."

the AI can retrieve the customer's current order status once the store is connected.

I'm also wondering how people are using it beyond support.

Some possible use cases I can think of are:

• Helping shoppers find the right product.
• Answering sizing or availability questions.
• Recovering abandoned carts by answering purchase questions.
• Handling repetitive "Where is my order?" requests.
• Reducing the number of tickets that reach human agents.

For anyone who has already deployed it:

• How long did the Shopify setup take?
• Have customers actually started using the order lookup features?
• Which Shopify action has been the most valuable in practice?
• Have you noticed a measurable reduction in repetitive ecommerce support tickets?

I'm less interested in marketing claims and more interested in hearing from merchants who have been using the integration with real customers.

One question I keep coming back to is this:

How do you actually know when a SaaS is ready to launch?

It seems like every founder has a different answer.

Some launch as soon as the core problem is solved, even if there are rough edges.

Others wait until the onboarding is polished, the bugs are minimal, and the product feels "complete."

The tricky part is that there's always another feature to build or another improvement to make. At some point, you have to decide whether more development is genuinely adding value, or just delaying the launch.

For those who've already been through it:

• What made you decide it was time to launch?
• Did you have a specific checklist, or did you trust your instincts?
• Looking back, do you think you launched at the right time?
• Was there anything you waited to perfect that users didn't actually care about?

I'm especially interested in hearing about the signs that gave you confidence to ship—or the signs you ignored and later wished you hadn't.

Everyone talks about building a SaaS, but deciding when to launch feels like one of the hardest decisions in the entire process. I'm curious how others approached it and what they learned afterward.

I've started noticing a change in how I evaluate AI customer support.

A year ago, I mainly looked at dashboards.

Things like:

• Number of tickets
• Response times
• Resolution rates
• CSAT

Those metrics are still valuable, but they don't always explain what's actually happening in customer conversations.

Lately, I've found myself spending more time looking at conversation analytics instead.

The questions I care about now are things like:

• What topics are customers talking about most?
• Are those topics changing over time?
• Is customer sentiment becoming more positive or more negative?
• Which responses are getting positive feedback?
• Which conversations consistently receive thumbs down?

Those insights feel much more actionable than simply watching a KPI move up or down.

One example is Chatbase's Analytics.

Rather than only showing high-level numbers, it groups conversations by Topics, automatically detects customer sentiment, and aggregates chat activity such as total conversations, messages, user feedback (thumbs up/down), and conversation geography. Being able to filter this data over different time periods also makes it easier to spot trends instead of reacting to one busy day.

For me, that changes the weekly review process.

Instead of asking:

"Did our AI resolve enough conversations?"

I'm asking:

"What are customers actually trying to accomplish, and what can we improve based on those conversations?"

That feels like a much better feedback loop for improving an AI agent over time.

I'm curious how other teams review their AI performance.

Do you still spend most of your time looking at dashboards, or are conversation analytics becoming the more valuable source of insight?

What metric or insight has helped you improve your AI customer support the most?

I've noticed more SaaS products moving away from simple monthly plans and introducing credit-based pricing instead.

Instead of paying for a fixed number of seats or features, customers get credits that are consumed based on what they actually use: AI generations, API calls, automations, data processing, reports, or other resource-intensive actions.

I can see why it's becoming more common:

• Costs are easier to align with actual usage.
• Heavy users pay more, while light users aren't overcharged.
• It's easier to price AI features where infrastructure costs can vary.
• Companies don't have to keep creating new pricing tiers every time they launch a feature.

At the same time, I've also heard complaints that credit systems can feel confusing.

Users often ask:

• "How many credits will this task consume?"
• "Why did my credits disappear so quickly?"
• "How much will this actually cost me each month?"

In some cases, a predictable monthly subscription still feels much easier to understand.

For those building or managing SaaS products:

• Have you switched to a credit-based model?
• Did it improve revenue or customer satisfaction?
• Did support requests about pricing increase?
• As a customer, do you prefer paying for usage or having a fixed monthly bill?

I'm curious whether this shift is becoming the new standard, especially as more AI-powered SaaS products enter the market, or if we'll eventually see companies move back toward simpler pricing models.

I've been following Chatbase's product updates over the past year, and one thing I've noticed is that the platform seems to be expanding far beyond what most people would traditionally call an AI chatbot.

When I first came across it, I thought it was mainly for adding an AI assistant to a website.

Now, after reading through the documentation and recent feature updates, it feels like the focus has shifted toward covering much more of the customer support workflow.

Instead of adding one feature at a time, it looks like several pieces are gradually coming together.

For example:

• AI agents that can be trained on websites, help centers, PDFs, documentation, and historical support tickets.
• Deployment across website chat, email, voice, WhatsApp, Slack, and other supported channels from the same platform.
• Actions that let AI interact with external systems instead of only generating responses.
• Procedures for handling repeatable workflows with consistent business logic.
• A built-in Help Desk where AI and human agents can collaborate on conversations.
• Testing tools for validating changes before they reach customers.
• Analytics and Suggestions for monitoring conversations and identifying areas for improvement.
• Backstage, which gives teams a central place to manage and refine AI agents.

Looking at those features together, it made me wonder whether Chatbase is moving into a different category.

Instead of being just another chatbot builder, it seems to be evolving into a platform where businesses can build, deploy, operate, and improve AI customer support without stitching together several separate products.

That doesn't necessarily make it the right choice for every company.

Some teams may still prefer specialized tools, especially if they're already heavily invested in platforms like Zendesk, Intercom, or Salesforce.

But if you're starting fresh, I can see why an all-in-one approach would be appealing. Fewer integrations to maintain, one place to manage knowledge, and one AI agent that can work across multiple customer channels sounds simpler than maintaining separate tools for each function.

For those who are already using Chatbase in production:

• Has it replaced any other tools in your support stack?
• Are you using the Help Desk, Actions, or Procedures, or mainly the AI chat experience?
• Which newer feature has had the biggest impact on your day-to-day workflow?
• Do you still see it primarily as a chatbot platform, or has it become something broader for your team?

I'd be interested in hearing from people who've watched the platform evolve over the past year. It feels like the product has changed quite a bit, and I'm curious whether existing users see the same shift.

How are you managing your multi-platform customer support? Do you use one platform, or do you use different ones for each platform?

I've been following the direction AI customer support platforms are taking this year, and one trend keeps showing up: a single AI agent deployed across every customer channel.

On paper, it sounds like a huge improvement.

Instead of maintaining separate chatbots for your website, email automation, WhatsApp, or voice, you manage one AI agent with a shared knowledge base, the same instructions, and the same workflows.

Platforms like Chatbase have been pushing further in this direction. Rather than treating each channel as a separate experience, the platforms let businesses deploy the same AI agent across

• website chat,
• email, voice,
• WhatsApp,
• Slack,
• and other supported channels while managing it from one place.

How are you managing this?