We built an AI that talks users through your product - here's what we learned
After months of building, we just launched Demogod - an AI that gives your product a voice.
The idea came from a simple observation: most users abandon products not because they're bad, but because they get stuck and have no one to ask.
What it does:
Your users land on your site, and an AI voice guide walks them through your product in real-time. No pre-recorded videos. No static tooltips. Just natural conversation that adapts to what they're actually doing.
What we learned building it:
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Voice > Text for guidance - Users process spoken instructions while keeping their eyes on the screen. Text overlays compete for attention.
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Real-time beats scripted - Pre-recorded demos can't answer "wait, how do I do X?" The moment a user has a question they can't answer, you've lost them.
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The "aha moment" is different for everyone - Some users need 30 seconds, some need 5 minutes. AI can adapt. Static demos can't.
Where we are:
Live demo at https://demogod.me - you can see it in action on the site itself (meta, I know).
We're looking for early feedback from founders who struggle with user onboarding or demo conversion. If that's you, would love to hear what's not working with your current approach.
What's your biggest challenge getting users to "get" your product?
You’re 100% right. It’s rarely the product that’s broken, it’s the onboarding and messaging gaps that tank conversions. A real-time voice walkthrough could be a serious game changer, especially if the product is slightly complex or category-defining.
If you’re open to it, I’d love to give the landing page a teardown from a sales-led perspective. I’ve been in SaaS enterprise sales 6+ years and now help early-stage founders tighten their copy to boost conversions with Pitchfix.
Your demo might be talking, but is the copy pulling its weight too? 👀
DM me if you’re down for a quick roast (friendly, mostly ) 😆
Quick follow-up - just realized IH doesn't have a built-in DM feature. Happy to connect another way if you're still up for the teardown.
You can reach us at [email protected], or if you have a preferred channel let me know here.
Appreciate the offer - would genuinely find this useful.
You're right that copy and the voice experience need to work together. We've been focused heavily on the AI side and probably haven't given the landing page the same attention.
Curious what you'd optimize first. Happy to take the roast - DM sent.
This perspective on onboarding + user guidance is very interesting — especially the idea that voice guidance adapts UX in real time.
I’ve seen similar challenges when watching product demos — the moment a viewer has to pause to “figure out what’s next”, attention drops fast. So dynamic pacing and clarity (whether it’s voice, annotation, or even subtle audio cues) can really help keep people engaged.
Out of curiosity, do you think users respond differently to audio cues vs visual guides when trying to understand flow?
Great question - this is something we've been studying closely.
Short answer: users respond to audio and visual cues differently depending on what they're trying to do.
Visual guides work best for:
Audio works best for:
What we found: the hybrid approach outperforms either alone. Audio for the narrative flow ("Now you'll set up your first project"), visual highlight for the action point (glowing button). The audio keeps momentum, the visual removes ambiguity.
The real killer is when you have to choose - visual guides that cover the thing you're supposed to click, or audio instructions you miss because you're focused on figuring out where to look. That's where adaptive timing matters.
One unexpected finding: users trust voice instructions more than text instructions for the same content. Text feels like marketing copy. Voice feels like a person helping.
Are you seeing similar patterns in the demo tools you're building? Curious what small details you've found move the needle on user comprehension.
This matches a lot of what I’ve been seeing as well, especially the distinction between momentum vs precision.
One small detail that stood out for me: audio seems to work best when it frames intent, not when it tries to describe mechanics. The moment voice shifts from “what you’re doing now” to “how to do it,” users start hesitating or pausing interaction.
I’ve also noticed that trust gap you mentioned — text often feels like instructions about the product, while voice feels like guidance inside the product. That difference alone changes how willing users are to keep moving.
Where it still feels tricky is timing: even a half-second misalignment between audio cues and visual readiness can break flow. Curious if you’ve found any reliable heuristics for when to delay audio vs delay visuals when things desync.
You've hit on something we spent months debugging - that half-second desync issue is brutal.
Our heuristics for timing conflicts:
Delay audio when:
Delay visual when:
The meta-rule we landed on: Audio can wait indefinitely, visuals feel "broken" after ~1.5s of nothing happening. So when forced to choose, we let audio lag.
Your point about "framing intent vs describing mechanics" is exactly right. We call it "narrator mode" vs "instructor mode" internally. Narrator mode ("Now you're setting up your workspace") keeps users in flow. Instructor mode ("Click the blue button in the top right") makes them stop and process.
The trick is recognizing when users need instructor mode - usually when they're lost or haven't interacted in 3+ seconds.
What patterns have you found around that intent-framing approach?
That framing lines up very closely with what I’ve been seeing.
One consistent pattern around intent-framing is that it works best when it names the phase, not the action. The moment audio describes what kind of moment the user is in (“you’re setting things up”, “this is where structure starts to form”), users keep moving. When it slips into how language, even subtly, interaction tends to pause.
Another thing I’ve noticed is that narrator mode seems to earn more trust when it’s predictable but sparse — short, timely cues that establish direction, then get out of the way. Over-narration starts to feel like supervision rather than guidance.
Your heuristic about instructor mode being reactive resonates a lot. In the cases I’ve seen, users accept directive guidance almost instantly once they’ve signaled confusion themselves (hesitation, mis-clicks, inactivity), but resist it when it arrives preemptively.
The “audio can wait, visuals feel broken” rule is especially sharp — that asymmetry matches user expectations much more closely than most systems assume.
Really appreciate you sharing how you’ve formalized this internally — it’s rare to see these timing decisions articulated this clearly.
This is incredibly valuable - you've essentially mapped out the behavioral calibration signals we've been hunting for.
The "names the phase, not the action" reframe is going into our internal docs immediately. It's more precise than anything we had.
Your observations on predictive signals align with early patterns we're seeing:
Higher guidance tolerance signals:
Lower guidance tolerance signals:
The challenge is that these signals need ~30 seconds to emerge reliably, but the first 15 seconds are when users are most likely to bounce. We're experimenting with a brief "calibration moment" - a low-stakes interaction early on that reveals preference without feeling like a test.
This thread has been one of the more technically substantive conversations I've had on IH. You're clearly deep in this problem space.
Would be curious to compare notes more directly - are you building something in this area, or researching from a different angle? Either way, happy to swap learnings if useful.
"Names the phase, not the action" - that's a better articulation than what we had internally. We've been calling it "orientation vs instruction" but yours captures the user's mental model more precisely.
The sparse/predictable observation is something we learned the hard way. Early versions had way too much narration - users felt babied. Now we aim for what one tester called "comfortable silence" - enough cues to know the system is with you, not enough to feel watched.
Your point about reactive vs preemptive guidance being the trust differentiator is spot on. We've started thinking of it as "invitation vs interruption" - when the user signals confusion first, guidance feels invited. When it arrives unsolicited, it feels like the system doesn't trust them.
One pattern we're still figuring out: the calibration window. Some users want 15 seconds before any intervention, others feel abandoned after 5. We're experimenting with letting early behavior (click speed, scroll patterns) set a per-user "patience profile" that adjusts intervention timing.
Have you found any signals that reliably predict whether a user wants more or less guidance upfront?
Really appreciate that — and “comfortable silence” is such a good way to describe it. That phrase alone explains a lot of early failures I’ve seen.
On the calibration question: I haven’t found anything that feels reliably predictive in the first few seconds. What has been consistent is that miscalibration seems more damaging than under-guidance.
If the system guesses wrong and intervenes too early, users read it as distrust. If it waits a bit too long, they’re more likely to self-correct — as long as the experience still feels intentional rather than empty.
That’s why I’ve been thinking less in terms of “detecting need” and more in terms of “maintaining a non-threatening baseline” until clearer signals emerge. Something that names the phase and sustains continuity, without implying action or evaluation.
Your “invitation vs interruption” framing fits that perfectly. It feels like trust is preserved not by being helpful quickly, but by letting users signal first.
I’d be very interested to hear how your calibration experiments evolve — especially whether the low-stakes interaction ends up feeling like orientation or like a test.