What resonated with me is that you're treating "I don't know" as part of the product rather than as a failure of it.
In customer support, confidence isn't created by answering every question. It's created by helping people understand which answers they can actually rely on.
Exactly, you captured one of the core ideas behind Chatwise.
I don’t think a support assistant should be measured only by how many questions it answers, but also by how reliably it handles uncertainty.
When the available business data does not support an answer, Chatwise should be transparent rather than generate a confident guess. From there, it can guide the customer toward clarification, a useful next step, or human support.
Thanks for taking the time to explain your thinking. I'd enjoy continuing the conversation outside the thread if you're open to it. What's the best email to reach you on?
Happy to continue the conversation here for now. What specifically would you like to discuss regarding Chatwise, product feedback, potential customer use, partnership, or advisory services?
I'm less interested in giving product feedback and more interested in understanding how you're thinking about the strategic decisions behind Chatwise. There were a couple of things in your comments that caught my attention, and that's what I was hoping to explore.
Rather than unpacking it over a long thread, I'd prefer to continue the conversation by email. What's the best email to reach you on?
Thanks for clarifying, Aryan. I’m happy to continue the conversation here if you’d like to share feedback or discuss the strategic thinking behind Chatwise.
Indie Hackers isn’t allowing my account to share email addresses yet, so I’d prefer to keep the discussion in the thread for now.
If you’re exploring a more formal strategic or business opportunity, I’d be open to connecting on LinkedIn.
Either way, I’d be interested to hear which parts of my comments caught your attention.
No made-up answers’ is a strong positioning. How are you handling edge cases where the AI doesn’t know — fallback UX is usually where trust is won/lost.
Absolutely, I see fallback UX as part of the core product, not just an error message.
When the available data does not support a reliable answer, Chatwise is designed to be transparent and then choose the most useful next step: ask for clarification, point the customer toward available information, or hand the conversation to a human while preserving the context.
The goal is to avoid both a confident guess and a dead-end “I don’t know.” I agree that this is where trust is often won or lost.
Yes , even though I’m still in the testing stage, a few patterns are already becoming clear.
Fallbacks happen most often when the customer asks for information the business has not provided, when the question is unclear, or when the answer depends on live details such as stock, pricing, delivery, or availability.
I’m handling these situations by making sure Chatwise does not simply stop at “I don’t know.” It can ask a follow-up question, provide the part of the answer it can support, explain what is missing, or transfer the conversation to a human without making the customer repeat everything.
The goal is to keep the conversation useful and honest, even when the AI cannot complete the full request. I expect the private beta to show me which fallback situations happen most often in real customer conversations.
The long-term goal is to position Chatwise as a modern replacement for traditional customer support systems, rather than only as an AI layer on top of them.
Many existing platforms were built around tickets, inboxes, and live chat, with AI added later as an extra feature. Chatwise is being designed around intelligence from the beginning combining verified business knowledge, customer conversations, automation, and human support in one system.
The goal is not to replace support teams, but to replace fragmented tools and give those teams a more capable platform.
That said, adoption can still be gradual. Businesses with existing systems may initially use integrations during the transition, while smaller businesses could use Chatwise as their complete support solution from the start.
That makes sense — building around intelligence first instead of layering it on top is a fundamentally different approach.
The interesting challenge with that path is that you’re not just competing on features, you’re asking teams to rethink how support itself is structured.
In my experience, that usually means the initial wedge isn’t “replace your system,” but “this handles a specific part better than anything else,” and then expansion happens naturally from there.
Curious — are your early users leaning more toward using Chatwise as a full replacement already, or starting with a specific slice of their workflow?
Happy to take a look at how you’re framing that transition if useful — this feels like one of those where the entry point really determines how fast it grows.
That’s a fair point, and I agree that the entry point will matter a lot.
I’m still in the testing and private-beta stage, so I don’t yet have enough real user behavior to say that one path has clearly won.
One of my goals in sharing Chatwise on Indie Hackers is to find a small group of early-access users before the public launch. I want to learn whether they naturally see it as a complete support solution or prefer to begin with one specific part of their workflow.
My current hypothesis is that the initial wedge will be the repetitive support questions Chatwise can answer reliably from verified business data, while passing unresolved conversations to a human with the full context preserved.
For smaller businesses, that may already be enough to use Chatwise as their complete support solution. Larger teams may start with that specific use case and expand as trust grows.
The long-term vision is still to replace fragmented support systems, but the early-access phase should help determine the strongest entry point.
I’d be interested to hear how you would frame that transition here in the thread.
You're thinking: "small businesses use us for everything, enterprises start with one use case." But the real split is onboarding speed vs. integration depth. The small business owner wants "live in 10 minutes with my FAQ." The enterprise team wants "plug into Zendesk, preserve context, gradual rollout to one queue." Same product, opposite first impressions.
If you frame the private beta as "complete support solution," you attract the small business owner who churns when they realize you don't have SMS or voice. If you frame it as "one workflow," you repel the small business owner who thinks "too narrow."
The fix: Run two private betas with two landing pages. Not one beta with a flexible pitch.
I run PreLaunch AI — we simulate buyer segments for B2B tools to find which landing page converts which buyer before you write a line of onboarding copy. Happy to model your SMB vs. enterprise split if you want to see which beta to run first. No strings.
About
Chatwise exists to make AI customer support reliable. It helps businesses answer questions using verified website, document, product, and API data while avoiding made-up answers and handing off to humans when needed.
16 Comments
What resonated with me is that you're treating "I don't know" as part of the product rather than as a failure of it.
In customer support, confidence isn't created by answering every question. It's created by helping people understand which answers they can actually rely on.
Exactly, you captured one of the core ideas behind Chatwise.
I don’t think a support assistant should be measured only by how many questions it answers, but also by how reliably it handles uncertainty.
When the available business data does not support an answer, Chatwise should be transparent rather than generate a confident guess. From there, it can guide the customer toward clarification, a useful next step, or human support.
Thank you for putting the idea so clearly.
Thanks for taking the time to explain your thinking. I'd enjoy continuing the conversation outside the thread if you're open to it. What's the best email to reach you on?
Happy to continue the conversation here for now. What specifically would you like to discuss regarding Chatwise, product feedback, potential customer use, partnership, or advisory services?
Fair question.
I'm less interested in giving product feedback and more interested in understanding how you're thinking about the strategic decisions behind Chatwise. There were a couple of things in your comments that caught my attention, and that's what I was hoping to explore.
Rather than unpacking it over a long thread, I'd prefer to continue the conversation by email. What's the best email to reach you on?
Thanks for clarifying, Aryan. I’m happy to continue the conversation here if you’d like to share feedback or discuss the strategic thinking behind Chatwise.
Indie Hackers isn’t allowing my account to share email addresses yet, so I’d prefer to keep the discussion in the thread for now.
If you’re exploring a more formal strategic or business opportunity, I’d be open to connecting on LinkedIn.
Either way, I’d be interested to hear which parts of my comments caught your attention.
Thanks for clarifying—that makes sense.
Rather than moving to LinkedIn, feel free to reach me at hello@beryxa.com whenever you're able. I'd be glad to continue the conversation there.
If email isn't practical right now, no worries—we can leave it here for the time being.
No made-up answers’ is a strong positioning. How are you handling edge cases where the AI doesn’t know — fallback UX is usually where trust is won/lost.
Absolutely, I see fallback UX as part of the core product, not just an error message.
When the available data does not support a reliable answer, Chatwise is designed to be transparent and then choose the most useful next step: ask for clarification, point the customer toward available information, or hand the conversation to a human while preserving the context.
The goal is to avoid both a confident guess and a dead-end “I don’t know.” I agree that this is where trust is often won or lost.
Completely agree — fallback UX is where trust is actually built.
Most tools optimize for ‘answering’, but the real differentiator is how they behave when they can’t.
The handoff + context preservation piece is especially important — that’s usually where frustration spikes.
Have you seen any patterns yet in when users hit that fallback path most often?
Yes , even though I’m still in the testing stage, a few patterns are already becoming clear.
Fallbacks happen most often when the customer asks for information the business has not provided, when the question is unclear, or when the answer depends on live details such as stock, pricing, delivery, or availability.
I’m handling these situations by making sure Chatwise does not simply stop at “I don’t know.” It can ask a follow-up question, provide the part of the answer it can support, explain what is missing, or transfer the conversation to a human without making the customer repeat everything.
The goal is to keep the conversation useful and honest, even when the AI cannot complete the full request. I expect the private beta to show me which fallback situations happen most often in real customer conversations.
That’s exactly where most AI support tools fall apart — either hallucinations or dead ends.
The handoff with preserved context is probably the biggest trust lever here.
Are you planning to position this more as a support replacement or a layer on top of existing systems?
The long-term goal is to position Chatwise as a modern replacement for traditional customer support systems, rather than only as an AI layer on top of them.
Many existing platforms were built around tickets, inboxes, and live chat, with AI added later as an extra feature. Chatwise is being designed around intelligence from the beginning combining verified business knowledge, customer conversations, automation, and human support in one system.
The goal is not to replace support teams, but to replace fragmented tools and give those teams a more capable platform.
That said, adoption can still be gradual. Businesses with existing systems may initially use integrations during the transition, while smaller businesses could use Chatwise as their complete support solution from the start.
That’s a fair point, and I agree that the entry point will matter a lot.
I’m still in the testing and private-beta stage, so I don’t yet have enough real user behavior to say that one path has clearly won.
One of my goals in sharing Chatwise on Indie Hackers is to find a small group of early-access users before the public launch. I want to learn whether they naturally see it as a complete support solution or prefer to begin with one specific part of their workflow.
My current hypothesis is that the initial wedge will be the repetitive support questions Chatwise can answer reliably from verified business data, while passing unresolved conversations to a human with the full context preserved.
For smaller businesses, that may already be enough to use Chatwise as their complete support solution. Larger teams may start with that specific use case and expand as trust grows.
The long-term vision is still to replace fragmented support systems, but the early-access phase should help determine the strongest entry point.
I’d be interested to hear how you would frame that transition here in the thread.
You're thinking: "small businesses use us for everything, enterprises start with one use case." But the real split is onboarding speed vs. integration depth. The small business owner wants "live in 10 minutes with my FAQ." The enterprise team wants "plug into Zendesk, preserve context, gradual rollout to one queue." Same product, opposite first impressions.
If you frame the private beta as "complete support solution," you attract the small business owner who churns when they realize you don't have SMS or voice. If you frame it as "one workflow," you repel the small business owner who thinks "too narrow."
The fix: Run two private betas with two landing pages. Not one beta with a flexible pitch.
I run PreLaunch AI — we simulate buyer segments for B2B tools to find which landing page converts which buyer before you write a line of onboarding copy. Happy to model your SMB vs. enterprise split if you want to see which beta to run first. No strings.