Gojiberry AI helps B2B teams find and contact leads who are showing buying intent.
Instead of scraping cold lists, it tracks real-time signals like LinkedIn likes, comments, job changes, and funding rounds to detect when someone is ready to buy.
With one prompt, Gojiberry builds your ideal lead list, enriches every contact, and generates personalized outreach messages.
Teams using Gojiberry get 3–5x more replies, close deals faster, and stop wasting time on people who aren’t interested.
This is the 3rd launch from Gojiberry AI . View more
GojiberryAI
Launching today
Stop blasting cold lists. Gojiberry detects intent signals, finds warm prospects, and personalizes LinkedIn outreach end-to-end—so you can track which signals convert into real conversations.
Free Options
Launch Team
Gojiberry AI
Hey Product Hunt 👋
I’m Romàn, co-founder of GojiberryAI
We built Gojiberry because outbound is broken.
Founders and small sales teams waste hours:
• Scraping random leads
• Sending generic “Hey {{first_name}}” messages
• Guessing who might be interested
• Burning accounts with bad automation
And the worst part? Most of those people were never ready to buy.
So instead of automating spam… we decided to automate intent.
GojiberryAI is an AI GTM Brain that:
→ Detects high-intent buying signals (profile views, job changes, funding, competitor engagement, content interactions)
→ Enriches and qualifies leads automatically
→ Generates hyper-personalized LinkedIn conversations
→ Centralizes everything in one inbox
→ Lets you fine-tune campaigns with an AI Co-Pilot
Unlike traditional outreach tools that focus on volume, we focus on signal first, message second.
The result:
• Higher reply rates
• More booked demos
• Less manual work
• No copy-paste templates
We’ve used this system to generate hundreds of conversations and scale from 0 to $1M ARR, and now we’re opening it to the PH community.
🎁 Product Hunt Special
We’re offering an exclusive launch discount for the PH community.
Use code: PH10 for 10% off your first month.
We’ll be here all day answering questions, sharing our exact stack, and being fully transparent about what works (and what doesn’t).
Thanks for checking us out, excited to hear your feedback 🚀
@roman_cz Hi Roman. This is amazing. Congratulations on launching! What’s the architecture behind the AI agents? How do they research, enrich, and act on leads automatically?
Gojiberry AI
@kimberly_ross I'm not the technical guy, I'll let @dylan_teixeira respond :)
@kimberly_ross @dylan_teixeira @roman_cz Hi Kimberley, that's our secret sauce :) Just kidding. We've developed our AI agents that search for prospects on the web, then we assign them a double score based on the typical user profile (with ChatGPT, & Claude API). Next, we use third-party tools to enrich the prospect via a smart waterfall.
False positives are the killer with intent tools. If Gojiberry is catching job changes and competitor engagement, having the AI Co-Pilot show the signal trail and conversion by signal helps reps trust it. I'd add pacing limits and a human approval queue in the central inbox so account health doesn't become the hidden cost. That's how signal first sticks.
Gojiberry AI
@piroune_balachandran Absolutely ! I could not say it better.
Easy Save AI
@roman_cz congratulations on the success of the launch
This is solving a real pain point. Cold outreach has always felt like shouting into a void, the intent signal approach makes so much more sense.
Curious about one thing, how do you handle signal accuracy? Like if someone liked a LinkedIn post about "sales tools," does Gojiberry flag them as a buyer, or is there more filtering happening behind the scenes to reduce false positives?
Also the "one prompt to build a lead list" UX sounds really clean. Is that powered by a custom model or GPT-based?
Congrats on the launch, B2B teams are going to love this!
Gojiberry AI
@alamenigma Love this question, that’s exactly the right skepticism to have.
On signal accuracy 👇
We don’t treat a single lightweight action (like one random “sales tools” like) as buying intent.
There’s a big difference between:
Weak signal
Someone liked a broad industry post once.
Strong signal
Someone repeatedly engages with a direct competitor, comments on implementation content, recently changed into a relevant role, and works at a company hiring for that function.
Gojiberry scores signals based on:
• Specificity (generic topic vs competitor-level engagement)
• Frequency (one interaction vs repeated behavior)
• Context (persona + company ICP fit)
• Recency (fresh signals weigh more)
• Signal stacking (multiple signals compound the score)
So instead of binary “buyer / not buyer”, it’s probabilistic. A single soft action won’t trigger outreach unless it aligns with strong ICP fit and other reinforcing signals.
That’s how we reduce false positives and avoid the “everyone who liked SaaS = hot lead” trap.
On the “one prompt lead list” UX 👇
It’s not just raw GPT on top of a database.
We combine:
• Structured data filters
• Signal engine
• ICP modeling layer
• Then an LLM layer to translate natural language into structured queries + refine targeting
So when you type something like:
“AI-native B2B SaaS hiring for sales and engaging with Apollo”
It gets converted into a multi-layer rule set behind the scenes.
The LLM is the interface.
The intelligence is in the signal + scoring engine.
Really appreciate the thoughtful questions, this is exactly the type of detail B2B teams care about.
How does it actually detect high-intent buying signals? Curious to use it. Congratulations on the launch, @roman_cz!
Gojiberry AI
@neilverma Really appreciate it 🙏
We don’t rely on generic “AI guesses.” We track real, observable intent signals across LinkedIn and company-level events.
Concretely, we detect things like:
• People engaging with your competitors (likes, comments, follows)
• Prospects viewing your profile multiple times
• Job changes into relevant roles
• Companies hiring for roles that indicate budget or need
• Funding events
• Tech stack signals
• Trigger events tied to your ICP
Then we layer that with:
ICP matching (persona + company fit)
Signal strength (how strong the buying intent actually is)
Recency (how fresh the signal is)
Each lead gets an AI score based on those combined factors, so you’re only reaching out when timing + fit + intent align.
That’s why reply rates are dramatically higher compared to cold lists.
Hey @roman_cz congrats on your launch and great product! Does this primarily only work with LinkedIn and would this require Sales Navigator enabled to enable DMs to people with intent but not yet in your network?
Gojiberry AI
@jerrybyday Thanks a lot ! You don't need SalesNavigator to use Gojiberry AI.
Congrats guys! With your experience from gojiberry, what’s one GTM lesson you’ve learned? And what’s the inspiration behind the name “Gojiberry”?! It sounds sweet, I like it! lol
Gojiberry AI
@jacklyn_i The one lesson I have learnt is that intent always beats volume !
Flexprice
High reply rates are good, but pipeline quality is what matters. Have you seen improvements in close rates too, or mainly top-of-funnel lift?
Gojiberry AI
@shreya_chaurasia19 Yes, with intent, the closing rates are way, way better.
This is super interesting focusing on intent over volume makes a lot of sense
Curious — with tracking signals from platforms like LinkedIn and automating outreach, how are you handling things like data privacy and account safety?
Especially avoiding issues like platform restrictions or unintended access risks
Gojiberry AI
@shrujal_mandawkar1 At Gojiberry AI, we value privacy.
We have implemented many security safeguards to keep your account safe, such as limits on message volume and connection requests.
Regarding data privacy, we do not store your password. It is securely stored in the cloud.