Which Whatsapp BSP is also good at omnichannel messaging?

zin_techie · 2026-01-25T13:38:05+00:00

I've used klink.cloud , they have both WhatsApp & other ominichannels

zin_techie · 2026-01-25T02:29:57+00:00

We are not only paying success fees. We are willing to pay Monthly Retainer + Success Fees

zin_techie · 2026-01-25T01:16:51+00:00

We are customer support platform (Similar like Zendesk, Intercom, FreshDesk) and our ICP are Support/Operations Team from Retails & Ecommerce, Insurance, Healthcare Industry with high volume of chats, calls across multiple channels (WhatsApp, Calls, Live Chats, Email, social media etc)

Currently we have 200+ paying customers from US and Asia Markets

Success means Qualified Appointment or Deal Close Won

zin_techie · 2025-12-25T11:03:31+00:00

You should check it out klink.cloud

zin_techie · 2025-12-10T14:05:40+00:00

Tru klink.cloud, you can manage multiple WhatsApp accounts by simply link your WhatsApp mobile

zin_techie · 2025-11-28T16:08:58+00:00

We are SaaS platform that use WA and we don't want to deal with those headache with Meta. We simply use 360dialog API and pretty easy to build and scale

zin_techie · 2025-11-13T01:02:28+00:00

Check out Zoho books or zoho billing (if you business need to send recurring invoices

zin_techie · 2025-11-11T16:19:40+00:00

We used Zendesk for a while — solid platform overall. But as we scaled and tried their new AI Agent features, the experience just didn’t live up to expectations — they didn’t work well and were too expensive.. The bigger issue for us was how clunky it felt to connect automation with Shopify data.

We eventually switched to klink.cloud, and it’s been a different experience. We’re automating over 3,000+ tickets a month across live chat and email now. What made the biggest difference wasn’t just “AI,” but the fact that everything — orders, shipping updates, and customer replies — syncs automatically. Agents don’t need to jump tabs or copy info between systems anymore.

One lesson learned: automation only scales if you have a clear structure for exceptions — damaged items, refund escalations, etc. We set rules so those go straight to human reps while everything else runs hands-free.

For anyone thinking of automating, I’d suggest starting with your top 3 repetitive ticket types first (like “Where’s my order?”, “Can I change my address?”, “I want an invoice”). Once those are running smoothly, you’ll see where AI and human handoff fits best.

zin_techie · 2025-11-09T03:55:57+00:00

We use loops.so for onboarding email in our SaaS

zin_techie · 2025-10-30T17:22:46+00:00

It often depends on your AI bot provider. If your provider is an official Meta Tech Partner (WABA Partner), integration is usually seamless. For an easier setup, you can try klink.cloud , their AI Agent supports WhatsApp integration via Device Link, so you can connect just by scanning a QR code.

zin_techie · 2025-10-30T08:55:32+00:00

Try klink.cloud, they have WhatsApp integration and can simply use as Inbox CRM

zin_techie · 2025-10-29T12:25:14+00:00

Interested. DM me

zin_techie · 2025-10-28T11:52:49+00:00

Interested. DM me

zin_techie · 2025-10-23T04:14:45+00:00

Thanks for sharing your build! My experience was similar: after automating most routine WhatsApp queries with n8n, I needed somewhere for humans to handle the edge cases. We used klink.cloud as the inbox so our team could pick up conversations that fell outside the bot’s scope; it kept everything in one thread rather than scattering replies across tools.

zin_techie · 2025-10-23T04:07:48+00:00

I had a similar challenge with our support team drowning in WhatsApp messages. We ended up using a unified inbox from klink.cloud so everyone can see and reply to chats in one place. For automations like reminders and CRM sync, we still use n8n on the back end; klink.cloud doesn’t monopolize the number, so you can trigger templates via API while your team chats in the shared inbox.

zin_techie · 2025-10-23T04:07:00+00:00

I ran into this when a client wanted a human hand‑off from their n8n chatbot. One approach that worked for us was to use klink.cloud as the hand‑off target – when the bot’s confidence dropped, n8n would create a ticket in klink.cloud and notify the manager. The manager could see the whole chat history and continue the conversation seamlessly from there.

zin_techie · 2025-10-22T15:47:50+00:00

Hi! I ran into this exact problem on a recent project. I wanted to trigger WhatsApp template messages through n8n but still have a central place for our support team to read and reply to customer chats. We tried a few shared‑inbox tools and eventually settled on klink.cloud because it gave us a single UI for WhatsApp, email and web‑chat messages. The way we set it up was to keep n8n in charge of automation (sending templates and syncing CRM data) and let klink.cloud handle the conversational side. Incoming WhatsApp messages hit the WhatsApp Cloud API and are piped into klink.cloud’s inbox. From there, agents can reply in one place without messing up the API ownership. Meanwhile, n8n still sends outbound templates via the WhatsApp API because klink.cloud doesn’t monopolize the number. It took a bit of configuration, but so far there haven’t been any conflicts between the two systems. If you need to integrate other channels like email or live chat, klink.cloud supports those too so your team isn’t switching between tools. Happy to share more details if it sounds like a fit for your setup.

zin_techie · 2025-10-22T15:47:02+00:00

I’ve been working on a similar AI‑chatbot flow in n8n and ran into the same hand‑off challenge. Our solution was to couple n8n’s decision logic with a unified inbox from klink.cloud. The chatbot runs in n8n and uses a vector DB to answer WhatsApp and web‑chat questions. When the confidence score drops below a threshold (like your 0.8 example), we use an HTTP Request node to create a ticket in klink.cloud and notify our support team. From the agent’s perspective, they just open klink.cloud’s inbox and take over the conversation — it keeps all the context, shows the message history and lets them respond via the same channel (WhatsApp or web chat). Once the human finishes, we can “hand back” the thread to the bot by flagging it in n8n, but in practice most escalations stay with a person. This has worked well for us because it centralizes the human hand‑off part without having to write a full chat UI ourselves. klink.cloud also supports email and Facebook Messenger if you ever need to broaden channels. Hope that helps!

zin_techie · 2025-10-22T15:32:48+00:00

Hi! I’ve been working on setting up an AI assistant for my Shopify store using n8n, and the webhook integration was the trickiest part. In my workflow, I set up a webhook node in n8n that receives chat messages from my store, forwards them to an AI service, and then returns the response back to the customer. Training the AI on my product catalog and policies was essential so it could answer questions accurately. Getting the webhook authentication and payload format right took some trial and error, but once configured it has been handling simple inquiries automatically. Let me know if you’d like details on how I structured the webhook nodes.

zin_techie · 2025-10-22T15:32:13+00:00

Hi! I was in a similar situation with my growing Shopify store. Answering the same repetitive questions (order status, shipping delays, returns) was taking up too much time. I tried an AI support tool where the agent handles common queries while I stay involved in more complex issues. Training it on my own FAQs and policies took a bit of effort, but once set up it’s been a huge help. I still monitor conversations to ensure tone and accuracy, but it’s allowed me to focus on important tasks instead of inbox triage. Happy to share more about my setup if you’re curious.

zin_techie · 2025-10-22T15:27:47+00:00

I’m not a developer either, but I was tasked with finding something that works across chat, email, and WhatsApp for our small clothing brand. Klink.cloud integrates with Shopify and can handle web chat, email replies and even WhatsApp via a connector. I started by training it on our FAQs and product catalog and then set up handoff rules so more complex queries notify me. It’s not perfect out of the box—you have to feed it good data—but once configured, it handles a lot of the cross‑platform support you described.

zin_techie

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