---
title: "This AI Tool Could Disrupt Sales Forever"
episode: 62
podcast: "The LeanScale Podcast"
publisher: "LeanScale"
guest: "Christian Peverelli"
guest_title: "Founder & CEO, Outbond"
date_published: 2026-05-15
date_modified: 2026-07-22
duration: 00:33:43
word_count: 5958
topics: ["outbound-sales", "ai-in-gtm", "gtm-strategy", "revenue-operations"]
canonical_url: https://leanscale-knowledge-hub.netlify.app/podcast/christian-peverelli-ai-outbound/
source: "LeanScale Podcast Knowledge Hub — https://leanscale-knowledge-hub.netlify.app"
license: "Free to quote and cite with attribution to The LeanScale Podcast."
---

# This AI Tool Could Disrupt Sales Forever

_Christian Peverelli on AI-native outbound, the death of spam, and putting agency-grade prospecting in one operator's hands_

**Episode 62 · The LeanScale Podcast**  
Christian Peverelli, Founder & CEO, Outbond · Hosted by Anthony Enrico  
Published May 15, 2026 · Updated July 22, 2026 · 00:33:43  
Canonical: https://leanscale-knowledge-hub.netlify.app/podcast/christian-peverelli-ai-outbound/

**Topics:** Outbound & Sales Development · AI in GTM · GTM Strategy · Revenue Operations


## Executive summary

Cold outbound is broken — not because buyers hate being sold to, but because the tools were never built for relevance, intent, or speed. In this episode LeanScale co-founder Anthony Enrico sits down with Christian Peverelli, founder and CEO of Outbond, an AI-native prospecting platform people have started calling 'the Lovable for prospecting' — or 'the power of Clay with the ease of use of Lovable.' Christian's path is unusual: roughly a decade building companies and training non-technical founders, a stint as director of an LA accelerator, and a prior company (We Are No Code) that taught people to build products with no-code and then AI tools. Watching that world, he concluded the hard problem had moved. Building products was getting easy; distribution was the new bottleneck. Outbond is his answer.

The spine of the conversation is a thesis about architecture: there is a fundamental difference between AI-native systems and 'slapping amazing AI on top of a product built pre-AI.' Christian calls Outbond a 'system of intelligence' — a multi-agent stack where different agents use different models based on what each does best, and where sophistication and ease of use aren't traded off against each other. His mission is to democratize the 'GTM engineer' so that building agency-grade outbound campaigns no longer requires a team of ten or a multi-million-dollar Clay agency wrapped around it.

Most of the episode is a live demo. Christian shows four ways to start a list (describe a role; paste a website and let it scrape and infer your ICP and personas; name up to ~50 target companies and a role; or upload your own list), then enriches and verifies contacts, spins up a research agent across the imported companies, and builds a multi-criteria qualification and scoring model — the kind of point-system a marketing-ops team used to spend weeks on — all in a single prompt, with the agent even authoring its own output schema. Feed it a domain (he uses leanscale.team) and it writes out your company context, ICP, and personas as reusable files that power copywriting and qualification. The payoff he cites: positive reply rates climbing from a classic 1–2% up to around 10%, because hyper-relevant micro-campaigns replace spray-and-pray blasts.

The sharpest tactical lesson is about signals: the biggest mistake people make is mentioning the signal. You don't earn credit for telling a prospect you noticed they're hiring or that they posted on LinkedIn — that real estate is better spent on your company's value. Use intent to decide who and when to reach; keep it out of the copy. Christian and Anthony also dig into stack consolidation (replacing Sales Nav → Apollo → multiple enrichment tools → a verifier → ChatGPT deep research with one fair-credit system), the collision of sellers who understand angles but not tech and 'GTMEs' who know tools but not selling, and a forward vision where the system finds look-alike audiences off your best responders while you drink your morning coffee. Who should listen: founders, RevOps leaders, sales and SDR managers, and marketing-ops operators who want to run sophisticated, relevant outbound without an agency — and anyone trying to tell AI-native GTM apart from AI bolted onto legacy tools.


## Key takeaways

1. **Distribution — not building — is the new hard problem** — After years teaching non-technical founders to ship products with no-code and AI, Christian concluded the bottleneck had shifted. When anyone can build (he points to Lovable, where he became an early investor), getting a great product in front of its rightful customers becomes the constraint — an eyeball-economy problem, not an engineering one.
   _Why it matters:_ Invest your scarce advantage in go-to-market, not just product. In a world where building is cheap, the durable edge is a repeatable way to reach and convince the right buyers.
   _For:_ Founders, RevOps Leaders, Sales Leaders

2. **AI-native beats AI bolted onto legacy tools** — There's a fundamental gap between an infrastructure built for AI from the ground up and one that 'slaps amazing AI on top' of a pre-AI product. Christian frames Outbond as a 'system of intelligence' — a multi-agent stack where each agent uses the model it's best at — versus chatbots or GPT plugins retrofitted onto old architectures.
   _Why it matters:_ When evaluating GTM tools, look past the AI label at the architecture. Companies built pre-AI often have to rebuild from scratch; native systems compound advantages older stacks can't easily copy.
   _For:_ RevOps Leaders, Founders

3. **A website is enough to build your ICP, personas, and lead list** — Outbond can take a single domain, scrape it, and infer the ideal customer profile and personas, then convert that into a targeted lead list — writing your company context out as reusable files that also power downstream copywriting and qualification. Christian argues that for most people the AI is better than they are at drafting ICPs and personas.
   _Why it matters:_ The friction of defining targeting collapses to a prompt. Operators should treat AI-generated ICPs as a strong first draft to refine, not a task to build from a blank page.
   _For:_ RevOps Leaders, Marketing Leaders, Founders

4. **Research and qualification agents in one prompt — with the schema written for you** — A single natural-language request spins up a research agent across the imported companies and a multi-criteria qualification model (company type, funding stage, size, revenue range) built from the best-practice prompts of top GTM engineers. The system authors its own output schema and returns structured results; you can delete columns you don't need and go 'under the hood' only if you want to.
   _Why it matters:_ Work that used to be a multi-week marketing-ops project — a scoring model, a point system, a research workflow — becomes a prompt, freeing operators to focus on strategy and angles instead of plumbing.
   _For:_ RevOps Leaders, Marketing Leaders, Sales Leaders

5. **Use intent signals to target — never to decorate the message** — The biggest thing people get wrong about signals is mentioning them. Telling a prospect you saw they're hiring or read their LinkedIn post doesn't impress them and burns limited email real estate. The signal's job is to qualify who and when to reach; the copy should carry your value.
   _Why it matters:_ Separate targeting from messaging. Let signals decide relevance and timing behind the scenes, and spend the words on the buyer's problem — 'good old value emails' aimed at the right people beat signal name-drops.
   _For:_ Sales Leaders, RevOps Leaders, Marketing Leaders

6. **Relevance kills spam — and lifts replies from 1–2% to ~10%** — Because the system makes sophisticated, super-targeted micro-campaigns fast to build, teams can reach the right people at the right time instead of blasting one offer to everyone. Christian reports positive responses rising from a classic 1–2% to roughly 10%.
   _Why it matters:_ The path out of the spam arms race isn't more volume, it's more relevance. Narrow, timely micro-campaigns are both more effective and less corrosive to your brand and deliverability.
   _For:_ Sales Leaders, RevOps Leaders, Marketing Leaders

7. **Collapse the bloated outbound stack into one tool** — The typical motion strings together Sales Navigator to build a list, Apollo for enrichment, two more enrichment tools, a verification platform, and ChatGPT for per-account deep research with a hand-crafted prompt. Outbond folds list-building, enrichment, verification, and research under one roof with a fair, usage-scaled credit system.
   _Why it matters:_ Consolidation cuts cost, latency, and tool-switching. A credit model that scales with results lets small teams start cheap and only pay more once campaigns are working.
   _For:_ RevOps Leaders, Founders

8. **Live data beats a static database** — Outbond builds lists from live data rather than a stale database, which is what makes signals like a recent job change usable. A newly hired head of RevOps has 30/60/90-day priorities; relevance comes from reaching people at that strategic moment, and from research filters (e.g., how many events a company runs, department growth, 10-K priorities) that segment for real intent.
   _Why it matters:_ Freshness is a competitive feature. Prospecting on live signals lets you catch buyers in their window of highest intent instead of contacting a database snapshot.
   _For:_ RevOps Leaders, Sales Leaders

9. **Empower the operator — strategy plus tech in one person** — There's a split between sellers and RevOps people who understand angles, customers, and playbooks but lack technical skills, and 'GTMEs' who know the tools but not selling. Putting execution power into a sophisticated-but-easy system lets a single AE who understands strategy build creative, sophisticated campaigns without a ten-person team.
   _Why it matters:_ The highest-leverage combination is a domain-fluent operator armed with AI-native tooling. Most people will be pushed into technical execution anyway — better to lead it than resist it.
   _For:_ Sales Leaders, RevOps Leaders, Founders

10. **The vision: look-alike expansion while you sleep** — Christian frames the longer-term product as a system of intelligence that, once a campaign gets positive replies, finds look-alike audiences of people who match your best responders. You wake up to a suggestion — e.g., 57 new people who fit — and approve with one click, so the human stays on selling.
   _Why it matters:_ Outbound becomes a continuously optimizing loop: the system learns from what's working and proposes the next batch, compressing the operator's role to two decisions — approve the list, approve the copy.
   _For:_ RevOps Leaders, Sales Leaders, Founders

11. **Founders build from their own scars** — Both Christian and Anthony trace their companies to personal pain: Christian hated paying for products and outreach he couldn't build as a non-technical person; Anthony ran RevOps at three VC-backed companies feeling underarmed on information and technology. Outbond and LeanScale are answers to problems they lived.
   _Why it matters:_ The clearest product and content bets come from lived operator pain. If you were underarmed doing the job, others still are — that's the wedge.
   _For:_ Founders, RevOps Leaders


## Frameworks

### Distribution Is the New Bottleneck (01:22)

**Definition:** As AI and no-code make building products easy, the hard problem shifts from creation to distribution — getting a great product in front of its rightful customers in an attention (eyeball) economy.

Christian's frustration teaching non-technical founders to ship led him to conclude that building would no longer be the moat; reaching buyers would be. Outbond is his bet on solving distribution, the way Lovable solved building.

### System of Intelligence (AI-Native vs. Bolted-On) (23:12)

**Definition:** An outbound platform architected for AI from the ground up as a multi-agent system — each agent using the model it's best at — rather than a pre-AI product with AI 'slapped on top' via chatbots or plugins.

Christian argues this is a fundamental architectural difference, not a feature: pre-AI companies must rebuild their infrastructure, while native systems can offer both power and ease of use without trading one for the other.

### Website → ICP → Persona → List (12:23)

**Definition:** A workflow where you paste a domain, the system scrapes it, infers your ICP and buyer personas, writes them out as reusable context files, and converts them into a targeted lead list that also powers copywriting and qualification.

It removes the blank-page friction of targeting. Christian claims the AI is better than most people at drafting ICPs and personas, while still letting sophisticated users override the output.

### Don't Mention the Signal (21:11)

**Definition:** Use intent signals (job changes, hiring, department growth, 10-K priorities, life events) to decide who to contact and when — but keep them out of the message. Mentioning the signal wastes scarce email real estate and doesn't impress the buyer.

The point of a signal is qualification and timing, not a talking point. Value-first emails aimed at the right people at the right moment outperform 'I saw you posted about…' openers.

### Collapse the Bloated Stack (28:44)

**Definition:** Replace the standard chain — Sales Navigator for lists, Apollo and other enrichment tools, a verifier, and ChatGPT deep research — with a single AI-native system on a fair, usage-scaled credit model.

Consolidation cuts cost, latency, and tool-switching, and encodes best-practice GTM prompts so small teams get agency-grade output. Credits scale with results, so it's cheap until campaigns are working.

### Strategy + Tech: Arming the Operator (25:24)

**Definition:** Bridge the gap between sellers who understand angles but not tooling and 'GTMEs' who understand tooling but not selling by giving one strategy-fluent operator an easy-but-sophisticated execution system.

When an AE who understands the customer can also build creative, sophisticated campaigns without a ten-person team, execution power lands with the person who has the judgment — the real unlock of the AI era.


## Quotes

_Speakers inferred from an undiarized transcript — verify before attributing._

> "The big problem — and this was before people really knew so much about Lovable — was no longer going to be building products, but distribution."
>
> — Christian Peverelli, The LeanScale Podcast Ep. 62 (01:22)

> "I don't think that being able to build sophisticated outreach campaigns should require a team of 10 people. And I'm also not willing to sacrifice sophistication and complexity — or power — for ease of use."
>
> — Christian Peverelli, The LeanScale Podcast Ep. 62 (02:04)

> "People are now calling it the Lovable for prospecting. Others are saying the power of Clay with the ease of use of Lovable."
>
> — Christian Peverelli, The LeanScale Podcast Ep. 62 (04:35)

> "For most people, this AI is way better than you at creating ICPs and personas."
>
> — Christian Peverelli, The LeanScale Podcast Ep. 62 (13:45)

> "I like how it creates a scoring model and point system and builds out that whole mechanism, which is usually a couple-week project for a marketing ops team to do — all in one prompt."
>
> — Anthony Enrico, The LeanScale Podcast Ep. 62 (15:06)

> "We've seen positive responses rise from a classic one to two percent all the way up to about ten percent. That's how we get out of this whole world of spammy emails — by creating things that are super relevant to the right people at the right time."
>
> — Christian Peverelli, The LeanScale Podcast Ep. 62 (17:00)

> "I think this is the biggest thing people get wrong about signals. They always want to mention the signal — 'hey, I saw that you posted about this.' You don't have to tell people you know they're currently hiring. It doesn't impress them that you figured it out. But it does qualify them as a more relevant lead."
>
> — Christian Peverelli, The LeanScale Podcast Ep. 62 (21:11)

> "If you're wasting that real estate on 'I saw your LinkedIn post, I'm really inspired' — you just wasted so much time where you could have been baking in your company value instead."
>
> — Anthony Enrico, The LeanScale Podcast Ep. 62 (25:24)

> "There are things you can do when you're AI-first, like we are, versus what most companies are — which is slap amazing AI on top of your product. It's just fundamentally a huge shift."
>
> — Christian Peverelli, The LeanScale Podcast Ep. 62 (23:12)

> "You have salespeople and RevOps people who really understand selling but lack the technical skills to execute, and then you have the GTMEs who know how to use the tools but have no idea about sales, angles, and playbooks."
>
> — Christian Peverelli, The LeanScale Podcast Ep. 62 (25:24)

> "If you give the power of execution in these sophisticated systems, it now gives an AE the power to crush it — because they understand the angles and can build very creative, sophisticated campaigns around the specific needs of their company."
>
> — Christian Peverelli, The LeanScale Podcast Ep. 62 (26:02)

> "They use Sales Nav to build a list, then shoot it into Apollo for enrichment, then maybe two other enrichment platforms, then a verification platform, then take each account into ChatGPT for deep research. We put all of that under one roof with a fair credit system."
>
> — Christian Peverelli, The LeanScale Podcast Ep. 62 (28:44)

> "You wake up in the morning while you're drinking your coffee and it's like, hey, we found 57 people who fit perfectly based on who's responding positively to this campaign. Would you like to add them? And you just say yes. Because frankly — let's get back to selling."
>
> — Christian Peverelli, The LeanScale Podcast Ep. 62 (30:04)

> "It's all of the stuff I wish I knew when I was an operator in a startup. I ran RevOps for three VC-backed companies — high growth, high pressure, high stakes — and I was completely underarmed with a lack of information and technology."
>
> — Anthony Enrico, The LeanScale Podcast Ep. 62 (31:58)

> "We're basically building companies because of our own past traumas. 'Oh man, I was underarmed, I was not technical, and I wish I could do this.'"
>
> — Christian Peverelli, The LeanScale Podcast Ep. 62 (33:18)


## Practical advice by role

### Founders

- Assume building is no longer your moat — pour your scarce advantage into distribution and go-to-market, which is now the harder problem.
- When you buy GTM tools, look past the 'AI' label at the architecture: an AI-native system will out-compound one that bolted a chatbot onto a pre-AI product.
- Build from your own scars. The pain you felt as an underarmed operator is the same pain your buyers still have — that's the wedge.

### RevOps Leaders

- Consolidate the bloated stack — list-building, enrichment, verification, and research under one roof beats stitching Sales Nav, Apollo, extra enrichment tools, a verifier, and ChatGPT together.
- Prospect on live data, not a static database, so signals like a recent job change or department growth are actually actionable.
- Treat AI-generated ICPs, personas, scoring models, and research schemas as strong first drafts to refine — not multi-week projects to build from scratch.

### Sales Leaders

- Use intent signals to decide who and when — never to decorate the message. Spend the email's limited real estate on the buyer's value, not on proving you noticed a signal.
- Trade spray-and-pray volume for relevant micro-campaigns; that's what moves reply rates from 1–2% toward 10%.
- Arm strategy-fluent AEs with easy-but-sophisticated tooling so they can build their own creative campaigns instead of waiting on a ten-person team.

### Marketing Leaders

- Let AI draft the ICP and personas from your own website, then have the operator sharpen them — it removes the blank-page tax on targeting.
- Segment with research agents (events run, department growth, 10-K priorities) so campaigns reach genuinely relevant accounts instead of everyone.


## AI takeaways

**Thesis:** AI-native systems collapse the entire outbound stack into a single 'system of intelligence' and hand agency-grade prospecting to one operator — but the winning move is relevance and speed, not spammier volume. Architecture matters: AI built in from the ground up beats AI bolted onto a pre-AI product.

- **AI-native vs. bolted-on** — A multi-agent system architected for AI (each agent on the model it's best at) is fundamentally different from 'slapping amazing AI on top' of a legacy tool via chatbots or plugins. Pre-AI companies often have to rebuild from scratch.
- **One prompt, full agent** — A natural-language request builds a research or qualification agent, writes its own prompt from best-practice GTM patterns, and authors its own output schema — collapsing multi-week ops projects into a sentence.
- **Signals target, they don't decorate** — Use intent (job changes, hiring, department growth, 10-K priorities) to qualify who and when — but keep it out of the copy. Mentioning the signal wastes email real estate and doesn't impress buyers.
- **Consolidate the stack** — One AI-native tool with a fair, usage-scaled credit system replaces Sales Nav + Apollo + extra enrichment + a verifier + ChatGPT deep research, cutting cost, latency, and tool-switching.
- **Arm the operator, not a team** — Sophisticated-but-easy tooling lets a single strategy-fluent AE build creative campaigns — democratizing the GTM engineer so agency-grade outbound no longer needs ten people.

**Agent & automation ideas**

- Website-to-ICP agent: paste a domain, scrape it, infer ICP and personas, and generate a targeted lead list plus reusable company-context files.
- Per-account research agent that auto-writes its output schema and returns structured fields (e.g., number of events a company runs, 10-K focus areas, department growth).
- Multi-criteria qualification and scoring agent built from best-practice GTM prompts (company type, funding stage, size, revenue range) that outputs a point-system in one prompt.
- Look-alike expansion agent that watches a live campaign and, off the positive responders, surfaces new matching leads each morning for one-click approval.


## Operations takeaways

### Revenue operations

- **Consolidate the stack.** Fold list-building, enrichment, verification, and research into one AI-native system instead of stitching Sales Nav, Apollo, extra enrichment tools, a verifier, and ChatGPT together.
- **Live data over databases.** Build lists from live data so signals like a recent job change or department growth are actionable — you catch buyers in their highest-intent window.
- **Prompts replace projects.** Scoring models, qualification point-systems, ICP drafts, and research workflows become single prompts — reclaim ops time for strategy and angles.
- **Credits that scale with results.** A fair, usage-scaled credit model keeps costs low until campaigns work, then scales as they do — friendly to small teams.

### Pipeline & marketing ops

- **Micro-campaigns beat blasts.** Sophisticated, super-targeted micro-campaigns to the right people at the right time move reply rates from 1–2% toward 10% and get you out of the spam arms race.
- **Two human touchpoints.** The system aims to require only two decisions from the operator — approve the lead list, approve the copy — and automate the play selection in between.
- **Signals for timing, value for copy.** Reach a newly hired leader inside their 30/60/90-day window, but let the message carry your value rather than name-dropping the signal.
- **Native + integrated sending.** Ship personalized messages through Instantly and HeyReach integrations today, with native sequencing being built for teams that don't already have a sender.


## Metrics mentioned

| Value | Metric | Context |
| --- | --- | --- |
| 1–2% → ~10% | Positive reply rate | Christian reports Outbond campaigns lift positive responses from a classic 1–2% to about 10% by making hyper-relevant micro-campaigns instead of spammy blasts. |
| 8 per week | Meetings booked on autopilot | The no-code micro-prototype Christian's now-CTO Aboudi built on Make and Airtable was booking eight meetings a week — the proof point that led to Outbond. |
| ~11 months | Time building Outbond | Christian and team spent roughly the past 11 months building Outbond before this episode. |
| ~50 | Companies per targeted search | In the known-accounts workflow you can paste about 50 company names plus a role to prospect specific people inside them. |
| 57 | Look-alike leads surfaced | In the product vision, once a campaign gets positive replies the system proposes e.g. 57 new look-alike leads matching your best responders, added with one click. |
| ~10 years | Christian's operator background | Christian spent roughly a decade building companies and training founders before Outbond, including running an LA accelerator and founding We Are No Code. |


## Entities mentioned

- **Outbond** (company) — Christian's company; an AI-native prospecting platform ('the Lovable for prospecting') that builds ICPs, lead lists, enrichment, research, qualification, and copy through a multi-agent 'system of intelligence.' Reachable at outbond.ai. · https://leanscale-knowledge-hub.netlify.app/company/outbond/
- **We Are No Code** (company) — Christian's previous company, teaching non-technical people to build products with no-code tools — a mission that evolved into building with AI and seeded the insight that distribution, not building, is the hard problem. · https://leanscale-knowledge-hub.netlify.app/company/we-are-no-code/
- **LeanScale** (company) — Used live in the demo — Christian points Outbond at leanscale.team to auto-generate LeanScale's company context, ICP (high-growth Series A/B B2B SaaS), and personas (VP of RevOps). · https://leanscale-knowledge-hub.netlify.app/company/leanscale/
- **Christian Peverelli** (person, guest) — Founder & CEO of Outbond, an AI-native prospecting platform; former accelerator director and founder of We Are No Code, and an early Lovable investor. · https://leanscale-knowledge-hub.netlify.app/guest/christian-peverelli/
- **Anthony Enrico** (person, host) — Co-founder of LeanScale and host of The LeanScale Podcast. · https://leanscale-knowledge-hub.netlify.app/guest/anthony-enrico/
- **Lovable** (tool, AI App Builder) — AI app-builder Christian fell in love with ~a year ago and became an early investor in; the reference point for making building easy, and the namesake of 'the Lovable for prospecting.' LeanScale uses it for MVPs and micro-apps.
- **Clay** (tool, GTM Data / Enrichment) — The one tool doing a good job at sophisticated prospecting — but requiring a whole multi-million-dollar agency model around it; Outbond aims for 'the power of Clay with the ease of use of Lovable.'
- **Make** (tool, No-Code Automation) — No-code automation tool Christian's now-CTO Aboudi used (with Airtable) to build a micro version of an outreach tool that booked eight meetings a week on autopilot — the prototype that sparked Outbond.
- **Airtable** (tool, Database / Spreadsheet) — Spreadsheet-database used alongside Make in the original no-code outreach prototype.
- **Instantly** (tool, Cold Email / Outreach) — Email-sending platform Outbond integrates with to deliver the personalized messages it writes.
- **HeyReach** (tool, LinkedIn Outreach) — LinkedIn outreach platform Outbond integrates with alongside Instantly for sending campaigns.
- **Apollo.io** (tool, Sales Intelligence / Engagement) — Enrichment tool named as part of the bloated multi-tool outbound stack Outbond consolidates.
- **LinkedIn Sales Navigator** (tool, Sales Prospecting) — Named ('Sales Nav') as the list-building step teams typically start with before shipping into a chain of enrichment and verification tools.
- **ChatGPT** (tool, AI Assistant) — Cited as the per-account deep-research step teams bolt on with a hand-crafted prompt — one of the manual pieces Outbond folds into a single system.
- **YouTube** (tool, Video Platform) — Where Christian built a large following making videos inspiring non-technical people to build — the audience-building playbook he's now recreating for go-to-market engineers.


## FAQ

**Q: What is Outbond?**

A: Outbond is an AI-native prospecting platform founded by Christian Peverelli, sometimes described as 'the Lovable for prospecting' or 'the power of Clay with the ease of use of Lovable.' It builds ideal customer profiles and lead lists, enriches and verifies contacts, spins up research and qualification agents, and drafts copy through a multi-agent 'system of intelligence,' so a single operator can run sophisticated outbound without an agency. It's available at outbond.ai.

**Q: What is the difference between an AI-native GTM tool and one with AI 'bolted on'?**

A: An AI-native tool is architected for AI from the ground up — Christian describes a multi-agent system where each agent uses the model it's best at. A bolted-on tool takes a product built before AI and slaps a chatbot or GPT plugin on top. Christian argues this is a fundamental architectural difference: pre-AI companies often have to rebuild their infrastructure, while native systems can deliver both power and ease of use without trading one for the other.

**Q: How should you use intent signals in outbound?**

A: Use signals — like a recent job change, hiring, department growth, or 10-K priorities — to decide who to contact and when, but keep them out of the message. Christian says the biggest mistake people make is mentioning the signal; telling a prospect you noticed they're hiring doesn't impress them and wastes limited email real estate. The signal's job is qualification and timing; the copy should carry your value.

**Q: Can AI build your ICP and lead list from just a website?**

A: Yes. In the demo, Outbond takes a single domain, scrapes it, infers the company's ideal customer profile and buyer personas, writes them out as reusable context files, and converts them into a targeted lead list. Christian argues the AI is better than most people at drafting ICPs and personas, while still letting sophisticated users override the output.

**Q: How much can AI-native outbound improve reply rates?**

A: Christian reports positive response rates rising from a classic 1–2% up to around 10%. The lift comes from being able to build sophisticated, hyper-targeted micro-campaigns quickly, so you reach the right people at the right time with relevant value instead of blasting one offer to everyone.

**Q: What tools does an AI-native prospecting platform replace?**

A: It consolidates a typically bloated stack: Sales Navigator for list-building, Apollo and other enrichment tools, a verification platform, and ChatGPT for per-account deep research with a hand-crafted prompt. Outbond folds all of that under one roof with a fair, usage-scaled credit system that stays cheap until campaigns are working.

**Q: Who benefits most from AI-native outbound tools?**

A: Strategy-fluent operators — AEs, SDR managers, RevOps, and founders — who understand customers and angles but lack (or don't want to build) heavy technical execution. By putting sophisticated-but-easy execution power in one person's hands, these tools let a single operator build agency-grade campaigns without a ten-person team, bridging the gap between sellers who know selling and 'GTMEs' who know tooling.


## Timeline

- **00:00** — Meet Christian Peverelli & Outbond
- **00:45** — Why distribution — not building — is the hard problem
- **01:22** — From We Are No Code to building with AI
- **03:22** — How Outbond was born: Clay's power, Lovable's ease
- **05:48** — Live demo: four ways to build a lead list
- **08:18** — Enrich, verify, and research agents in one prompt
- **12:23** — Auto-building your ICP and qualification from a website
- **15:47** — Copywriting and sending: 1–2% to 10% reply rates
- **18:24** — Using intent signals the right way — don't mention them
- **24:46** — Empowering AEs: strategy + tech is the game-changer
- **27:59** — Collapsing the bloated outbound stack into one tool
- **30:04** — The vision: look-alike audiences on autopilot
- **30:39** — Where to find Outbond + closing


## Related episodes

- **Ep. 42: Outbound Is Dying: How Spara's Multimodal AI Turns Inbound Into Pipeline (Live Demo)** (David Walker) — Another AI-native GTM live demo tackling the same broken-outbound problem from the inbound side. · https://leanscale-knowledge-hub.netlify.app/podcast/david-walker-spara-multimodal-inbound/
- **Ep. 47: From Physics to Fixing Sales: How Amplemarket Is Rewriting GTM** (Mica) — A fellow AI-native prospecting/engagement platform founder on rebuilding outbound around signals and agents. · https://leanscale-knowledge-hub.netlify.app/podcast/mica-amplemarket-rewriting-gtm/
- **Ep. 92: Agents That Run Outbound While You Sleep** (Mica) — Directly echoes Christian's 'wake up to 57 look-alike leads' vision of autonomous, continuously optimizing outbound. · https://leanscale-knowledge-hub.netlify.app/podcast/mica-ample-market-outbound-agents/
- **Ep. 51: How This Founder Built a LinkedIn Outbound Engine After 2 Exits** (Zayd Ali) — A founder's tactical playbook for modern outbound — pairs with the relevance-over-spam thesis. · https://leanscale-knowledge-hub.netlify.app/podcast/zayd-ali-linkedin-outbound-engine/
- **Ep. 29: GTM Product Demos: Exploring Ocean.io** (Michael Heiberg) — A companion prospecting-tool demo on the show, useful for comparing list-building and targeting approaches. · https://leanscale-knowledge-hub.netlify.app/podcast/michael-heiberg-ocean-io-gtm-demo/
- **Ep. 24: AI Is Breaking Sales — Here's How to Fix It** (Mustafa Saeed) — The macro version of this episode's title thesis — how AI is disrupting sales and what operators should do about it. · https://leanscale-knowledge-hub.netlify.app/podcast/mustafa-saeed-ai-breaking-sales/


## Full transcript

_Machine-transcribed and not diarized; speaker attribution is inferred._  
_Transcript only, as a separate file: https://leanscale-knowledge-hub.netlify.app/podcast/christian-peverelli-ai-outbound/transcript.md_

### 00:00 — Meet Christian Peverelli & Outbond

**[0:00]** (logo whooshing) Today we have Christian Peverelli, founder, CEO of Outbond. So pumped for you to be here on The LeanScale Podcast today, Christian. Thank you for being here. Christian, you are absolutely changing the game for how B2B, Saas, and AI companies are finding their prospects, finding their leads, and doing it all in an agentic and simple way, and really, really stoked for what you're doing. And whenever we have a founder on, I love to just hear the story. What gave you the passion, and I call it courage, the start-out bond, and bring this into market? I love it. Anthony, what's up, man? I'm super stoked to be here, and hi to everyone watching.

### 00:45 — Why distribution — not building — is the hard problem

**[0:45]** Yeah, man, first of all, I love torture. I think anyone who's a startup founder, you gotta like a little bit of torture if you want to be in the game, 'cause we're playing long games here. We're playing the 10-year games every time, if not more. And so for me, I basically have a career path from where I spent the past 10 years building training founders and building companies, and I hit a frustration, which was that non-technical people couldn't really, it was difficult for them to build products. So this was when I was the director of an accelerator program based in LA, and that frustration actually led me to build

### 01:22 — From We Are No Code to building with AI

**[1:22]** We Are No Code, which is kind of the previous company that I was building, teaching people how to build products with no-code tools that quickly evolved into building with AI, basically. And that fundamentally changed the way that I thought about tech, right? I fell in love with Lovable about a year ago and became kind of an early investor as well. Through that process, I basically realized that the big problem, and this was like before people really knew so much about Lovable, was no longer gonna be building products, but in distribution. And yeah, basically, you know, that's where I kind of shifted my energy into like solving the next problem.

**[2:04]** Throughout my career, I was able to build like a very big following on YouTube, just creating videos for people to like, you know, inspire them like, hey, you can also do this if you're not technical. And I think I'm trying to do exactly the same thing for go-to-market engineers, right? I don't think that being able to build sophisticated outreach campaigns should require a team of 10 people. And I'm also not willing to sacrifice sophistication and complexity or let's say power for ease of use. So that's basically the mission we're going after, you know? And we've created a system of intelligence. That's what I call it. I love it.

**[2:43]** Well, I'm really, really excited about what you're building, how you're building it. And absolutely, I think some of these platforms and tools have democratized the ability to create and develop and to do things that required either unbelievable experience or massive teams to pull off. I'm a big fan of Lovable as well. We use it quite a bit for building MVPs of things at lean scale and building up micro-apps for our customers. So I think some of these platforms have just made things that were impossible for us to do before possible. So I know you prepared a demo for us today. I'm really, really pumped to go through it

### 03:22 — How Outbond was born: Clay's power, Lovable's ease

**[3:22]** and see exactly how Outbound can work within a GTM team. - 100%, yeah. So I think the first thing I'd like to say is the way that I came to this build of Outbound was because customer discovery, I had like a whole basically like module around customer discovery, which is like, you know, ultimately the beginning of sales before you even build a product and it requires doing outreach, right? It requires finding people who are your ideal customers and having chats with them, right? And discovering what they need. And yeah, one of my students who's now my CTO called Aboudi, he basically joined my program and basically, you know, built a micro version

**[4:02]** of an outreach tool, leveraging my know-how in terms of like the templates and all that with like a no-code like approach to it, which was leveraging an automation tool called Make and Air Table. And he was booking eight meetings a week on autopilot. And so I started looking into it and I kind of realized like, okay, like it seems like there's all these like old school tools and then there's one tool that's kind of doing a good job called Clay, but it requires like a whole agency. There's like a, you know, multimillion dollar agency model being built around it. And so that's when I realized like there's an opportunity

**[4:35]** here and I'm gonna go after it basically. And so we spent the past 11 months building out, you know, out bond, which people are now calling the lovable for prospecting. Others are saying like the power of Clay with the ease of use of lovable, but ultimately like it's an honor to be able to be compared to either of those companies 'cause I'm a huge fan as well. And I just think that the power now can go into the hands of more people, right? Because I actually believe deeply that like great products should see the light of day and should be able to be put in front of their rightful customers who can see value from them.

**[5:11]** And that is becoming increasingly difficult in a world where like it's the, you know, eyeball, you know, economy basically. So yeah, man, I can't wait to show you the product a little bit. I'm just gonna hop right into it. - Yeah. - Is that what you want me to do next little demo? - Perfect, perfect. Yeah, and I think that I agree with you. Those are massive compliments to be compared to, to be compared to those companies, especially like right out the gate. So I think you're in really good company if that's what the comparisons are. - Yeah, I, you know, honestly, in the end of the day, like we're building our own thing

### 05:48 — Live demo: four ways to build a lead list

**[5:48]** and it's very different from what other people are doing. And we're just super excited about that. And I would say just like lean into your own like issues because everyone, there's such a big market right now around this. I think that in the AI coding space, there's huge amount of competitions. We're starting to see some of the big winners. And I think that in the AI for sales, go to market, go out reach, there's still so much space. So I'm excited to kind of kick it off. So here we are inside of Outbound, my friends. And there are kind of different approaches you can use. I'll kind of run you through the big ones.

**[6:21]** One is you basically describe the role of the person you're going after. So like marketing manager at the type of company or industry in specific area with excise. And you can just like search like that. The other thing you can do, which is actually like being blowing people away is like you just put in your website and it'll go and scrape your website. It'll build your ideal customer profile. It'll build out your persona. And then it'll convert that into a lead list. And the third one is like, we know exactly the companies we're going after, but we need to reach these very specific people who have a specific role in that company.

**[6:57]** In that case, we just describe the role. And then we say at these companies and dump. And here you can dump about 50 names of companies. The final one is kind of like upload your own list and work from there. But for this use case, we're going to start off with this one. So let me just like, you know, click on this one. We could just add it here. I love how I'm like, I was like clicking on the wrong screen, on the recorded screen. So, you know, you can adapt anything you want here. Like, so let's just put product manager, just so you see that I'm not cheating here. But as soon as you upload, what happens is that it's going to bring you into the view

**[7:34]** where you can see it breaking out into, on the left hand side, your sort of left nav experience. On the right hand side will eventually be your columns. And the AI basically is a full agentic system, multi agent who each use different models, especially based on what they do best. And it's going to break it down basically by understanding that criteria and then doing a full search and creating a preview list here. So it goes ahead, it goes out in prospects. Awesome, so now it's showing us here a lead list. It's found this number of people, the fit this criteria. Underneath, we see all of the criteria. And from here, you can kind of fine tune it.

### 08:18 — Enrich, verify, and research agents in one prompt

**[8:18]** So you can just, you know, you can adapt it. Okay, let's actually just go after people who have recently been hired or, you know, actually let's go after, you know, San Francisco as well. And as soon as you're ready with this list on this preview, you can import leads. So at that point, you basically are importing your leads and the AI is going to actually build out the table for you. And it's going to then import the leads inside of this table as well. And so you're gonna see them populate. There we go. And from here, there are a number of things we can do. On the left hand nav, we see that it's basically asking us, do we wanna enrich these contacts?

**[8:58]** So like find email, so I'll say find email. For example, the goal is that once we have the actual prospects we're looking to go after, we need to do a number of things. Number one is find their like updated contact and verify that contact information. So we make sure we're contacting them correctly, right? So with work email, those checks you see on the left hand side, those are basically saying that those are validated as well. So we find the email, then we ping a verifier, we ping the server and check if they're actually valid emails. The next thing is probably gonna be something like research, right?

**[9:34]** So we might wanna do some research around these individuals. You can do that also in one prompt. So let's do something like, okay, could you research these companies and determine, and I spell terribly, who their ideal-- - Check has ruined numbers, so that's okay. - Or exactly, yeah, I just do everything by voice right now. Customers are return and answer between three and five words.

**[10:13]** Now this is basically a building research agents. So research of 25 companies in your imported list, yes, so it's one. It asks you questions when it's not sure of exactly what you mean. And it's easy to misinterpret my mess. But ultimately, it builds out inside of one of these columns a full research agent. Now one thing that for people will be quite impressive is the fact that this is a very sophisticated basically research agent builder that not only created the prompt here, and this is really based on best practice from the top GTMs in the market. It also built out the output schema, which is nerdy stuff that if you're not technical,

**[10:58]** you don't really need to know, but it's important. Let's just put it that way. If you're an Uber nerd, you can even come in and go under the hood and adapt lots of stuff. But ultimately, it also shoots out the outcome. So if we look inside of one of these cells by double clicking it, we're gonna see success as the status whether it found it or not, then company name and then ideal customer and it returns it in exactly the format that I asked for. This type of sophisticated agent usually requires crazy skills, but now we allow you to do it in one prompt with AI. The cool thing is, let's say you don't need the status thing here.

**[11:36]** Well, you could just delete this column, right? So we've built this in a way where you don't have to deal with all the complexity, but if you want to nerd out, you can go under the hood and go super hardcore. So we have things like, you know, you can bring your own, you can bring your own APIs, you can, you know, shoot leads into a table via web hook. You know, there's just a huge number of things you can do. You have a lot of control, but you can do awesome sophisticated things without having, you know, a degree in GTM basically. So that's kind of like some of the stuff in this qualification. So it's like, okay, could you qualify these leads for me?

### 12:23 — Auto-building your ICP and qualification from a website

**[12:23]** My, oh, let's just leave it at that. And I know that you guys are probably gonna say, well, for qualification, we probably need more information. So basically it's, it knows that. So it's like, okay, cool. I need some more like information about your customers. So it's like, do you have a website or domain? You can like upload. So that's one thing we could do. Let's say, yes, lean scale.com. Is it .com? .team, .team, .team, excellent. A little bit of promo thrown in there, lean scale.team. And basically it will send out an agent to go out and basically scrape Anthony's website. Sorry, Anthony. And, you know, convert all the information

**[13:09]** that it's finding into files. This is where it gets exciting because one big thing when you build an infrastructure like this, that's agentic is that you can provide it with context that it's able to then use for everything that you're doing. And that's why we've seen a lot of success with people just putting in the website, being like, build me a lead list. 'Cause it identifies it. It's like, hey, I think these are the people you're going after, is that correct? And if it is, it'll basically, you know, and you can fine tune it if you want. But if it is, then it'll basically build a list based on what it's figured out.

**[13:45]** And for most people, this AI is way better at you at creating ICPs and personas. So, however, if you're more sophisticated, obviously I would say, you know, you could take a different route as well. And these files basically are being written out now. So we can see that, you know, you guys are AI driven, go to market operations and revenue operations consulting firms specialize in B2B SaaS startups. And it basically breaks down everything about you guys that it can use as context. So when it's then, and it's only doing that right now to be able to qualify these leads against this. It's also created your actual ICP.

**[14:23]** So, high growth B2B startups, series A, series B, you need to have a bit of money if you want to work with LeanScale. They don't work for free. User personas, VP of RevOps, and then some also equivalent job roles. And yeah, it basically uses all of that as context to do things like copywriting, to do things like, you know, the qualification that we've just done. So here we see that these qualify terribly, incredibly cold because they're product managers. So, you know, but we can actually go ahead and check out what is done here. So this is no like, oh, we just kind of tried to see if it matched. No, no, this is an in-depth qualification agent

**[15:06]** based on the prompts of the best GTM engineers in the world that basically is multi criteria. So if we look at this, like qualification based on company type, based on funding stage, based on company size, based on revenue range, like it goes hardcore and it basically uses what we figured out as like the context of what you do, Anthony, as a way to do that. - Well, I like how it creates a non-scoring model and point system and built out that whole mechanism, which is usually like a couple of week project for a marketing ops team to do, all in one prompt. - Exactly. And it shoots out these, these don't actually cost any credits

### 15:47 — Copywriting and sending: 1–2% to 10% reply rates

**[15:47]** and you could just delete the ones you don't actually need to see inside of this view. But yeah, you know, out of a hundred, these are all terrible leads for you. Maybe we should have just used that, you know, we should have gone after the RevOps people in this example, just so you would have some actual leads leaving this demo, but yeah. Then after that, it's like, hey, can you copy rights? You know, write them a message. You can have full control over the message writing. You can also go back and forth with the AI being like, hey, like, you know, make it more casual. Or in the first line, could you mention their ICP

**[16:24]** that we like did research about here, right? So it's like, maybe it's like, hey, you know, if Anthony were to do outreach, he might say, hey, wondering if you're looking for more and then input, you know, ideal customer profile. We probably would want it in a slightly different format here, but it would be a good way to show, okay, this person kind of understands what I'm going after. And yeah, then you just plug it. We basically, you can integrate with many platforms. One of them is instantly, if you want to send them out, you know, you can plug them in and it just basically is going to send out those personalized messages.

**[17:00]** Anthony, we have seen a rise of like, basically positive responses from kind of like a classic one to 2% all the way up to like 10%. And the reason for that is that this allows you to build campaigns that are like sophisticated so quickly that now you can write, create these micro campaigns that are super targeted. And that's how we get out of this whole world of like spammy emails. It's by creating things that are super relevant to the right people at the right time, right? And that just turns out, if you don't spam people, a lot of people answer and if you're actually targeting them with something they're interested in,

**[17:40]** instead of, you know, some random offer that you're blasting to everyone, you get really good results. So yeah, that's pretty much a short demo. I could go into way nerdier stuff, but I'm sure that, you know, we don't want to get too, too much into it. - No, I think this is great. I think one, it highlights the ease of use. And I had a couple of questions, if we can hop back in. - Oh, cool. - I think, yes, there's so much, I'll just call it AI slot that hits my inbox, hits, you know, my LinkedIn feed. And I'm always looking for ways to like combat that. And I think the way to do it is to get more data, better fine tune data,

### 18:24 — Using intent signals the right way — don't mention them

**[18:24]** and then have a sophisticated way of reaching out 'cause I can't tell you how many times I get the, "Hey, I read your post on LinkedIn by my product." Like I can't read another one of those. So one of, what are some of the biggest intent use cases that you're seeing your product used for to help get in that timely information? Something like, yes, it is relevant to reach out right now. - Absolutely. So that basically is when we get into signals, right? And there's a bunch of signals built into this all the way from the fact that we build the lists with sort of like, you know, with live data. So we're not just using like a database. This is like live data.

**[19:12]** And we can, you know, we then know things like this person has recently changed jobs, right? So relevance happens when you, you know, or get back into the strategic seat, right? What do I mean by that? Well, if someone, for example, who's responsible of RevOps has recently started their job as, you know, head of RevOps, you know that there's certain, you know, 30, 60, 90 day priorities that they want to get done. And so relevance happens when you're able to get to those people at the right time, right? And there are a bunch of different sort of, you know, signals that you can use in building a list, but then also in the research itself.

**[19:54]** I was telling you this off screen earlier, one of our customers basically has a company that helps people, you know, convert more people post event or, you know, events. And, you know, he just basically built a list of his ideal prospects. And then he went and created a research agent that determined how many, you know, events these individuals or these companies were running over the past year. And then he was able to segment for those who had more than, you know, three to 10 a year, for example. And that allowed him to then, you know, have a much more, you know, in depth, like a much better pitch to those people and to avoid going after people

**[20:33]** who like don't really run online events and where this would be less relevant to them. But, you know, there's lots of actual, like I could, you know, and probably I'll drop for you a whole list of the different, you know, criteria you can search for. You can, you know, search for growth, you know, there's company filters around growth of specific departments. So that can be a very good signal that people are looking to improve X, Y, or Z, right? They're hiring people in the cybersecurity unit. You know that there's, you know, probably they're focusing a little bit on that aspect of things in their business. There's also research agents, right?

**[21:11]** They can do the job. So like you can go and basically figure out and do research about, you know, if they're a public company, like what did they, it's called a, what is it? It's a 10K, something like that. I'm not American, but basically, yeah. So the 10K, it basically outlines what they're trying to focus on for the upcoming year. And if you, and I think this is the biggest thing people get wrong about signals in general. They always want to mention the signal. It's like, hey, I saw that you posted about this, that. It's like, why don't you just, you know, like, you don't have to tell people that you know that they're currently hiring.

**[21:50]** They know that it's not, you know, and it doesn't impress them that you've figured that out. But it does qualify them as a more relevant lead to go after. So I think that's one of the big things that's happening, especially like for more sophisticated people who do these kind of like signal based campaigns is to always think that you have to have the signal inside your messaging. The truth is a lot of people are finding incredible value in just good old value emails, but just targeted to the right people who are in like, who are showing the right intent. But basically our tool allows you to bring in any of these signals.

**[22:31]** So anything that's possible, you could bring into your, you know, outbound campaign. And I think that's kind of another big thing that I want to like illustrate. What you're seeing here and what we're actually building and where this is going is gonna be as simple as like you input your website or you say who you're going after and it basically, there are just two touch points where it asks you, hey, is this lead list good? Yes, no, or we can adapt it. Do you like this copy? Yes, no. And everything else is handled by basically automatically figuring out the plays, the best plays to go after based on what your company is, the products,

**[23:12]** you know, and what you're trying to service. And that's where I call it a system of intelligence because there are just things that you can do when you're AI first, like we are, versus what most companies are, which is slap amazing AI on top of your product. It's just fundamentally a huge shift. So any company that built their infrastructure pre-AI is either having to rebuild their entire infrastructure or products that have from the ground up infrastructures and architectures built for AI or not, you know. And so that's kind of one thing that I think will be a lot more obvious in the coming months as we continue to release amazing features

**[23:57]** and make it both, you know, easy to use and also allow people to have that awesome complexity. That's amazing. No, I think there's a huge difference between AI native and throwing on some chat bots or, you know, GPT plugins, I think it's just fundamentally different. That makes a ton of sense on intent. I've recently, personal experience, I'm in the process of moving. We just sold our house or getting a new house. And I don't know what tools these moving companies and other ones are using, but I have definitely been targeted. And I was like, this is such a good use of intent. They saw that my house was sold. They obviously know I'm going to be moving.

### 24:46 — Empowering AEs: strategy + tech is the game-changer

**[24:46]** So now I have like a few moving companies that reached out. So I just think intent and timely messaging has to be the future of your outreach. And I agree, you don't have to let them know. They're not letting me know that, oh, I can, you know, they don't, we're not wasting a bunch of messaging and letting me know that I sold my house and I'm moving. They're just saying, we know you need. And when you're thinking about your emails, you only have so much real estate. So if you're like wasting that real estate on, I saw your LinkedIn post, I'm really inspired. You know that I don't really mean this. Like you just wasted so much time

**[25:24]** where you could have been baking in your company value instead, but it's still the right person to reach out to, like you mentioned. - Oh man, Anthony, you're singing in my ears right now because I think there's like this problem, this massive problem people are not talking about where it's like on one end you have these like sales people and these RevOps people who really understand selling, really understand the customer, really know what they're going for, but lack the technical skills to kind of execute on those things. And then you have like the GTMEs and you have like these like nerdier, like people who know how to use the tools,

**[26:02]** but have no idea about like sales and like angles and like what is gonna work, what's not like playbooks. And I think that one of the biggest things that maybe I don't emphasize enough is the fact that if you give the power of basically execution in these like sophisticated systems like we're giving them, it now gives the power to like an AE to be able to basically crush it because they understand the angles, they understand the strategy and they can build very creative and sophisticated campaigns around the specific needs of their company. And I think that's what kind of changes the game. Now, you'll tell me not everyone wants to like learn tech

**[26:46]** and I would argue like that's fine, but like honestly, most people are gonna be forced into it. So might as well be kind of at the forefront of that and then just select tools that like allow you to explore your creativity 'cause I think that's what people hate. What I hated and this whole journey starts from me not being a tech person, hating my life 'cause I need to pay a bunch of money to get a product built, then doing like outreach, having to pay more for that. And I just believe that like if you empower people who have the motivation, who have the know-how to actually do technical things, that combo between strategy and tech

**[27:23]** is just an absolute game changer, especially in the AI era. So that's what excites me and like gets me to like wake up every day it's like knowing the like, oh man like, and then I look like at the faces of AEs as DR managers like RevOps people like who start using it and like it's similar to like AI coding where it's like, oh, I can actually do this. It like gives them that realization that there's actually that spark they need to like engage into some learning, you know? 'Cause it's too easy to just lay, sit back and be like, you know what? I'm just not technical and I was the first guy to do that. So I can't, you know, judge anyone

### 27:59 — Collapsing the bloated outbound stack into one tool

**[27:59]** for being in that position. All I'm saying is that like, it doesn't have to be that way anymore. - Yeah, no, I'm really excited for the day and you know, this is an excellent example of it where the right people have access to the right tools. One last question that's platform related. So once you have everything, you mentioned great integration with Instantly. You have native sequencing within the platform as well. So once you have your list, you can really start reaching out to them. Writing some great messaging, not bad AI slot messaging, but like getting the right messaging to the right person. - Yeah, so the answer to the first one is that right now

**[28:44]** we have integrations with sort of Instantly for email and then HeyReach. We're building out some more integrations for people who already have these platforms. Usually when we talk about sort of RevOps people, they are already doing this. But yes, we're definitely building out sequencing ourselves as well so that people wouldn't have to, and that's another big thing. Like if we look at the market, honestly, they use Sales Nav to build a list, then they shoot it into a tool like Apollo to do like enrichment, then they might have two other enrichment platforms, then they might have a verification platform. Then they would take each account

**[29:24]** and put them into chat GPT to do the deep research with the special prompt that they crafted. We basically put all of that under one with like a fair credit system. And again, like this is based on the best practice from the best GTMs who get the best results with their campaigns. So we've combined the technical side with also the great tech all under one hood. And then it scales as you do, right? If you're not seeing results, it's cheap 'cause you're not using so much. And as soon as you start seeing results, like here's the longer term vision. This is a system of intelligence. So as soon as a campaign starts seeing positive

### 30:04 — The vision: look-alike audiences on autopilot

**[30:04]** sort of responses, this will have the intelligence to go and find lookalike audiences of people who match exactly who you're going after. And you wake up in the morning while you're drinking your coffee and it's like, hey, we found 57 people who fit perfectly based on who's responding positively to this campaign. Would you like to add them to this campaign? And you just say yes. 'Cause frankly, let's get back to selling, right? That's what I truly believe. - Well, I'm really excited for the vision, Christian and really excited for what you put together so far. Christian, I'm so excited about what you've built,

### 30:39 — Where to find Outbond + closing

**[30:39]** what you're building, what the future and the vision is. I think everything you've done so far is gonna really enable people to just market to the best of their ability, sell to the best of their ability. And I love that it's completely AI native. So I know it's gonna give you flexibility to be creative and create things that we're probably not even thinking about right now. Christian, what's the best way for people to get their hands on out bond or get connected with you and start experiencing the platform for themselves? - Absolutely, yeah. So, well, first of all, outbond.ai is where you can find the platform.

**[31:20]** Also, if you're looking to get in touch with me, LinkedIn is a good place. So if you can spell my last name, Christian Peverelli, then you can find me. Shouldn't be too, too difficult, but yeah. And the last thing I'd say is that you're a team of people who are looking to implement this. We're at a phase right now where we can help on the onboarding process because our success is your success. And so yeah, we're super stoked. But I wanted to thank you and the whole Lean Scale team as well because I've been watching you guys' videos for a while now and you do a fantastic job of really bringing to light some of the important learnings

**[31:58]** that people have and helping other teams benefit from the best practices that are happening in the market. And for me, that's like, again, that's like giving back. So thanks for what you do, man. - Yeah, I really appreciate that. And absolutely, it's a pleasure to do it. It's all of the stuff I wish I knew when I was an operator in a startup. I ran RevOps for three, VC-backed companies, high growth expectations, high pressure, high stakes. And I was completely underarmed with lack of information, lack of technology, what works, what doesn't work. And the space moves so fast. There's so many new tools, so many new approaches,

**[32:38]** and the market changes their response so quickly. So we're really excited about the work we're doing on the podcast and our content and the work we do with our customers. But what really gives us a lot of relevance is all of the new tech and getting people connected with the best tools to scale their GTM. So thanks for what you're building. Thanks for what you're doing. And as you continue to build, we'd love to have you back. And I can't wait to see what you guys do next. - For sure, yeah, I'm stoked. I even get impressed by this product as I'm building. I'm like, "What, it can do that?" And I also love that we're basically like building companies

**[33:18]** because of our own past traumas, right? Like, "Oh man, I was underarmed. I was not technical, and I wish I could do this." Yeah, it's really awesome. And I think that I see a lot of paths in what you do. So yeah, great joining and I'll be back. I appreciate you, man. - Amazing, appreciate you, Christian. Thank you. Take care. Bye.


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