Launching today
Pebbles Ai
AI sales platform for modern B2B teams
420 followers
AI sales platform for modern B2B teams
420 followers
The only GTM orchestration platform you will need to successfully take your products & services to market. Pebbles AI is a Go-To-Market Operating System built for B2B revenue teams. It brings strategy, lead generation, outreach, sales, & shared company knowledge into one AI-powered workspace. Using neurosymbolic AI trained on your business, it helps teams plan campaigns, personalize outreach, generate qualified leads, & execute without switching between disconnected GTM tools.
Congratulations on a launch! I have used a similar project before, but it had very limited features. You look very solid and look exactly like what I need.
P.S. Dashboard looks nice, but some extra "llm" prompts showed me a bit confused. Like asking for ICP and then fetching from the data we already gave
Pebbles Ai
@spiri7 Thank you, that really means a lot. And congrats right back for being an innovator-minded person that likes to try novel things.
Hearing it looks like exactly what you need, especially after a thinner tool let you down before, is the best thing I could read this afternoon.
Now, your P.S. That's the most valuable part of your whole comment, so thank you for flagging it. Honest UX feedback like this is how the product actually gets sharper.
You're right that it can feel like doubling up. There is a reason behind it: the ICP step is about sharpening who you target, which is subtly different from the raw company data you gave us. But if it read as "didn't you just ask me this," that's a gap on our side to close, not yours to decode.
So here's what I'd love to do. Let me give you a 30 min walkthrough. I'll show you the tips and tricks, why a few steps exist, and how to get the system really flying for your company.
Would you like that?
For small B2B teams, the sales-agent win is usually handoff quality rather than full autonomy: what changed, what evidence supports it, what needs human judgment, and what should never be sent without approval.
Pebbles Ai
Great question Patrick!!! Love when technologists go deep.
For small B2B teams, full autonomy is mostly a pitch-deck fantasy today, and the reason is arithmetic, not opinion.
Agent errors compound multiplicatively across steps. At 95% reliability per step, a 10-step task lands around 60% overall (0.95^10). Even at an optimistic 99% per step, a 20-step chain only succeeds ~82% of the time.
Empirically it's worse: in Carnegie Mellon's TheAgentCompany benchmark, even the strongest model completed only ~30% of realistic, multi-step office tasks end-to-end (earlier runs landed at 24%).
And the market is pricing it in. Gartner expects over 40% of agentic AI projects to be canceled by the end of 2027, citing unclear value and inadequate risk controls, and notes today's models can't reliably follow nuanced instructions over long horizons.
People are frustrated, and frankly, when I talk to customers, angry!
So the winning pattern isn't more autonomy. It's selective autonomy: aggressive automation on low-stakes steps, hard human gates on high-stakes ones, with escalation paths that cap the blast radius. Which is precisely the handoff quality you're describing.
You framed these four around the sales agent, but they're really the design spec for the whole platform, strategy, marketing and sales alike. So here's how each one works at that level:
What changed?
Whatever the surface, a market shift the strategy core detects, a competitor move, a new buying signal, an inbound reply, the platform isolates the delta and leads with it. You get the one thing that moved and why it matters to YOU, not a raw feed you have to reverse-engineer. Same behaviour whether it's your positioning or your pipeline.
What evidence supports it?
Every recommendation carries its reasoning, a strategic call, a beachhead segment, a line of outbound copy. This is where neurosymbolic earns its place over a raw LLM: the symbolic layer keeps each inference traceable back to the rule or data point that produced it, so a conclusion arrives with its "why" attached rather than as an unexplained assertion from a black box. Inspectable reasoning like that is what the research points to as the precondition for trustworthy human-in-the-loop oversight.
What needs human judgment?
Whether it's a strategy decision, a targeting call, or an outbound message, when confidence is low (we have "judges") or the call is genuinely ambiguous the platform surfaces that explicitly and routes it to you, instead of papering over uncertainty with a confident-sounding guess. Overconfidence in failure is one of the documented ways agents break, so we treat "I'm not sure, your call" as a feature, not a bug.
What should never be sent without approval?
Anything irreversible or externally facing, a published campaign, an outbound send, an investor or sales asset going out the door, is gated by default. The platform can research, reason, write, sequence and prepare the whole thing, but the irreversible action stays with a human until you explicitly choose to loosen it.
Notice the common thread: every one of those four depends on reasoning you can actually trust, not a confident guess. That trust is the exact thing the arithmetic up top says nobody has yet, it's why 40% of these projects get canceled. So the real question is whether our engine is any different. Here are the numbers.
How does our neurosymbolic actually perform?
A base LLM is inherently a chatbot system. It predicts the most probable next word, brilliantly, but that is not enough for complex domains such as B2B GTM, Corporate Law, and Human Resources. Nothing in it stops to ask "is this true, and does it obey the domain rules".
Neurosymbolic AI is 3-step systems working together, with a check between them:
The neural half reads language and context, the way any strong LLM does
The symbolic half applies explicit logic, rules and a structured knowledge base
A verification step sits between that and the output, catching anything that breaks
So you get the fluency of an LLM with a reasoning and fact checking layer bolted underneath. Outputs are accurate, reproducible and explainable (even auditable) rather than a confident black box.
It is also rare: academic interest went from 112 papers in 2015 and 2016 to over 9,000 in 2025 and 2026 [Google Scholar], yet real production systems are almost nowhere, because building one needs machine learning, formal logic, knowledge engineering and domain science in the same room at once.
Let's look at some numbers. First, let's compare Claude Opus on the Max tier with a single instruction against the full Pebbles pipeline:
Accuracy: 33% vs 87%
Precision: 57% vs 91%
Sales Efficacy, MQL to SQL: 15% vs 85%
And at the architecture level:
82% lower error rate than LoRA fine tuning, because the architecture is structurally accurate rather than nudged
3x better gross margin than wrappers, because the reasoning is not rederived from scratch on every call
~2% hallucination on rule bound queries, versus 31.4% across real world use HalluScore benchmark
But don't take my word for it: Claude Opus sits around 33% factual hallucination on the public HalluScore benchmark, while generally neurosymbolic methods approach ~100% accuracy on rule based tasks [arXiv 2502.01657]. We are closing up at 85-98%.
Cost is where it gets almost silly. To rebuild one reply with a raw model:
Around 12 prompts per reply, each re-sending 25,000 to 35,000 tokens of context
Roughly $5 to $7 per usable output, and that's not even top 1% reasoning
About $5,000 to $7,000 a month at that volume, before 500 hours of human prompting
THIS MEANS WE CAN GIVE MUCH MORE AI ALLOWANCE TO OUR USERS!!! 🫶🏻❤️📈
Finally, the neurosymbolic reasoning is what lets it carry complex, multi-faceted, and cross-functional B2B work all the way through. These are examples you can build and execute on, which is impossible with base-models or tools with wrappers:
A full go to market strategy, grounded in your ICP, positioning and live market signals, then turned into the campaigns that run it
A beachhead strategy, picking the wedge segment worth attacking first, sizing it, and sequencing the entry instead of guessing
Industry trend analysis read across macro, meso and micro signals, so you see the shift before it hits your pipeline
Investor decks and enterprise sales assets, two pagers, RFPs and proposals that hold up when a sharp reader pushes on them
An omnichannel outbound engine, from fresh leads to reasoned email and LinkedIn sequences to replies captured and qualified in Smartbox, built and run end to end
And this is only the current stage. We are making the first steps toward a true Jarvis for go to market: a system that can safely, securely and reliably run the work fully autonomously, with no human in the loop.
I hope that makes sense.
Pebbles Ai
Thank you for stopping by our Pebbles Ai page. This is our story.
Three years ago, we set out to build something the market simply didn't have. We did it the hard way.
We built the brain first, the neurosymbolic AI, then wrapped the operating system and the workflows around it. Everyone else builds the car and forgets the engine. We built the engine, then realised we should probably add doors.
We didn't wrap a general-purpose LLM in a logo and call it a Series A. We refused. To make things more difficult, we built it across two cities: London (UK) and Lviv (Ukraine).
If you've been following what's happening in Ukraine, you know what that means. Our team has built the smartest GTM solution on earth through air raid sirens, blackouts, and nights filled with whistling ballistic missiles.
They never stopped shipping.
Honestly, the team jokes that the war wasn't even the hard part. Building something this new, this wide, and this deep all at once, an entire operating system grounded in empirical science, from scratch, that was the truly terrifying bit. The missiles were just the background music.
So why would you put yourself through all of that?
In hindsight, therapy would have been cheaper. But less scalable.
Because the commercial game is rigged. Enterprise companies hoard the best talent out of universities, pay obscene salaries, and keep the most advanced tools for themselves.
The rest of us? Mere peasants in a story of oligopoly kings.
The result is the erosion of small and mid-sized businesses. And just like the erosion of the middle class, that's bad for capitalism, bad for the economy, and frankly bad for democracy.
So we wanted to level the playing field. To give David the slingshot he deserved, our technology, so he can take Goliath down a peg and steal real market share without breaking the bank.
We're not throwing stones. We're slinging lethal pebbles at Mach 3 speed.
That's the whole point. Enterprise-grade go-to-market firepower, in the hands of the people with big ambitions and great products.
So how do I know you won't just burn my money?
We're not the startup that raised millions and blew it on ping-pong tables and Super Bowl ads.
We raised millions and spent it on the least glamorous thing imaginable: making it actually work. 87% went straight into the technology. The team took pay cuts. The founding team forfeited salary for three years. Our accountant thinks we're a charity.
Because we'd rather give the world something that actually works. This was never going to be easy, and we needed every dollar in the tech.
So what did you actually build?
The world's first Go-To-Market Operating System (GTMOS™), powered by 8 neurosymbolic AI cores, where management, marketing, and sales all work together in one workspace.
We went wide: one platform that replaces 10+ tools. And we went deep: real reasoning under every feature. One stack to replace them all. Sauron would be jealous of the licensing savings.
Every capability is custom-built for precision, accuracy, and commercial efficacy. And like real departments, they talk to each other.
So you can replace your whole stack with one GTMOS, pay less than you do today, and get an intelligence that understands your company better over time.
One last thing. Your assistant isn't built to agree with you. It's built to make you succeed. It will push back. It won't stroke your ego, because it can't stroke your ego and make you win at the same time.
It only cares about your success, not your feelings. Refreshing, we know. These are autonomous, top 1% domain-expert thinkers, built to guide you through the maze of go-to-market.
So who's actually behind this?
I'm endlessly proud of the people who built this, many of them while their country is at war:
Dmytro Antoniuk, our CAIO @dima_antoniuk
Maksym Kuzmovych, Head of Engineering @kuzmovych
Priyanka Mandal, Head of Community @priyankamandal
And thank you to every early adopter showing interest in Pebbles Ai. I'm truly humbled, and I appreciate your time and intellectual curiosity.
Go sign up, it's free. Built for people with big ambitions and small teams. Emphasis on small. Push the system hard. Tell us what you love, tell us what you want more of, and help us shape it around you.
💛 If you're an entrepreneur: just follow and message me. I will personally give you a 30-minute demo and show you how Pebbles genuinely improves your professional life, strengthens your team, and transforms your company.
Whether you are an early-stage startup of two co-founders or a mid-market organisation with 1,000+ employees, you will see how science and technology applied to GTM can move the commercial needle without breaking the bank.
Or, alternatively, if you would rather not talk to anyone:
Head to Pebbles Ai and discover it without any cost ("start free" button)
No credit card required. A generous AI allowance. Up to 3 seats included
And 3,000 fresh leads to get you started!!!!!!!!!
🚀 PRODUCT HUNT EXCLUSIVE. 24 HOURS ONLY.
Pebbles Ai
For investors (VCs/Angels) we are incidentally also raising our first institutional round (Seed). Get in touch!
I like the focus on reducing GTM tool sprawl. Sales and marketing teams often spend as much time moving context between tools as they do actually engaging prospects.
Pebbles Ai
@varun1jan Exactly, that context switching tax is a huge hidden cost for GTM teams.
Pebbles Ai
@varun1jan Exactly, Varun. The hidden tax in most stacks is not the software, it is the human hours lost carrying context between tools that refuse to talk to each other. Even though, that is also very costly for a big company.
A better way is one operating system, one shared context, a neurosymbolic brain underneath. Your strategy, leads, and outreach all read from the same brain, so the rep spends the time on the prospect instead of playing courier between 10 tabs.
Assistants think. LeadGen hunts. Auto SDR warms. SmartBox closes. Every other feature on the OS amplifies.
The idea sounds great. The question is - how smart it would be in the question of personalisation.
Pebbles Ai
@julia_shtogren Great question. And a designer's eye would land on exactly this, because bad personalisation is obviously cringe.
The short version: our personalisation goes both wide and deep.
Wide means we look at every angle of your prospect before writing a word:
The marketing angle: what message actually resonates with them?
The sales angle: where's the commercial fit, the pain we can solve?
The human angle: who is this person, really, beyond the job title?
Deep means we apply proper sales methodology, mapping the impact of your solution at three levels:
The organisation: what does this mean for their company?
The team: what does it change for their unit's goals?
The individual: what's in it for this specific person?
But here's the important bit: none of that sits on its own.
We fuse it with true hyper-personalisation. Our systems work out what's actually top of mind for your prospect right now, what they've been posting about, what they care about, who they are.
Then we use science-based methods to find genuine common ground, the shared interest or the icebreaker that actually starts a real conversation.
I hope that make sense.
Lancepilot
👋 Excited to hunt Pebbles AI today!
One thing I've noticed from talking to founders is that go-to-market often becomes a patchwork of disconnected tools. One for strategy, another for outreach, another for lead generation, another for documentation... and before long, the workflow becomes harder to manage than the work itself.
Pebbles AI takes a different approach.
Instead of adding another AI tool to the stack, it brings strategy, lead generation, outreach, sales, and team knowledge together in a single AI-powered Go-To-Market Operating System. What stood out to me is that it's built around how modern GTM teams actually work, helping them move from planning to execution without constantly switching between tools.
The team has also built Pebbles on neurosymbolic AI, allowing it to reason using your company's knowledge instead of producing generic outputs.
If you're a founder, marketer, or part of a GTM team, I'd love to hear:
What's the biggest bottleneck in your go-to-market process today?
✅ The makers are here throughout the day and would genuinely appreciate your thoughts and feedback. Looking forward to hearing what everyone thinks!
@emincanturan @dima_antoniuk @priyankamandal
Pebbles Ai
@dima_antoniuk @priyankamandal @istiakahmad :
🙏 This means a lot, thank you for hunting us.
You've articulated the problem better than we usually do. GTM becoming a patchwork where managing the tools costs more than doing the work, that's the exact frustration we built Pebbles to remove.
And you nailed the why behind the neurosymbolic bit. It reasons from your company's knowledge instead of spraying generic output. That's the whole difference.
To throw your question back to the room, because it's a good one:
What's the single biggest bottleneck in your own GTM right now?
We built the demand through our own platform, but had no resources to keep up with it. Too much inbound.
We ran a great campaign on our own platform and the positive replies flooded in. We hired and let go 5 people in a year attempting to keep up. Alas, to no avail.
The SDR function is broken, and even we couldn't fix it. The real gap: business netiquette, critical thinking, creative problem solving, consistency, and execution speed. Though it is not their fault. Hardly anyone trains SDRs. The industry turned ruthless. Universities do not prepare them. They are set up to fail.
So we found another way to save our own sales pipeline, and that of our customers.
What actually went wrong:
❌ One SDR quietly did not touch the inboxes for 2.5 months, around five LinkedIn and 10 outbound email inboxes. Campaigns and inside sales looked great, but nothing trickled down. We only found out when we went looking for why
❌ The SDR is the first human contact in the company. We watched roughly 20 interested, ready-to-pay prospects get so put off by the replies that they walked. Essentially a first-impression problem.
Here's the before and after building
🐖 Total costs
• Before: ~$120k+/yr | 3-4 SDRs / Jr. Sales Managers plus a manager
• With Auto SDR: $0 + $350/mo Team subscription (~$4,200/yr, zero SDRs hired)
💸 Pipeline value
• Before: -$32k/yr lost | ~90% of quota missed, warm replies rotted
• With Auto SDR: Confidential, ~7x more gained | demand caught, founders on the calls
🧠 Sales Efficacy
• Before: ~10 SQLs/mo | ~30 MQLs at ~32% MQL→SQL
• With Auto SDR: ~22 SQLs/mo | same demand at ~70%
⚡ Reply time
• Before: Hours, or never | the 2.5-month blackout
• With Auto SDR: 3-7 min | 24/7, every inbox
📈 Net margin
• Before: Confidential | baseline, if we had staffed it
• With Auto SDR: Confidential, ~1.9x | running Auto SDR
The "getting your first customers" problem is painfully real. as a founder, market research, lead sourcing, outreach, follow-ups, and messaging can quickly become five different tools with five different versions of the company context.
The most interesting part here is the neurosymbolic approach and the promise that Pebbles learns the business instead of making teams explain it again in every workflow. Curious how much of the GTM plan is generated from company data versus fixed playbooks, and how clearly users can inspect why a lead, segment, or campaign was recommended.