Stop Prompting. Start Talking. Reloop Wants to Rethink the AI Ad Workflow

The Macro: AI video ad creation is crowded, but the UX problem is still wide open

Runway, Pika, HeyGen, Synthesia, Creatify, AdCreative.ai — the list of AI tools for video ad creation has gotten long enough that it’s genuinely hard to keep up with, which is usually a sign that the category is either maturing or overcrowded or both. Each of these has something real to offer. Runway’s visual quality is legitimately excellent. HeyGen’s avatar realism keeps improving at a rate that is, depending on your perspective, impressive or unsettling. Creatify has built specifically for performance marketers and has the workflow to show for it.

Here’s the thing: what this category is not short of is capability. What it is consistently short of is a coherent workflow for anyone who isn’t already fluent in AI prompting. The dominant interaction model — write a prompt, generate a clip, iterate through variations, stitch it together in something editor-adjacent — works fine for teams that have a motion designer or a creative director who speaks fluent text-to-video. For everyone else, it’s a new skill layered on top of an already demanding job. The prompt has become a bottleneck, which, look, is fixable.

The total addressable market for AI video ads is not creative agencies. It’s the millions of e-commerce brands, SaaS companies, and solo operators who need compelling video content and don’t have a production budget. For them, the current generation of tools is simultaneously too powerful and too complicated. The interface hasn’t caught up to the capability, and that gap is an opportunity.

The Micro: Brief it like a human, get a video ad out the other end

Reloop’s answer to the prompt problem is to replace it with a conversation. Instead of writing a text-to-video prompt, you describe your product to an AI agent the way you’d brief a creative partner — in normal sentences, with context, without thinking about shot composition. The agent makes choices about format, structure, and visuals, and produces a finished video ad: avatar presenter, optional voice cloning, captions, and an editor for adjustments.

The workflow here is genuinely different from iteration-heavy loops. The agent handles the intermediate decisions — shot type, pacing, voiceover tone, text overlay placement — that currently require either expertise or significant trial and error. You’re describing an outcome, not specifying a process. Whether that actually works in practice depends entirely on how good the agent’s interpretation is, which is the part you can’t evaluate from a Product Hunt launch.

The built-in editor is important and not just as a feature checkbox. Fully automated ad generation sounds great until you’re looking at the first output and it got the product name wrong in the voiceover. The ability to make targeted adjustments without re-running the full generation pipeline is what separates a demo from a workflow. Tools that nail this keep users. Tools that don’t become expensive first-draft machines.

The 34 comments alongside 324 upvotes is a healthy ratio for a video tool — people watched the demo and had specific reactions, which is what you’d expect. Video content is self-demonstrating in a way that a lead gen tool or a cloud infrastructure product isn’t. The quality of those 34 comments would tell you a lot about whether people are excited about the output specifically or just the concept. The concept is easy to be excited about.

The Verdict

Reloop is making what I think is the right bet: that the UX layer is the remaining competitive frontier in AI video ad creation, not the model quality. The models capable of producing compelling video ad content are accessible to anyone building in this space. The team that figures out how to abstract away the prompting complexity without sacrificing the output quality is the one that actually reaches the mass market.

The honest uncertainty — and it’s a real one — is whether the conversational interface actually delivers on the brief. Replacing structured prompts with natural language only helps if the agent’s interpretation is consistently accurate. If it misreads the product, guesses wrong on tone, or produces outputs that require extensive iteration to fix, the conversation paradigm becomes more friction, not less. You’ve replaced one hard thing with a different hard thing and called it progress.

The 324 upvotes tell you people liked what they saw in the demo. Whether what they get in the product matches what they imagined watching it — that’s the question. It always is.