Launching today
Sales teams have been stuck with stale databases for 15 years. Jesse changes everything.. the first internet-wide search engine built for sales & marketing. Ask in plain English: "Find newly opened soccer facilities in the Midwest needing turf solutions." Jesse scans the live web and finds the right buyers in the market today. We are an anti database company, we don’t scrape and store stale databases and sell them at premium. Every lead is found fresh from the live internet and delivered.
Jesse
Hey Product Hunt! 👋 I’m Sudipta, cofounder of Floworks.
Quick intro: I’m an IIT-Kgp grad. My first startup failed — and what stung was the reason. Not the product. We just couldn’t crack sales.
So I started Floworks and got into Y Combinator. Different company, same ghost: sales never got any easier.
We were spending hours building lead lists, only to discover half the people had changed roles, the signals were outdated, and the best accounts were hiding in plain sight.
So today we’re launching something close to my heart: @Jesse — a modern prospecting tool for GTM teams.
Think of it as a search engine, but for finding your next 100 customers.
Apollo and Clay scrape data once, then sell it back at a premium. By the time it reaches you, it’s stale — titles changed, companies pivoted, signals came and went.
Jesse searches the entire live internet on every query and hands you fresh leads.
We’re an anti-database company.
What’s more 👇
Apollo gives you 8–10 filters. Clay makes you wire up data sources. Jesse just works.
Write in plain English:
🔎 “Looking for healthcare providers who just opened chemotherapy centres”
🔎 “Find me 100 kids’ play facilities that just started a football academy and are seeing high footfalls”
Jesse tells you which signals to look for.
Building a fintech for debt collection? Jesse surfaces:
🎯 VPs of Risk at mid-sized NBFCs ($5M–$50M ARR) posting about defaults / hiring underwriters — want real-time alternative credit-reporting APIs
🎯 VPs of Compliance at digital neobanks (50–200 emp), newly appointed, replacing manual KYC/AML with a consolidated real-time API
🎯 CTOs at Series A/B fintechs (100–500 emp) tired of juggling vendor APIs — want one unified identity + credit + AML API
AE with named accounts every quarter?
🕵️ Jesse finds the right people inside those accounts by reading decision-makers’ real online activity — posts, public appearances, forums
A daily barrage of fresh leads, delivered to your inbox every morning. 📥
Since we launched, the adoption has been incredible:
🚀 1.2k+ teams signed up
🌍 From a real estate agent in Florida, to a fintech founder, to 4 Fortune 500 companies and one of the largest logistics companies in the world
📈 50%+ WAU/MAU
🔥 A constant rave across sales communities
These are still early days. I get a ton of messages from Jesse users on what to improve, and I personally read and act on every one.
I’d love your feedback — especially from founders and sales leaders who’ve lived the pipeline grind. What’s the most painful part of finding good leads for you? Happy to answer any questions! 🚀
As someone who has used Jesse first-hand, I can say they are really, really good. I’ve tried many products that promise to use intent signals to build lead lists with just a prompt, but the list quality usually turns out to be quite poor. Most of them feel like LLM wrappers on top of traditional lead databases.
Jesse felt different. They seem to understand real intent and match it with actual buying signals. We used to run email campaigns to hundreds of thousands of leads, mostly with poor reply rates. But when I used Jesse, it clearly helped us identify which leads were actually relevant - allowing us to go much deeper on personalization and get ridiculously high reply rates.
Highly recommended, and congratulations to the team on the launch.
@upendra_varma Thankyou for such honest feedback. Really appreciate your support in growing Jesse. With Jesse we aim to change the way outbound is being done and I can honestly say, it's for sales leaders like you, we have built Jesse.
I would say you should also try out our two flagship features:
1. People Search - To try and identify prospects at a persona level. It helps you find prospects emitting signals around your need
2. Look alike search - First of its kind look alike search built not on static databases but rather on dynamic internet-based signals. This is specially helpful to curate similar looking lists that have already worked for you.
I am sure we will hear more from you as we keep building Jesse. Thanks for all your support
The pitch makes sense if the core problem is list staleness, but I'm curious what "live internet" actually means in practice for sales data specifically. Are you pulling from public web sources in real time, or is there a database that gets refreshed on some cadence? That distinction matters because a lot of "live" tools still have a 30-90 day lag on job changes and funding rounds, which is exactly when the timing-sensitive outreach breaks down. Also wondering how Jesse handles signal prioritization, whether you can filter by something like "title changed in the last two weeks" versus just searching by current role.
@fberrez1
Good question, and the distinction matters.
We runs live, grounded web research at query time.
It is not a pre-built database that goes stale between refreshes.
When you run a search it goes out to public sources right then and reasons over what is currently there, so there is no fixed 30 to 90 day snapshot lag baked in.
An honest caveat:
Freshness is bounded by what has actually gone public.
A funding round or role change that has hit the web (news, company site, LinkedIn) is catchable as soon as it surfaces. Anything not yet public, we will not invent.
On prioritization:
Today you encode recency in the criteria itself, for example "VP of Sales who recently changed roles at Series B SaaS," and the search pulls on whatever recency signals are publicly visible rather than just matching a current title.
A built-in recency filter, like a "changed role in the last two weeks" toggle, is what we are building next, so time-sensitive outreach runs off a real signal instead of how you worded the search.
Congrats on the launch! The biggest pain point with tools like Apollo or Clay has always been how quickly the data decays. Doing live web research at query time sounds like a game-changer for timing-sensitive outreach. Looking forward to taking this for a spin! Do you have any n8n/MCP integration to be used with Clade code?
Jesse
@kevin We are live on n8n as an approved template. MCP coming next week. API is already live for those who want to integrate in their own application.
@kevin Hey Kevin, thanks for hunting us on ProductHunt. Great suggestion, as @sudipta_biswas4 mentioned, we have gone live with both the n8n/MCP integrations. This is especially important for us as most of the modern GTM teams are building their stack on Claudecode and this way they can just plug-and-play with Jesse.
We are also planning to launch direct integrations with other outbound tools like:
1. Alisha - the outbound engine of Floworks
2. Instantly
3. SendGrid
And CRMs of the like of Salesforce, Attio and Hubspot for smoother transition of leads in your GTM flow.
Please let us know if you think any other workflows could be of interest. We would love to look into them.
This would solve so many problems in prospecting! I normally have to go over multiple surfaces (LinkedIn / X / funding announcements) to find my target customer! What data sources do you use?
@prasoon_shukla2
That cross-surface hunt is exactly the manual work Jesse is built to collapse. Instead of one fixed list, it reads the open web at query time across the surfaces you named, public profiles, social, news and funding coverage, company sites, and synthesizes them into a single ranked result with the reasoning attached.
On top of that we layer a dedicated contact enrichment step for the email and outreach details.
In short: It reads the live public web fresh on every search, instead of pulling from one fixed database. That is the whole point, the cross-surface digging you do by hand now happens in a single search.
Jesse
@prasoon_shukla2 Essentially, anything and everything on the open internet (including forums like X, LinkedIn) are accessed by Jesse.
Nektar
Cool product. Do you integrate with a CRM directly and can find leads based on winning deals signals/painpoints/characteristics?
@abhijeet_vijayvergiya
Thanks. Two parts to this.
On winning-deal signals: yes, this is core. Hand Jesse a set of your closed-won accounts and it runs a lookalike search, finding companies that match the characteristics of what already wins for you, and you can fold pain points and traits straight into the criteria. So "more like my best deals" is exactly a usecase we solve for.
On CRM: not a native sync yet. Today you get leads in via CSV, or through our new n8n integration using n8n's HubSpot and Salesforce nodes. A direct CRM connection, pushing leads out and pulling winning-deal signals in automatically, is what we are building next.
Which CRM are you on? That helps us prioritize the native integration.
Most of our buyers are pretty active online, so this should work well. But how Jesse handles signal quality when the web footprint is thin like a company that exists but rarely posts. Does it still rank them, or filter them out?
@amanpreet_zop
Good edge to probe.
Jesse ranks rather than hard filters, so a real but quiet company still shows up as long as there is enough public signal to confirm the match.
A thin footprint usually just means a lower score and a shorter rationale, not exclusion, and every result carries the evidence we actually found plus a source, so you can judge the borderline ones yourself instead of us dropping them silently.
The honest boundary:
If a company has effectively no public trace, there is nothing to ground on, so it will not surface, and we will not pad the list with guesses to hide that.
If they are quiet online, no problem. If there is nothing online at all, that is the only case we cannot find them.