Kvota turns your voice into professional quotes and invoices in seconds. Describe the job naturally — AI extracts line items, materials, pricing, and builds a polished document. Client signs on their phone. Quote becomes a project with time tracking, expenses, and payments. Built for solo tradespeople and freelancers. Voice-first (works with gloves on), 100% bilingual English/Spanish, and $29.99/mo vs $50-200 for competitors. Speak it. Send it. Get paid.
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Maker
📌
Hey PH! 👋
I'm Amey, solo founder of Kvota. I built this because I watched tradespeople and freelancers lose jobs because their quotes looked unprofessional.
These are skilled people who can install a $15,000 HVAC system or deliver a full brand redesign, but send estimates via text message because every quoting tool is either too complex, too expensive, or too slow.
The "aha moment" for me was watching a plumber spend 25 minutes typing up a quote on his phone after a site visit. He was exhausted, it was 7pm, and he just wanted to go home. The quote he sent looked like a text message to a friend.
I thought: what if you could just *talk* about the job — the way you'd describe it to a coworker — and get a professional document out of it?
That's Kvota. Speak it. Send it. Get paid.
A few things I'm particularly proud of:
🎤 **The voice flow is magic.** Speak naturally with filler words, trade jargon, whatever. The AI figures it out and structures everything perfectly.
📊 **Effective Rate.** This shows your *actual* hourly earnings on each project. Lots of people discover they're making $35/hr on jobs they quoted at $80/hr because of scope creep and underestimated materials. Over time, it teaches you what you should actually be charging to stay profitable.
🇪🇸 **Full Spanish support.** Not just translated UI — the entire app and every document works natively in Spanish. One tap to switch any document.
I'd love your feedback. I'm a solo founder, so every piece of input directly shapes what gets built next.
AMA in the comments! 🚀
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The voice-first approach is smart, especially the part about tradespeople with gloves on. I'm building an AI chatbot platform for freelancers and agencies and we ran into the same insight, these folks don't have time for complicated UIs. How do you handle the AI accuracy when someone speaks in mixed English/Spanish mid-sentence?
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Maker
@cuygun Great question! Right now the speech recognition is set to one
language at a time — English or Spanish — based on the app language
setting. Apple's on-device speech engine handles each language
really well individually.
For code-switching (mixing both mid-sentence), the AI cleanup
step actually handles it surprisingly well. If someone says
"two days para remoción del unit," the AI understands the intent
and structures the line items correctly regardless of the mix.
That said, true bilingual speech recognition is on the roadmap. It's a real need —
a lot of crews speak Spanglish on job sites. Would love to hear
how you're approaching the language challenge on your end too!
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@amey_avhad, yeah the AI cleanup catching mixed-language intent is the pragmatic path, trying to do true bilingual speech recognition is a rabbit hole. On our end we went with a similar approach, we tell the LLM "detect the language and respond in it" rather than doing language detection server-side. Works for 18 languages but code-switching is still messy. Interested to see how you solve the Spanglish problem, that's a real-world edge case that matters more than adding language #19.
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Maker
@cuygun Yep, you are right in saying that. Hopefully in V1.1. For now, I’m letting the AI cleanup handle it since it understands intent regardless of the mix. Not perfect yet but working. Would love to see what you're building — drop a link!
The voice-first approach is smart, especially the part about tradespeople with gloves on. I'm building an AI chatbot platform for freelancers and agencies and we ran into the same insight, these folks don't have time for complicated UIs. How do you handle the AI accuracy when someone speaks in mixed English/Spanish mid-sentence?
@amey_avhad, yeah the AI cleanup catching mixed-language intent is the pragmatic path, trying to do true bilingual speech recognition is a rabbit hole. On our end we went with a similar approach, we tell the LLM "detect the language and respond in it" rather than doing language detection server-side. Works for 18 languages but code-switching is still messy. Interested to see how you solve the Spanglish problem, that's a real-world edge case that matters more than adding language #19.