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TalkToSales makes websites voice-controlled

Hacker News

TalkToSales makes websites voice-controlled

Hi folks, Alex Kolchinski here - I'm a YC alum (W21 with Mezli) and Stanford AI Lab alum from before that. Today I'm launching something new with my friend Alec Bell (a W23 YC alum) that we'd love to get your thoughts on. TalkToSales adds AI assistants to websites: they can answer visitors' questions, give them live tours of the website or app, and even help them book follow-up calls and buy things right in the conversation. Unlike traditional websites, which are the same for everyone who visits them, the TalkToSales assistant customizes the conversation and content shown to every visitor, whether the content is dynamic slides, a website/app tour, or both. TalkToSales also lets the people behind a website offer to "drop in" in place of the AI assistant and talk with visitors directly, when visitors accept. Shopkeepers in physical stores have always been able to talk to customers as they browse, but it hasn't been possible to do this on the web until now. We think this could be quite useful for companies that do business online to have more personal engagement with web visitors. We think TalkToSales assistants can be helpful for a number of use cases: Software sales: the website itself can become the first sales call, answering visitors' questions, qualifying their good fit and intent, and booking follow-up calls for good leads. Software onboarding and support: Imagine a Clippy that actually worked, talking to you over voice and actually taking actions in a web app for you as you ask it for how to do things. E-commerce: Imagine having a personal shopper on e.g. the Home Depot website, where a virtual man in an orange apron could show you different dishwasher models and help you pick between them. Real estate: Imagine going to an apartment building's website and having a virtual real estate agent answer your questions, give you virtual tours of apartments, and book you for a real-world tour. (Any other ideas? We're all ears!) We think TalkToSales assistants can also be very helpful from an analytics point of view - instead of just seeing who visits your page and for how long, imagine being able to see which questions people have, what objections prevent them from engaging deeper, etc. TalkToSales logs transcripts of conversations and the AI can surface patterns from those conversations from you. Please try it out and let us know what you think! And if you know anyone else who'd be interested in this, I'd appreciate you sharing it with them. If you're interested in talking about using TalkToSales for your business, or just have any more questions, please reach out - I'm at alex@talktosales.com (And for even more details, you can also see my blog post at https://alexkolchinski.com/2025/03/28/announcing-talktosales... )

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, new · Missing: mac, agents, macos
93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: calls · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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