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The "fully traceable to their original data trigger, no blackbox" line is the part I'd push on, because I think traceability is necessary and still not sufficient — and it cost us to learn where the gap is.
We build an AI that makes real phone calls, and we had a system that would produce a confident summary of how a call went. Every summary was traceable: it pointed at a real transcript, a real call, a real record. The sourcing was never fabricated. What was fabricated was the inference. A six-second call where nobody picked up produced "she picked up and the surprise was delivered successfully." Fully traced to a genuine data trigger, and completely wrong about what that data meant.
Two things that did not fix it, in case they save you time: lowering the temperature (it was already low on the path that mattered), and adding "be honest, do not exaggerate" to the prompt. Models read those abstractly and sail straight past them. What worked was grounding each claim to something literally present in the source, plus naming the exact phrases the system was not allowed to emit unless the transcript supported them. Fabricated-confirmation rate went from about 24% to 2% across ~1,200 calls, with no measurable over-suppression.
The reason I think this lands on you specifically: in creative research the equivalent failure is not a made-up source, it is a real winner with a wrong reason attached. The ad genuinely won. The attribute your workflow traced it to is not why. Trace it back and the citation checks out, so the story is more believable than a black box would be — which is exactly what makes it expensive when it is wrong.
So: does your traceability cover the trigger, or the claim? Concretely, if a workflow says "this concept came from these three reviews and this competitor ad," can a user see the evidence for the causal step, or only the provenance of the inputs?
Not rhetorical — you have thought about this more than most launches here, or the line would not be in your tagline.
Adomate
@getosmo Hi Anuj, very good question. As a data-first company, we're extremely careful about attributing the success of an ad to a specific cause. It would be easy to just ask an LLM and get a convincing-sounding answer, but in reality, you'd be attempting to reverse-engineer one of the most sophisticated commercial algorithms in the world. We'd rather show users the facts and all available data, and let them apply their own experience and market knowledge to draw the right conclusions. Adomate is built to extend the marketer, not replace them.
Adomate
@hamza_afzal_butt Every interaction with newly created content is captured on the backend, so the system can learn from the feedback users provide, think of it like swiping left or right on Tinder. Users can also access the brand brain directly, which contains all the visual style elements of the brand.
Congrats on launching! We spend way too long digging through customer reviews to find angles for ads, so pulling Trustpilot and Amazon feedback into the same workspace as ad performance sounds really handy. How does it work for smaller brands that only have a handful of reviews and a modest ad account to learn from?
Adomate
@doganakbulut Hi Dogan, every brand needs to start somewhere! That's why we allow users to draw on proven ads from more established brands. On top of that, competitor reviews can also serve as a data source, think of it as leveraging the frustrations customers express about your competitors and positioning yourself as the better alternative.
Walkable
Love the product and the team. From my previous launch, I met with Ozan (he is in Adomate team) and he showed me the demo of the Adomate. The most interesting part they are generate creatives by learning from your competitors. I'm planning to use it for my e-commerce business. Hopefully, in near future I can use for my apps too :)
Adomate
@metehan_caliskan Thank you for your input. Initial focus is on DTC and Saas, but apps are definitely a field we further want to explore!
@s_logghe Love the transparency around the no black box approach. I'm curious how do you balance AI suggestions with preserving each brands unique voice especially when using competitor and review data?
Adomate
@vishnu_kant_07 Hi Vishnu, great question. For every brand, we create a "brand brain" that captures its specific tone of voice, visual style, and product features. Users can then choose how closely they want the generated ads to follow their own brand versus staying closer to the original.
Have you seen users discover any surprising creative angles from customer reviews that they probably wouldn't have tested otherwise?
Adomate
@ill_robyn Hi Ill, yes, definitely. We have a template that scans thousands of reviews to find "Golden Nuggets": phrases that use authentic consumer language and are funny, relatable, and genuine: true advertising gold.
I am thinkin this could help brands scale creative production without losing consistency. What reporting features help users understand which research sources influence successful ads the most?
Adomate
@darly_selbyHi Darly, typically a mix of sources performs best. Iterating on your proven winners is a solid strategy, but it doesn't bring much fresh thinking into your creative process. Consumer reviews and proven ads from other brands help increase creative diversity. And drawing inspiration from winning concepts in other industries has proven to be both highly effective and genuinely refreshing. We pull performance stats directly from the Meta ad Manager.