Pebbles Ai - AI sales platform for modern B2B teams

@ethan_lee8 Hi, thanks! We offer free trial and then you can pick subscription that matches you most. Professional and team tiers are available right now with option to buy bundles for AI and leads.

@ethan_lee8 Free first, because a paywall on the first hello is a poor way to make friends. 😂

You can run the full platform for nothing, no card, cancel whenever. For the Product Hunt launch the free entry comes with a glass of champagne, some snacks, and a shout out.

• The champagne: 3,000 fresh leads to put it through its paces

• The A generous AI compute allowance (approx. £250 GBP)

• And you can invite 2 more team members for free (3 seats in total)

Professional, £49 a month
For solo operators doing the work of an entire team.

• 1 seat, 400 fresh leads a month, 3 neurosymbolic cores

• General Assistant your GTM pilot, Smartbox, your adaptive Personal Library, Google Workspace, CASA Level II security

• Retires the solo stack: a data tool, a sequencer, Sales Navigator and a couple of AI subscriptions. That pile runs north of £200 a month. This is £49. One login, roughly a quarter of the cost.

Team, £349 a month (down from £499)
For startups and small businesses growing smarter.

• 3 seats, 2,000 fresh leads a month, 6 cores

• Adds Strategy Assistant, email and LinkedIn outreach, the Brand Voice core, cross-feature memory, the collaboration hub, Company Library, Microsoft 365

• Retires the above across three people, plus a team CRM. A comparable three person stack lands around £900 to £1,500 a month. This is £349, so most of that spend goes back in your pocket.

Organisation, £1,499 a month
For mid-size businesses uniting every team on one plan.

• 10 seats, 6,000 fresh leads a month, 7 cores

• Adds team productivity analytics, centralised memory, a dedicated CSM, expert onboarding, and the SCALE 4 week GTM programme

• Retires the full mid-market stack: data, outreach, CRM, analytics and enablement. That typically runs £3,000 to £6,000 a month. This is £1,499.

Enterprise, £3,499 a month
For multinationals aligning GTM across regions and teams.

• 20 seats, 15,000 fresh leads a month, 8 cores

• Adds custom core and AI model tuning, SSO and SAML, persistent memory, and a dedicated account manager

• Retires an enterprise GTM stack that, with autonomous SDR tools alone, runs £8,000 to £15,000 a month. This is £3,499.

💛 Priced like a tool, performs like a department 💛

@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.

@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 😂

@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

@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.

@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. ;)

@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%

@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.

@ghi4a Yevhenii, this means a lot coming from you. And you're right, that "this isn't it" feedback loop is exactly why it shipped as good as it did. Proud to have built this with you. 🚀

@canice_neary this genuinely means a lot, thank you!!!

Coming from a partner at an agency doing outreach in a specialist space is high praise.

If there's ever an angle where the system could serve the Gray Market even better, tell us. Feedback from our customers using it in the wild is how the GTMOS becomes more powerful.

Really grateful to have you on board.

@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?