Pebbles Ai
AI sales platform for modern B2B teams
701 followers
AI sales platform for modern B2B teams
701 followers
More pipeline, faster deals, fatter margins. Pebbles Ai is a Go-to-Market Operating System™ that changes how commercial teams work. One place replacing 10+ tools powered by neurosymbolic AI. Whether you're two co-founders, a 10-person startup, or a 100 strong company, Pebbles Ai truly moves the commercial needle without breaking the bank. Generate strategies grounded in science. Create content that earns attention. Launch campaigns infused with persuasion. Build sales assets that close deals.
Pebbles Ai
Hey Product Hunt! 🙋🏻♂️
My name is Dmytro Antoniuk, and I'm the Chief AI Officer at Pebbles Ai. I'm incredibly excited to introduce Pebbles Ai to the community today.
What are we solving?
Getting your first customers is the hardest part. You instantly get pulled in many different directions: market research, finding target leads, writing cold outreach, and managing follow-ups.
Before you know it, your day is swallowed by a chaotic pile of disconnected AI tools. Teams end up wasting time switching between tabs, losing context, and burning budgets on a dozen different subscriptions just to align sales and marketing. It shouldn't feel this fragmented, and it definitely shouldn't feel this overwhelming.
We built Pebbles Ai to collapse that heavy, expensive stack into one secure, unified workspace for your entire growth team.
Who It's For?
B2B growth teams and professionals across sales, marketing, and RevOps trying to land their first or next customers.
What is the solution?
Pebbles Ai brings your strategy, audience targeting, and outreach into a single, connected loop. It combines advanced neurosymbolic AI with real B2B growth expertise so teams can work together in one place.
Learns your product and tone of voice from your brand docs, so you never re-explain your business
Reads B2B market signals to find your ideal customers and split them into clear segments
Acts like an in-house strategy consultancy and content agency in one
Crafts multi-touch campaigns tailored to each segment automatically
Keeps your data private in tenant-isolated architecture (CASA II certified)
We are live in the comments all day! Check out trypebbles.ai, give it a spin with your team, and let us know your thoughts. We would deeply appreciate your support and feedback today! 🚀
@dima_antoniuk On the buyer side, most "AI sales platform" demos fall apart the moment they hit a messy CRM and a sales cycle that doesn't match the template. How much manual mapping does it take before the AI is actually useful, and does it augment the reps' judgment or try to own the first touch?
Pebbles Ai
@dima_antoniuk @artem_fedorovich
Good question! Most tools bolt AI onto a messy CRM, assume it is clean, and fall over on contact. We went a different way.
Pebbles does not depend on you fixing your CRM first. Fresh Leads brings its own enriched data through a three provider waterfall, so the AI is useful on day one rather than after a mapping marathon.
The reasoning layer also reasons about your actual motion, so a long, unusual cycle becomes the input it plans around, rather than the thing that breaks it.
On your second question, it augments. The assistants are built to make you win, so they push back rather than flatter, and the rep keeps the final call.
It's the Dutch-direct approach. We cannot stroke your ego AND make you successful. Choose one. We choose to build an honest, science-led approach that ACTUALLY moves the (commercial) needle.
Where it does act on its own, it is deliberately narrow. Auto SDR takes the first touch only on inbound, once someone has already raised their hand. It qualifies and warms that reply in three to seven minutes, then hands a human a ready conversation.
It never cold blasts strangers, and it never gets to close. Farming, not hunting.
Happy to show you on a live demo.
Interested?
@dima_antoniuk congrats on the launch Dima. Tell me, can Pebbles trace the underperforming campaigns back to a specific assumption: ICP, segment, offer, channel etc?
Pebbles Ai
@zolani_matebese Thank you very much, this launch means a lot for us as a team.
We not only provide the unified chat interface with UI/UX enhanced experience for human in the loop activities to delegate the work to be done to Assistants on the platform. They can have access to out communication channels and evaluate the performance and pick-up proactively to keep warm leads warm increasing the overall campaign success.
The other way around there is a possibility to qualify your strategy and the direction with Strategy Assistant that have access to high quality GTM knowledge base constructed from years of experience in the domain. This is what we use for our product to handle GTM needs and be able to compete with much bigger teams.
Pebbles Ai
@zolani_matebese Thanks Zolani, and good to see you here. Short answer, yes. That is exactly what it does.
Most tools tell you a campaign flopped. They stop at the what. Pebbles is built to find the why, and the why almost always lives in an assumption you made before a single email went out.
A campaign is really a stack of bets: this ICP, this segment, this offer, this channel, this message. When it underperforms, the Strategy Assistant works backwards through that stack to find which bet was wrong, rather than blaming the subject line and moving on.
In practice it separates the layers. Good opens and no replies points at the offer or the value proposition, not delivery.
Strong replies from the wrong titles points at the ICP or the segment
One channel flat while another sings points at potential channel fit
Same message landing in one segment and dying in another points at positioning
It isolates the variable instead of lumping it all into "that one did not work".
We use a science-based, domain-expertise led approach with all performance audit and data analysis requests.
@dima_antoniuk I've been using the app, and finally I can stay on one tab and be focused
Only one piece of improvement from my side is the navigation bar. It feels too massive, and at first I got lost in it. Over time, I got used to it and knew where to press without thinking, but for new users that many options at once may be overwhelming. It would be great to optimise it, so the navbar feels more concise, simpler, and clearer.
@dima_antoniuk Nice timing, man. Feels like a lot of teams are hitting this exact problem right now.
Pebbles Ai
@rchornovol appreciate it, thank you!
Pebbles Ai
@dima_antoniuk @sasha_buratynskyi All of them, except for a handful of exceptions. The solution doesn't work for agriculture companies, defence tech, and government.
Lancepilot
Pebbles Ai
@raihanshezan Brand voice here isn't a static style guide you set once and hope it sticks. It's actively built through a neurosymbolic workflow inside Pebbles that structures how your voice gets defined – not assumed or inferred passively. Once constructed, it's saved to the centralised Library alongside your other company context, making it a living asset you can reuse and refine over time, not a passive setting buried in a panel.
From there, the Library acts as persistent memory. It holds your outputs and team knowledge so the AI draws on your real content, not a blank slate. To be precise about the mechanics: this is context-driven memory retention, not model fine-tuning in the ML sense. No weights are updated. The system gets sharper as usage grows because it's working from richer, more specific context.
On follow-ups, the logic is adaptive – not a fixed drip sequence. Assistants use stored context and reply signals to decide what comes next. It's not being retrained on the fly, but it's also not running a rigid script.
Your core question is the right one to ask: does this reduce downstream editing, or just move it somewhere else? Persistent context – including the brand voice you've explicitly constructed – is exactly how we're trying to solve that. One more thing worth knowing: tenant isolation is in place, so your data never crosses over with other clients. What you build stays yours.
Happy to go deeper on any part of this.
Pebbles Ai
@raihanshezan You clearly know this space at a veteran level, the kind of read that only comes from running real outbound and watching exactly where it breaks. Your sharp questions. One at a time. Here we go:
Trained on your own content, or a style guide you configure upfront (Brand Voice Creation Feature)?
Your own, and the way you build it is the fun part. It runs as a guided Q&A, about 2 hours, closer to a sharp interview than a setup form.
It pulls your voice out of your answers and your best existing writing, then hands you a full spec: a word arsenal you actually use, a forbidden list you never touch, your signature phrases, and the mechanical fingerprints like sentence rhythm and punctuation.
What comes out is a brand voice that is distinctly yours and, more to the point, one that actually performs.
Our Brand Voice Creation Feature was built on principles drawn from McKinsey strategy practice and Saatchi and Saatchi creative heuristics, then layered with persuasion science, communication sciences, and a full library of anti patterns.
So it does many things at once. It captures how you sound, sets you apart, and makes the voice ACTUALLY effective, not just a gimmick.
This is the first half of the puzzle.
How does it learn and hold the voice over time?
Two parts, depth and enforcement.
The depth is a layered stack sitting under every message in every feature (from Marketing Assistant, Auto SDR to Smartbox). It uses 7 layers as enforcement. All proprietary Pebbles IP, only 2 are general heuristics tuned to you.
From your foundation up to the surface:
Organisational intelligence, your company's source of truth
GTM knowledge base, proprietary best practices and business netiquette
Neurosymbolic logic, what to do in every case, even the edge cases
Applied persuasion sciences
Brand voice framework
Hyper-personalisation
Persona-centric writing
Cultural nuances
Every draft runs a final check against your spec before it leaves, so the voice stays put instead of drifting the way a fine tuned model does. This is around (a) precision, (b) accuracy, and (c) efficacy.
Think of the baby of a senior Saatchi and Saatchi copywriter and a marketing scientist. It has your style guide memorised, knows every persuasion principle, and never has an off day. As you approve and edit, the spec sharpens toward your style too (the cherry on top).
This enforcement is the other half of the puzzle.
Do follow-ups adapt to reply signals, or is it a fixed sequence?
They adapt. The logic reads the intent behind each reply, then acts on it:
An objection gets answered on its merits
A "not now" gets a gentle nurture
A no gets turned into a maybe
Not me gets the colleague in
Silence gets the auto follow up
The sequence bends to the symbolic signal instead of marching on like chatbot.
There is more IFTTT neurosymbolic logic built in, but I'll spare you the novel 😂
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.
Pebbles Ai
@andrasczeizelGREAT questions!!! You've named the exact thing that caused me anxiety as a founder. You wake up one day and realise you're paying for 10+ different tools related to GTM.
That's insane even for an established small business of 100 people, let alone a startup finding its feet with 2 co-founders.
Each tool is another subscription, another login, another line item, another learning curve, and that creeping OpEx is the silent killer of your runway.
Let me break it down for you:
🧱 At the base sits your organisational intelligence, the source of truth about the company, the approach, and the products/services
📚 Above it, the GTM knowledge base: best practices, business netiquette, and the neurosymbolic logic for what to do in each case, and IFTTT logic for complex requests
🔬 On top, the sciences: communication science, applied persuasion sciences, hyper-personalisation methods, brand voice rules, persona-centric writing, and cultural nuances
On how much comes from your data versus fixed playbooks, think of it as an 80/20 split:
The 80% is us: the GTM sciences, B2B heuristics, neurosymbolic workflows we've distilled from how the top 1% of management, marketing and sales actually operate, judge, and executes. We never leave it to the base AI models, we use our battle-tested reasoning system that adapts to each case
The 20% is you: your organisational data, your brand voice, and your company history to date; that's the only data we need, onboarding takes only 3 minutes
On how clearly you can inspect the why why a lead, segment, or campaign , which is the part I care about most:
Every output has an audit trail. Because the reasoning runs over explicit workflows, sciences and market intelligence, you can follow the whole chain, even as a spider spider web of neurons, and see exactly how we arrived at a strategic recommendation, an ICP analysis, or a campaign
That means you can verify the system isn't making random calls: the logic is traceable end to end, so a lead, a segment or a campaign is always backed by a reason you can inspect. We built this specifically for enterprise as they deem this VERY important, but provided access to companies of all sizes.
For research it draws on roughly 10x more sources than a typical base model (Strategy Assistant draws at least 60 sources per inquiry), and we've categorised every source by tier (Tier 0 to Tier 4): data providers like Statista, market intelligence companies like Gartner, down through Reddit threads. Each Tier is used only when it's actually appropriate
In fact, the Marketing Assistant takes a bottom-up approach: it runs semantic analysis across sources like Reddit and X to surface what the public actually thinks and feels about a given topic (for example, sentiment analysis on a product category or a competitor)
The Strategy Assistant works the other way, top-down: it runs market intelligence analysis over open data sources like the World Bank Open Data and Eurostat to forecast how markets are likely to move (e.g. a DIKW approach, turning raw data into information, knowledge and finally insight) so you can plan against where the market is heading, not just where it is today
Put together, it's like having a senior analyst from McKinsey, a senior copywriter from Saatchi & Saatchi, and a senior enterprise closer from Big Tech, all working in one place. No more stitching together expensive consultancies and senior hires you can barely afford, and often can't justify before you've even found product-market fit. You get that calibre of thinking from day one, at a fraction of the cost of a single one of those salaries
And so you know this isn't a weekend project?
This wasn't built over a weekend. It was built on 10+ years of first-hand GTM experience, 18 months of PhD-grade research, and 3 years of development, roughly 500 weekends, but who's counting. ;)
@emincanturan Okay, this might be the MOST detailed answer I've ever received to a PH comment. Thanks for that. :))
The 80/20 breakdown made the product much clearer for me. I especially like that the company context personalizes a structured reasoning system, instead of leaving the base model to improvise an entire GTM strategy from scratch. And the fact that every recommendation has an inspectable audit trail makes the whole thing much easier to trust.
500 weekends definitely explains the depth... huge respect for what you've built. You absolutely sold me on trying Pebbles :)
Pebbles Ai
@imtiaj_ahmad That gap is the whole reason why Pebbles Ai exists. Hearing it from someone who ran growth at 20 people is very interesting. You had the talent. You just did not have the war chest for the institutional playbook.
That's what we built. Enterprise firepower without the price tag.
Also, your skepticism is the correct default. "Thinks before it writes" is easy to print on a landing page and still be a templated-wrapper prompt chain.
A standard LLM predicts the most probable next words. That is the whole thing. It has no separate step that asks "is this true, and does it follow the rules." If the sentence looks right, it goes for it, even when it is confidently wrong.
With Pebbles Ai, the neurosymbolic layer adds a second system that reasons with explicit rules and a structured knowledge base.
In other words, Pebbles Ai is a neurosymbolic reasoning system, not a wrapper chatbot.
The neural half drafts. The symbolic half checks that draft against the rules of GTM and against grounded facts before it provides you the output.
If the draft breaks a rule or asserts something that is not in the data, it gets caught and corrected instead of sent. One half writes fluently, the other half checks the writing against logic and evidence. This massively simplified btw, the truth is much more complex.
A concrete example. Ask a normal LLM to personalise a cold opener using the prospect's recent funding. If it does not actually have that data, it will often invent a plausible one, "congrats on the recent Series B," because a confident guess reads better to the model than admitting it does not know.
That is how people end up congratulating a company on a round that never happened, which is a fast way to torch the first impression.
Our symbolic layer only uses a signal that exists in the verified lead data. No real funding event, no funding line. It reaches for a different, true angle instead. The model reaches for a nice sounding sentence. The symbolic layer reaches for a correct one.
The same logic covers strategy. If it drafts a plan that contradicts a constraint you set, or pitches an enterprise motion to a 30-person startup, the neurosymbolic rules catch the mismatch rather than letting a fluent paragraph paper over it.
In our own testing this cut errors to roughly a third of naive prompting, measured on HalluScore. Not zero, we would never claim that. A system that checks its work beats one that only sounds sure of itself.
Happy to run a live one. Give me a prospect and a claim you would want in the opener, and I will show you where it refuses to make something up.
Here are some actual stats:
Claude Opus (MAX) vs Pebbles Ai
Accuracy: 33% vs 87%
Precision: 57% vs 91%
Sales Efficacy, MQL to SQL: 15% vs 85%
Cost per usable reply: ~$5 to $7 vs $0.012
Hallucination on rule-bound queries: 31.4% vs under 2%
Congrats on the launch! I've been evaluating this space recently. The point tools (Apollo/Instantly-style outreach, separate lead-gen, separate enrichment) all promise pieces of this. The "one workspace" pitch lives or dies on the strategy layer actually informing the outreach, not just co-locating the tools. Can you share a concrete example of the neurosymbolic side changing what an outreach sequence says versus what a well-prompted LLM would write anyway? That's the claim I'd want to see proven before consolidating.
Pebbles Ai
@michael_shollenberger Thanks man, and great questions!!! From what I can see, surface level, you're building something similar just for a different domain.
Firstly, a well-prompted base model LLM (e.g. Claude Opus Max) writes an acceptable message. Fluent, personalised on surface data. If writing quality and personalisation were the bar, you wouldn't need us. And everyone would be driving Maseratis 😁.
But it requires fundamentally more to commercially move your company. Here is our formula:
Y (success) = (Geo × Industry × Persona × Common Denominators × Market Trends) × (Persuasion × Communication × Personalisation × Heuristics × Lexical Semantics)
Allow me to show you live on a call, concretely, the difference between our generation of strategy, campaigns, content, marketing materials, and sales assets, and that of a base model.
Would you like that?
Interesting take on skipping open-rate tracking for deliverability reasons. I've seen the same pattern in my own outreach, decent opens, zero replies. How does the AI decide what actually counts as a good reply signal vs just politeness?
Pebbles Ai
@benjouss Yeah, open rates are basically noise at this point. Bots, preview panes, and Apple MPP have made the metric too unreliable to act on, so we made a deliberate call to drop it entirely. What Pebbles does instead is classify reply intent: positive, neutral, or negative – so a "thanks, not right now" gets tagged differently from a reply that actually moves the conversation forward. The system is specifically built to tell politeness from a real buying signal. Rather than opens, we track reply rate, positive reply rate, meeting booked rate, a message quality score the AI runs before anything sends, and deliverability and inbox placement.
The "decent opens, zero replies" pattern you described is exactly the problem we set out to fix – because opens are vanity and replies are the only signal worth optimising for.
@kuzmovych That distinction between "polite" and "moves the conversation forward" is the one that's hard to get right without over-engineering it. Makes sense to track reply intent rather than opens if bots and preview panes have made opens that unreliable. Does the model get better at telling the two apart over time per account, or is it more of a fixed set of rules across all your users?
Pebbles Ai
@kuzmovych @benjouss I like the questions, thanks. In regards of the guardrails that keep public facing content acceptable in regards of quality checks we have that are deterministic and industry proofed there are many other customisations and flexibility that brings wide variety for the user to step in and control the output tone, style, shape etc. We handle the complex part of many layers of company-user-recipient specifics with use of proprietary and industry battle tested frameworks, workflows, conflict resolution logic(IFTTT), memory management, model attention tricks, context engineering and more. The system evolve as you use it by enriching the internal knowledge hub called library with user approved content(team roles that provide the permissions is a different topic so I will not deviate here). In Q3 we are going to improve assistants capability to build up library parts on behalf of the user in a user defined permissions. User stays in control but the autonomy of the platform grows up. I genuinely believe the self evolving agents are the future but people need to gradually get used to this new world
Pebbles Ai
@benjouss Ha, "decent opens, zero replies" is something I hear often. An open tells you the subject line worked. It says nothing about whether anyone actually wants what is inside.
The silence tells you nothing worked once they did.
When a whole campaign gets opens and no positive replies, it is not bad luck. It is an equation you have not solved yet.
Picture outbound as a formula, not a sum:
Y (outbound success) = Strategy × Positioning × Targeting × Value Proposition × Offer × Netiquette × Communication Sciences × Persuasion Tactics × Lexical Semantics x Persona-centric Writing
One incorrect part of the formula (variable) drags the whole result toward zero, however strong the others are. Bad luck is just the name people give the equation when they have not solved for the variables.
So Pebbles treats a reply as a strategy signal, not just a lead status. A campaign of polite nothings gets read as a diagnostic. Wrong audience, weak hook, or a value prop that does not answer "so what".
The Strategy Assistant is built to pull the formula apart, so instead of running the same dud again next month with new subject lines, you fix the term that was actually near zero.
Or my personal favourite response to a dud campaign: MOREEE!!! More emails, more LinkedIn messages.
Doubling down on a formula that is still unsolved, which is a bit like flooring the accelerator when the handbrake is on.
On telling a real reply from mere manners, it reads for intent over tone. "Sounds interesting, will keep you in mind" is the corporate cousin of "we should grab coffee sometime". Warm words, empty calendar. A reply that asks about price or fit, raises a real objection, or floats a next step is the one that counts. The neural side reads the tone, the symbolic side checks whether anything was actually asked or committed to.
Does that make sense?
@emincanturan Good breakdown, thanks for the tip. The "one weak variable drags the whole thing to zero" framing is a useful way to think about it, most people just add more volume instead of finding which term is actually near zero.
Pebbles Ai
@benjouss Not a problem at all. Give it a real try. You won't regret it.
Pebbles Ai
@tihomiropacic Totally fair point. We don’t expect teams to rip out everything at once. The better path is often to start with a single use case, get value fast, and then expand from there. Pebbles Ai is built to support that kind of step-by-step adoption.
Pebbles Ai
@tihomiropacic Appreciate it. This is exactly the concern we built around. Moving from N tools to a bundle is a real risk, so the sensible route is a foot in the door.
You can start with one module, Fresh Leads for pipeline or Smartbox for outreach, and run it beside your current stack.
No rip and replace, and no burning down the old setup before you have judged the new one. We don't want hostages as customers, we want our customers to be in love with us.
Customer that have Stockholm syndrome 🤣. Joke.
All kidding aside, the payoff grows as you expand. Because it is one operating system with a neurosymbolic brain underneath, each feature you switch on makes the others smarter.
The features within the OS share the same context but have different intelligence. That's why I personally call it your digital marketing-sales department.
Different people, different domain experts, different hard-skills, different expertise, different judgement, and different knowledge.
We built the platform in that image.
For the next 24 hours you can start free, no card, so the foot in the door costs you 3 minutes onboarding.