OnChat
Boost your sales with an AI chatbot for customer support
Managing an online business often means juggling customer questions, product information, and order updates across multiple channels. OnChat was built to make this workload easier to manage, without adding more complexity to your day-to-day operations.
How OnChat Can Help
Customer support
OnChat can take care of common, repetitive questions that usually consume a large part of support teams’ time. By handling these routine interactions, it helps reduce interruptions and allows your team to focus on tasks that require human judgment and creativity.
Product recommendations
Instead of relying on pre-trained answers that can become outdated, OnChat retrieves product information directly from your store. This means customers receive up-to-date details about prices and stock availability at the moment they ask.
Order management
Customers often want quick updates about their orders. OnChat can respond to order-related questions, such as order status, without requiring manual intervention, helping shorten response times and set clearer expectations.
Discounts & lead generation
OnChat can share information about ongoing promotions when relevant and help collect basic details from interested visitors. This supports lead qualification without turning conversations into forced sales interactions.
Where OnChat Fits In
Your online store
When connected to your store, OnChat can answer questions about orders, returns, and shipping, and provide product suggestions based on what’s available. It integrates with Shopify, WooCommerce, PrestaShop, and BigCartel.
Social media & email
Customers communicate across many platforms, and OnChat helps keep those conversations consistent. It can respond through Facebook Messenger, WhatsApp, Instagram, Telegram, LinkedIn, and email, helping you stay present where your customers already are.
OnChat is designed as a practical tool for everyday operations—supporting accuracy, consistency, and availability—without trying to replace the human side of customer relationships.
4 Comments
Congrats on the launch! What channels are you testing to bring in your first customers?
The “custom persona” feature is smart—so many AI chat tools feel generic. For my AI video tool, I’m leaning into “only Kling 2.6 model” (no other models cluttering the UI) to stand out. Do you think doubling down on a single, specific value prop (like your custom personas) is better for indie AI tools than being a “one-stop shop”?
Real-time store data is smart - you're solving the "wrong price" problem that kills trust in most e-commerce chatbots. But there's a deeper layer most support tools miss: answering questions isn't the same as making customers understand your value.
OnChat can tell someone the correct price and stock, but can it explain WHY they should care about your product compared to alternatives? That's the gap between customer service and customer comprehension.
Most chatbots optimize for response speed and accuracy, but they assume the customer already understands what they're looking at. The real friction happens earlier - when someone lands on your store and doesn't immediately grasp what makes you different.
We're building voice agents that guide users through products in real-time (demogod.me) - basically turning "here's the answer to your question" into "here's why this matters to you specifically."
Your multi-channel approach is solid for where customers already are. The next level is helping them understand your value proposition before they even have to ask questions.
Congrats on the launch @mathezsolti! Did a quick market research and added some feedback here (check under feedback tab)
Smart to focus on live store data instead of static FAQs - that's where most e-commerce chatbots fail. The multi-channel angle (WhatsApp, Instagram DM, etc.) makes sense for non-US markets where that's how people actually shop. How are you thinking about competing with Gorgias/Tidio who have much deeper Shopify integrations and App Store distribution? Is the play to stay cheaper and simpler, or will you eventually need feature parity?