Exportly.ai

Certa.ai is a Gartner Magic Quadrant leader in Third-Party Risk Management Tools. We're proud to partner with their GTM team 🚀

"Clay is not rep friendly." That's what ZoomInfo says to prospects. Then the RevOps team of a $20B+ company replaced ZoomInfo with Clay + Exportly.ai entirely. Here's the full playbook (and why many are making the switch): “we’re not rep friendly” refers to Clay’s lack of a native Chrome extension. By design, Clay isn’t meant for reps. And if you go by feature comparison, then you’re evaluating software all the wrong way. It’s not who has the better features that matter. Features don’t matter in the era AI. It’s about infrastructure and building blocks to build new things. This is the approach we’ve taken by partnering with Exportly. 𝗖𝗹𝗮𝘆 𝗽𝗿𝗼𝘃𝗶𝗱𝗲𝘀 𝘁𝗵𝗲 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗮𝗯𝗹𝗲 𝗱𝗮𝘁𝗮 𝗹𝗮𝘆𝗲𝗿. 𝗘𝘅𝗽𝗼𝗿𝘁𝗹𝘆 𝗽𝗿𝗼𝘃𝗶𝗱𝗲𝘀 𝘁𝗵𝗲 𝗿𝗲𝗽 𝗶𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 (Chrome extension) that surfaces any workflow that’s built with Clay. Reps can trigger workflows while on a profile or prospecting. Ex. “Account Target” workflow sends a company LinkedIn URL to a Clay workflow that: - Researches objectives & context needed for personalization - Sources the buying committee - Enriches the buying committee with profiles, email, mobile - Generates personalized emails - Sends the data back to Exportly’s chrome side panel - Sends emails to Gong ready to review, click, send in Engage The 𝗽𝗼𝘀𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝗮𝗿𝗲 𝗲𝗻𝗱𝗹𝗲𝘀𝘀 since Clay is the backend. The new era of 𝗚𝗧𝗠 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝘀 𝗳𝗹𝗲𝘅𝗶𝗯𝗹𝗲 & 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗮𝗯𝗹𝗲 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲. Migrating off ZoomInfo’s rep experience isn’t that hard either. 👀 Elliot O'Connor 𝗮𝗻𝗱 𝗜 𝘄𝗿𝗼𝘁𝗲 𝘂𝗽 𝗮 𝟱 𝘀𝘁𝗲𝗽 𝗽𝗹𝗮𝗻 𝗮𝗻𝗱 𝟯𝟬 𝗱𝗮𝘆 𝗺𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗽𝗹𝗮𝗻 𝘁𝗼 𝗴𝗲𝘁 𝘆 𝗼𝘂𝗿 𝘁𝗲𝗮𝗺 𝗼𝗳𝗳 𝗭𝗼𝗼𝗺𝗜𝗻𝗳𝗼 𝗮𝗻𝗱 𝗶𝗻𝘁𝗼 𝗖𝗹𝗮𝘆. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝗴𝗿𝗮𝗯 𝗶𝘁 𝗵𝗲𝗿𝗲: clay.link/aUrApdf

We just crossed 1M leads sourced with Exportly.ai + Clay after onboarding our first user 11 months ago. 235K sourced in May alone: 23% of the year in the latest month. But volume isn't the headline. Conversion is. 1M leads isn't actually a lot of leads in this world of rampant automation: one GTM Engineer could source that many leads in a week (or even a day!) with Clay or Claude Code etc. So what happened to all those leads? Those 1M leads turned into roughly 20,000 meetings booked, an avg. 2% meeting-booked rate across our customers. Pound for pound, that beats just about any automated workflow at scale out there. Human reps are better than fully autonomous systems at working and converting leads - and the enriched data their RevOps team paid a lot of money for - into pipeline, and they do it at scale across a huge range of situations, day in and day out. AI-Native reps, empowered with technology and the right mindset are capable of a lot, just like AI-native engineers. We've been quietly seeing it in our data for months and we'll be sharing more of this publicly soon...

The best “AI SDR” ideas aren't about replacing SDRs ‼️ They're about giving great reps better systems. Here are 5 takeaways from Clay's livestream yesterday on the AI-native SDR: 1. Start the day from signals, not a task list.   Johnny DeFazio from Notion talked about reps beginning in an internal app that surfaces account-level signals: product usage, engagement, warmth, and activity. That becomes the source of truth for what to action. 2. Build your own signal layer if you don’t have PLG data.   Nicole McKelvey from Decagon shared how her team hunts for signals like AI initiatives, leadership changes, competitor mentions, website visitors, new hires, champions, and warm paths. The goal is to give reps a real reason to reach out. 3. Replace hundreds of sequences with one smart shell.   Rob Cook from Clay described moving from maintaining endless persona/use-case sequences to one shell sequence where AI stacks signals, selects proof points, surfaces pain, and personalizes the message. Humans still review, but the system does the heavy lifting. 4. Teach the workflow manually before automating it.   Rob also said Clay has reps do the job manually before leaning on automation. That one stuck with me. If you can’t do the job well yourself, you probably can’t prompt or automate it well either. 5. Fix data before obsessing over messaging.   Nicole McKelvey put it well: garbage in, garbage out. Better targeting and cleaner data make every downstream workflow better. The big takeaway: The AI-native SDR isn’t a bot. It’s a rep with better context, better signal prioritization, better prep, and tighter feedback loops with GTM engineering and ops. Thank you Davide Grieco for hosting a great session 🔥 #GTMEngineering #SalesDevelopment #AI #OutboundSales