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.
Congrats on the launch! Curious — what does the neurosymbolic part actually catch that a plain LLM would miss?
Pebbles Ai
@alex_tomilinThank you. This is my favourite question by a mile. Most people admire the Ferrari (the GTMOS). Don't really care about the engineering that went into the engine (neurosymbolic AI). Let me break it down in 3 levels.
Macro Level | What is it?
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.
Meso Level | How did we build it?
The engine is not a weekend project. The numbers behind it:
18+ months of research before the first line of production code
70,000+ engineering hours in the proprietary neurosymbolic architecture
800tn parameter permutations in business communication alone
Over 3,000 rule-based IFTTT rules across all B2B use cases and workflows
7 specialised neurosymbolic cores under GTMOS™, each mimicking the top 1% domain experts
Every output reasons up through a layered stack. Seven layers are proprietary Pebbles IP, two are heuristics tuned to you: your organisational truth at the base, then the GTM knowledge base, neurosymbolic logic, applied persuasion sciences and brand voice, with persona and cultural nuance on top.
That knowledge layer encodes six GTM disciplines, persuasion science, neuromarketing, behavioural economics, competitive strategy, segmentation theory and sales methodology, codified from closely-guarded secrets.
Break a rule or assert something not in the data, and it gets caught and corrected before it is ever sent. It also doesn't agree with you. It cares more about your success, than your ego. It will not allow you to make mistakes.
Micro level | Why it matters?
Let's look at some numbers. First lets look Claude Opus on the Max tier with a single instruction versus 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 neurosymbolic methods approach ~100% accuracy on rule based tasks [arXiv 2502.01657]. We are closing up at 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 a outputs, before 500 hours of human prompting
Pebbles does the same job inside a subscription near $450 a month, all in. Same output, a fraction of the cost, none of the babysitting.
The neursoymbolic 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 makingthe 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.
Try it yourself, break it, and see where it holds.
Pebbles Ai
@tehreem_fatima5 tool sprawl is genuinely one of the most exhausting parts of running a small team, so I'm really glad this resonates.
What we do make easy is bringing your contacts in via CSV, Google Contacts, or Microsoft Contacts, so you're not starting from scratch.
Deep integrations with products like HubSpot, Attio, Monday.com are already on our roadmap for Q3. And we are not going to stop there and gonna bring our own agent-first CRM solution to support the vision of replacing 10+ tools
Pebbles Ai
@tehreem_fatima5 That is the exact nightmare everyone is in. Whether you are an early-stage startup of two co-founders or a mid-market organisation with 1,000+ employees.
Our primary research with over 120 respondents (companies) that they use anywhere between 10-25 siloed, mechanical tools (commercial related) that don't talk to each other.
Their monthly OPEX is anywhere between 2k-20k USD. Insane.
Pebbles puts management, marketing and sales under one roof instead. It sounds cliché, but a sustainable GTM with compounding momentum requires strategy, marketing and sales to work harmoniously together.
Singing from the same hymn sheet as it may.
On migration, you do not need a clean data move to get value on day one. Fresh Leads brings its own enriched data through a three provider waterfall, so Pebbles is useful from the first login, not after an import project that eats your first fortnight.
For your existing pipeline, you do not have to burn the old place down before you move in. Run Pebbles alongside your current CRM and bring your data across at your own pace, so nothing breaks halfway through a quarter.
In fact, we built it in such a way that it actually compliments your existing CRM tech stack.
For the next 24 hours you can start free, no card, and see how it feels once you are settled in. Let me know if you want a demo.
AI sales platform is a pretty crowded category at this point, what's the thing pebbles does that a rep couldn't already do with a CRM plus a good sequence tool. genuinely curious what the wedge is
Pebbles Ai
@omri_ben_shoham1 You're absolutely right. Most of these tools overlap, and the category is crowded with point solutions that do one or two things very well.
But none of them focus on reasoning. They only focus on execution.
People think outbound is a sending problem. It's usually a reasoning problem, and it breaks into two formulas most tools never touch.
The first is the sequencing itself. A message that actually lands isn't a template with a {first_name} slot. It's at least 5 disciplines working together:
Persuasion science: the established principles of influence and buyer psychology (reciprocity, social proof, commitment) and knowing which to use at which moment
Communication science: how the message is structured and paced to be understood. Clarity, framing, timing, and the netiquette that makes it read like a human wrote it
Hyper-personalisation: real profiling of who this person is and what they care about, so you connect on a human level despite being total strangers. Not a dumb variable field like job title
Heuristics: the judgment rules for each situation. When to push, when to nurture, when to stay quiet, distilled from the combined wisdom of top operators
Lexical semantics: precise word choice and sentence construction, because the same point lands or dies on phrasing and tone
Miss one and the sequence underperforms. Which is why so much outbound gets 1% reply rates and blames the list.
But here's the part almost nobody does. All of that only works if the upstream formula fired first. Before a single message goes out, you need:
Geolocation (e.g. Greater London)
Industry (e.g. financial services)
Persona cluster (ICP and archetypes)
Common denominators (historical data)
Market trends (the ones that actually move you)
You can do all of it on the platform. Great sequencing on bad upstream work is just a beautifully written message, sent to the wrong person, with the wrong value proposition.
That's the whole reason we built Pebbles as one neurosymbolic engine, not another point tool. It reasons over both dimensions: strategy upstream, execution downstream.
And this is the real problem with the point-solution pile. Those tools were built for a pre-AI world. They aren't AI-native, nor domain-specific, so they physically can't reason at this level.
There's a deeper issue too. Dig into the research and you'll find most GTM point solutions are built exclusively by engineers with no background in strategy, marketing, or sales.
So ask yourself: how do you solve something you've never lived? It's like our tream trying to fix a problem in corporate law. We don't know how paralegals work with associates, how associates work with solicitors, how solicitors work with partners, how partners work with the board, and how each of them handles clients through completely separate workflows. Hand on heart, personally I wouldn't know where to start.
Don't get me wrong, these engineers are brilliant. Seriously smart, and they've built genuinely great products. But each one covers one or two stages of a funnel that has at least ten.
Because go-to-market is no different from building software. Teamwork is everything. Back-end works with front-end, front-end with the product designer, everyone with QA and DevOps, and a solution architect sits across all of it. Engineering has Jira for exactly this.
Nobody had built the Jira for commercial teams. We're the first. It's called the Go-To-Market Operating System, and it could only exist with extraordinary intelligence beneath it.
Think of it like iOS. Each app (feature) has its own tech stack, its own intelligence, its own knowledge base, its own mapped-out GTM science. It's like having multiple startups in one place. And every app talks to the others.
Point solutions can only bolt on an "AI feature," which is usually just a free ChatGPT or Gemini model in a sexy trench coat (UX). Execution works, so you gain speed.
But it doesn't move the needle on pipeline, revenue, or margin. You and your team just do busywork faster. Three months later you look back, and nothing material has changed on the financials.
The meme says it perfectly. You think the fix is one more point solution. It isn't. It's more busywork, plus a fresh learning curve for yet another tool that most people never fully learn.
That's exactly the problem Pebbles solves, with a completely new approach.
Pebbles Ai
@omri_ben_shoham1
Jinna.ai
Congrats on the launch! What’s the main user interface to your product, web UI, MCP or something else? Screenshots mainly demo the UI; I’m wondering how well it integrates with Claude for instance.
Pebbles Ai
@nikitaeverywhere The main interface today is the web app. We also have desktop apps that live in your dock on iOS (mac) and Windows, so it sits right alongside your other daily tools rather than buried in a tab.
No installation required. Super light.
On Claude specifically: we don't integrate with Claude, and that's on purpose. Pebbles runs its own reasoning models, our cores, which are purpose-built for GTM rather than general-purpose.
MCPing Claude would mean inheriting a general model's guesswork for rule-bound, domain-specific complex work, which is exactly what we set out to avoid.
Instead, we make switching effortless. Think of it like changing mobile providers and keeping your number. The system does the heavy lifting, and you're moved over in about 3 minutes without losing your most up-to-date company info.
You don't rebuild your progress that you've carefully built within Claude (or ChatGPT, Gemini, etc.). You just move it over to Pebbles Ai within 3 minutes, and run it on much smarter models.
Lastly, every customer gets a custom API that allows to connect the Pebbles Ai neurosymbolic brain trained exclusively on your company to your existing techstack.
Think of it as the primary brain of your existing techstack. You can wire it into your website to answer inbound inquiries, power the chatbot on your site, and plug into the other automation tools you already run.
In short, you get both a sophisticated multi-purpose platform + a connectable brain that makes your other tools smarter too.
Hope that make sense.
Many teams already use HubSpot, Clay, Apollo, and ChatGPT together. What workflow do you think Pebbles replaces most completely, and what do you still expect customers to keep?
Pebbles Ai
@tarqiya_forgah Great question! Let me break it down for you.
ChatGPT: replaced completely.
For GTM specifically, this is the cleanest swap. Generic AI gives you fluent text with zero memory of your company, your ICP, or your positioning, so you spend more time briefing it than it spends adding tangible value back to you.
Pebbles' Marketing Assistant and Strategy Assistant cover it, and actually make an impact on your pipeline, revenue, and margins. Not to mention it gives you energy back, because you don't need to learn so many tools or keep so many tabs open that never inform each other.
Clay and Apollo: largely absorbed.
This is where the patchwork usually collapses into one flow. Clay's real value is its enrichment waterfall and AI research, and Apollo's is its contact database plus sequencing.
Both run natively inside Pebbles, on our own multi-provider enrichment waterfall, feeding straight into reasoned email and LinkedIn sequences.
So the sourcing, the enrichment, and the outreach stop being 3 tools you stitch together by hand. It's all connected now.
On top of that, we use specialised profiling that reads each prospect from both a marketing angle and a sales angle. Pebbles doesn't just hand you leads. It tells you who they actually are, and recommends how to build a relationship with them.
HubSpot: keep it.
Your CRM is your system of record, and it should stay that way. It's wired into your reporting, your deal tracking, and your support.
We are not asking you to rip out the ledger. Pebbles sits on top as the reasoning and execution layer, and the CRM stays the source of truth. We'll be integrating with HubSpot, Notion, Salesforce, and Pipedrive soon.
Cost
There's a cost angle too. Those four as a combined stack, across seats, is not cheap. We priced Pebbles to come in under Clay alone at every tier, and that's before you count the three or four other subscriptions it folds in.
So the short version: keep the CRM as your record, retire the patchwork on top of it. We'll have integrations soon with Notion, HubSpot, Salesforce, and Pipedrive.
@Emin Can Turan appreciate you laying that out in full, that's a much clearer pitch than what fits in a tagline. The reasoning-over-execution framing makes sense to me now - most of the tools I've seen really are just execution wrappers. I'm curious how you handle the cold-start problem though, when a team has none of that historical/common-denominator data yet. Does the upstream reasoning still work day one or does it need a few weeks of signal first?
Pebbles Ai
@omri_ben_shoham1 Short answer: the upstream reasoning works on day 1. It does not need to sit and watch you for a month first.
Here's why. The intelligence isn't starting from zero and waiting to learn from your behaviour. It already carries the encoded GTM knowledge, the strategy models, the persona logic, the market-signal reads. Y
And telling it who you are takes about 3 minutes. Think of it like switching phone providers. You don't rebuild the phone number and data, you just port your number across and it works.
Onboarding walks you through your company, your product, and your ICP, and the cores light up immediately with that context.
So it's not "use it for three weeks and it might get good." It's already INSANELY SMART in the first session, and it compounds from there.
Congrats on the launch, @dima_antoniuk 👏 I'm curious—does Pebbles go beyond analytics and help identify the root cause of poor campaign performance? For example, whether the issue is the ICP, audience segment, offer, messaging, or marketing channel?
Pebbles Ai
@seffal_abdelaziz Great question, and yes, it can. If a campaign is underperforming, you can take it into the Strategy Assistant, use the Audit workflow, and reason through exactly that: is the weak link the ICP, the segment, the offer, the messaging, or the channel. Or, something else.
The engine can work through those variables with you and pinpoint where it's breaking.
What's coming (now in development) is the automated version. We're building a feature that runs that whole diagnosis for you, isolating which variable failed across the full formula:
Y = (Geo × Industry × Persona × Common Denominators × Market Trends) × (Persuasion × Communication × Personalisation × Heuristics × Lexical Semantics)
Because it's multiplication, not addition, one weak input collapses the whole result. So the diagnosis isn't "the campaign was bad," it's "this specific factor was the zero."
The system pinpoints the exact variable, then tells you how to fix it.
That's a VERY complex analysis, tracing a poor result back through every input to find the one that broke, and it's a reasoning problem the neurosymbolic engine is built for.
Short version: today you can diagnose it with you Strategy Assistant. Soon it'll do the full root-cause breakdown automatically and hand you the fix.