Influcio replaces one-off influencer campaigns with a self-learning AI system. It finds the best influencers, runs campaigns end-to-end, helps you manage them in an all-in-one platform, and uses performance data to optimize every next launch.
✨ “Unlock your business momentum with trackable AI-powered campaigns”
👋 Hey Product Hunt! I’m the PM at Influcio.
We realized the real challenge in influencer marketing isn’t execution—it’s strategy. Most teams don’t know how to go from an idea to a campaign that actually performs. Tools give filters, agencies give opinions—but there’s no repeatable, data-driven way to figure it out.
So we built Aria, our AI CMO. It turns rough ideas into structured strategies, finds the right creators, and continuously improves using real campaign data—so every launch gets smarter.
Excited to hear your thoughts and feedback 🙌 — Alice
Report
@alice_zeng For bootstrapped founders with tight budgets, how does it recommend creators that punch above their follower weight for max ROI?
Instead of optimizing for follower count, Influcio ranks creators based on conversion signals: things like content velocity, audience interaction depth (not just likes), and historical performance patterns across similar campaigns.
For tight budgets, we’ll often recommend a portfolio of smaller creators instead of one big bet — it de-risks spend and compounds learning faster. :)
Huge congrats @alice_zeng and team! Love the product the vision.
Curious:
How do you differentiate vs. other comps like Clikq (also ugc) and Helena by Enrich Labs (ai cmo)?
What’s your creator pool? Do you ‘own’ them?
Any findings from all the campaigns you have ran? What budget works best? What category / sector should run ugc more?
Report
Maker
Most tools either: (1) help you find creators, or (2) give you strategy slides
We don’t stop at discovery — we go from strategy → creator selection → content → performance → iteration
Report
The 'AI CMO' framing is interesting — most tools stop at discovery and leave the strategy gap wide open. As an indie maker about to launch my first app solo, I'm curious: is Influcio built for teams with existing campaign budgets, or can it work for someone starting from scratch with zero influencer experience and a tight budget?
Report
Maker
@misbah_abdel We work with clients from 0 to 1 and from 1 to 100! :)
@jiaqichen Our AI CMO Aria will search and recommend influencers that match influencer persona given by our clients. Recommended influencers are picked from our data base (4M+ influencers).
Report
The self-learning part is interesting. Does Aria actually get better at picking influencers over time based on your past campaigns, or is it more like it uses general data from all users?
@abhra_das1 Yes! Aria gets better and better for picking right influencers. She gradually gains more knowledge about customer's preference from different industries. The past cases can always bring more and more insight to Aria.
Report
Happy launch!
The self-learning system sounds like the holy grail for this. How does it weigh different conversion events when it's optimizing?
Report
"self-learning" is doing a lot of work here - learning on what signal? influencer performance attribution is notoriously hard. curious how you handle that before the system learns anything useful.
Influcio
✨ “Unlock your business momentum with trackable AI-powered campaigns”
👋 Hey Product Hunt! I’m the PM at Influcio.
We realized the real challenge in influencer marketing isn’t execution—it’s strategy. Most teams don’t know how to go from an idea to a campaign that actually performs. Tools give filters, agencies give opinions—but there’s no repeatable, data-driven way to figure it out.
So we built Aria, our AI CMO. It turns rough ideas into structured strategies, finds the right creators, and continuously improves using real campaign data—so every launch gets smarter.
Excited to hear your thoughts and feedback 🙌
— Alice
@alice_zeng For bootstrapped founders with tight budgets, how does it recommend creators that punch above their follower weight for max ROI?
@alice_zeng @swati_paliwal
Instead of optimizing for follower count, Influcio ranks creators based on conversion signals: things like content velocity, audience interaction depth (not just likes), and historical performance patterns across similar campaigns.
For tight budgets, we’ll often recommend a portfolio of smaller creators instead of one big bet — it de-risks spend and compounds learning faster. :)
Timelaps
Huge congrats @alice_zeng and team! Love the product the vision.
Curious:
How do you differentiate vs. other comps like Clikq (also ugc) and Helena by Enrich Labs (ai cmo)?
What’s your creator pool? Do you ‘own’ them?
Any findings from all the campaigns you have ran? What budget works best? What category / sector should run ugc more?
Most tools either: (1) help you find creators, or (2) give you strategy slides
We don’t stop at discovery — we go from strategy → creator selection → content → performance → iteration
The 'AI CMO' framing is interesting — most tools stop at discovery and leave the strategy gap wide open. As an indie maker about to launch my first app solo, I'm curious: is Influcio built for teams with existing campaign budgets, or can it work for someone starting from scratch with zero influencer experience and a tight budget?
@misbah_abdel We work with clients from 0 to 1 and from 1 to 100! :)
InsForge
How does the AI ensure the recommended influencers align with my brand’s values and tone?
Influcio
@jiaqichen Our AI CMO Aria will search and recommend influencers that match influencer persona given by our clients. Recommended influencers are picked from our data base (4M+ influencers).
Influcio
@abhra_das1 Yes! Aria gets better and better for picking right influencers. She gradually gains more knowledge about customer's preference from different industries. The past cases can always bring more and more insight to Aria.
Happy launch!
The self-learning system sounds like the holy grail for this. How does it weigh different conversion events when it's optimizing?
"self-learning" is doing a lot of work here - learning on what signal? influencer performance attribution is notoriously hard. curious how you handle that before the system learns anything useful.