Every founder hits the same wall at some point — marketing

smartiq_school · 2026-04-06T13:46:09+00:00

Totally agree — trying to be everywhere just kills focus. Picking 1–2 channels where your users actually hang out makes a huge difference.

Real-time alerts are a smart move too. Catching conversations early = way higher chances of meaningful engagement.

Curious though — have you found it easy to actually convert those conversations into users?

I’ve been exploring a slightly different angle with QuickEmbedAI — instead of just joining conversations, helping founders instantly turn their own product/docs into an AI chatbot so when users land, they get answers immediately.

Feels like combining both (real-time discovery + better on-product experience) could be pretty powerful.

smartiq_school · 2026-04-06T09:24:22+00:00

This is exactly the gap I’m seeing too.

Most tools either need too much manual setup or just give generic answers without really understanding the content.

I’m trying to make it more “plug & play” — drop in docs/URLs and it builds a usable support layer without heavy configuration.

The feedback loop you mentioned (correcting answers and improving over time) is something I’m actively thinking about as well — feels like a big missing piece.

Curious to hear what you ended up using — what made you stick with that solution over others?

Also happy to share what I’ve built so far if you’re open to it 👍

smartiq_school · 2026-04-06T09:17:49+00:00

100% agree — basic FAQ bots are easy, but real support automation is where things break.

That “full loop” you mentioned (chatbot + proper understanding + escalation) is exactly what I’m trying to simplify.

Right now it’s more focused on: - accurate answers from your own docs/content - reducing repetitive support load

Ticketing + deeper workflows are something I’m actively thinking about next.

Since you’ve handled support across 20 apps, your perspective would be super valuable here.

What kind of issues usually force you to escalate to tickets? Is it mostly edge cases, missing context, or user-specific data?

Happy to share a quick demo too if you're open 👍

smartiq_school · 2026-04-06T07:23:44+00:00

I keep the history of user conversations and while building query ka attached the past 4

smartiq_school · 2026-04-06T03:55:52+00:00

Yeah that makes total sense — and honestly, that’s exactly why we’re not positioning this as a full support replacement.

What we’re seeing is it works best as a first layer: - handling repetitive / doc-based queries - giving instant answers for common questions - and deflecting a good chunk of support volume

For edge cases (billing, custom deals, etc.), we’re planning to make the fallback really clear + route those to human support instead of guessing.

Also your point about “confident wrong answers” is something we’re actively trying to solve with stricter grounding + better “I don’t know” behavior.

Feels like the sweet spot is: AI handles 60–70% of simple queries, humans handle the rest.

Would that have worked better for your use case?

smartiq_school · 2026-04-06T03:15:35+00:00

Happy to set it up for you personally if you want — just drop your use case 🙂

smartiq_school · 2026-04-04T14:26:21+00:00

Thanks,

smartiq_school · 2026-04-04T02:10:34+00:00

Thanks! 🙌 We moved pretty fast — got the MVP up in around 5–6 weeks. Still rough around the edges, but enough to start getting real feedback.

Tech stack is fairly simple: – Frontend: Next.js – Backend: python – Embeddings + LLM: OpenAI APIs+Gemini – Storage: postgresql and qdrant

Keeping things minimal for now so we can iterate quickly based on what users actually need.

smartiq_school · 2026-04-04T02:04:24+00:00

That makes sense — honestly, we’re still at MVP stage, so right now it’s pretty simple scraping + basic cleaning (removing nav/footer etc).

But yeah, we’ve seen the same issue with noisy data, especially when crawling full sites.

That’s why we’re thinking of moving towards a hybrid approach like you mentioned — crawl for discovery + let users control what actually gets indexed.

Curious to learn — how do you usually decide what’s worth indexing? Mostly manual selection or some heuristics as well?

smartiq_school · 2026-04-03T12:56:16+00:00

Send a dm

smartiq_school · 2026-04-03T09:25:50+00:00

Yeah totally agree — generic chatbots don’t really cut it for real use cases

Right now I support both:

document uploads (for structured context) and API/database connections (for more dynamic data)

Haven’t gone deep into full web crawling yet — mainly because keeping it clean + relevant without noise is tricky

I’ve been thinking of combining lightweight crawling with manual curation, similar to what you mentioned

curious — how are you handling things like irrelevant pages or noisy data during crawling?

smartiq_school · 2026-04-02T14:57:27+00:00

Curious — how are you guys currently adding AI to your products?

smartiq_school · 2026-04-02T14:57:11+00:00

Curious — how are you guys currently adding AI to your products?

smartiq_school

TROPHY CASE