AI outreach doesn’t fail quietly anymore. It fails in your prospect’s inbox, at scale. An email referencing the company someone left six months ago. The same DM twice because the record never merged. A “recent conversation” the prospect already corrected. Jacki Leahy 🪄, fractional RevOps leader, calls this the “uncanny valley of AI outreach”. And it starts in the CRM. Humans compensate for bad data. AI compounds it at machine speed. In our latest blog, Jacki breaks down why episodic cleanup can’t protect an AI-powered GTM motion and what continuous data trust actually looks like. 👉 Read more: https://lnkd.in/g6Jqc5UE
Common Room
Software Development
Seattle, Washington 29,750 followers
AI-native GTM Platform powering Precision GTM at scale
About us
Common Room is the AI-native GTM platform powering Precision GTM at scale. We unify first-party customer data with real-world buyer signals into a continuously updated system of complete and trusted buyer intelligence. Revenue teams use AI agents to prioritize accounts, understand what’s changing, and orchestrate action — driving faster execution and more consistent pipeline. With enterprise-grade governance, fully managed integrations, and flexible permissioning, Common Room activates buyer intelligence across the surfaces where teams already work — including the Common Room app, Slack, email, browser extensions, Salesforce, and AI assistants.
- Website
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https://commonroom.io/?utm_source=linkedin
External link for Common Room
- Industry
- Software Development
- Company size
- 51-200 employees
- Headquarters
- Seattle, Washington
- Type
- Privately Held
- Founded
- 2020
Locations
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Seattle, Washington, US
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San Francisco, CA, US
Employees at Common Room
Updates
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Common Room reposted this
The CLIs are making a comeback, and it has nothing to do with nostalgia. 📺 My first encounter with a CLI was during an internship at a Swiss bank. I was confused why so many non-technical employees preferred it over the "modern" UI apps. Then I tried it: fast, keyboard-only, no mouse, seamless workflows. Developers have known this for decades. But calling it a "comeback" undersells what's actually happening. CLIs never left. What's new is that UI-first SaaS tools which never shipped one before are now launching them, usually branded as "headless," and it has very little to do with giving humans a faster interface. The trigger is AI agents. 🤖 Coding agents like Claude Code, Codex, and Cursor are often more comfortable invoking CLIs than MCP servers. Smaller token footprint. More reliable for chaining steps. A few SaaS tools that recently launched or revamped one: 📝 Notion (ntn) → shipped May 13, alongside Workers and the new Developer Platform ☁️ Salesforce Headless 360 → full platform exposed via API + CLI for agent access from any surface 📧 Google Workspace (gws) → 100+ agent skills baked in, built explicitly for humans and agents 🚀 Google Antigravity (agy) → agent-first dev platform, launched at I/O on May 19 🏠 Common Room (cr) → just shipped yesterday: GTM data as a scriptable surface, not a dashboard At Lenses.io, we've had a CLI since our early days, but the spotlight was always on the UI. That's changing. Agents are now first-class users of Kafka, and the interface they want isn't UI-based. What's the next software that needs to ship a CLI?
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Headless changed the content stack. GTM is next. For years, buyer intelligence lived in one place: the dashboard. Powerful, but stuck. You couldn't share it across systems, run it in a pipeline, or hand it to an AI agent. It just sat there. Today that changes. We're launching the Common Room CLI and expanding our MCP Server with write capabilities. Your buyer intelligence—identity-resolved, continuously enriched, signal-unified—is now programmable infrastructure. Run it from a terminal. Pipe it into any LLM. Let Claude write back to it. AI agents don't log into dashboards. They call tools, retrieve context, and execute. Now yours actually have something worth working with. Buyer intelligence just went headless. Now go build on it. Link in comments to learn more. #GTM #MCP #CLI #AIAgents #CommonRoom
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Everyone's rolling out AI in GTM, but not everyone's seeing it work. The teams getting the most out of AI aren't winning on tools. They're winning because their CRM data is actually trustworthy. Feed stale records, duplicate accounts, and missing contacts into your AI plays, and you're not scaling execution. You're scaling noise. On June 4, Khaled AlSaleh (RevOps leader at incident.io) is joining us for a live webinar to talk through how his team got the data layer right before scaling AI execution. 🗓️ June 4th @ 9AM PT / 12PM ET 🔗 Register → https://lnkd.in/gwssQfY7
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"I wouldn't say minutes versus hours. I'd say minutes versus days — or weeks." That's Khaled AlSaleh, RevOps Leader at incident.io, describing what changed after implementing DataAgent. He wasn't talking about a one-time cleanup sprint. He was talking about the ongoing work of keeping a CRM accurate across 140,000+ accounts — without a dedicated data team to do it. DataAgent is Common Room's new execution layer for CRM accuracy. It continuously surfaces what's drifted — duplicate accounts, stale contacts, unmatched records — and makes it actionable without requiring manual audits or upfront configuration. For incident.io, that meant: ✔ Account duplicates from ~3% down to 0.8% ✔ 3,700 unmatched contacts linked to real accounts ✔ A GTM system the team can actually execute from The goal was never just cleaner data. It was a foundation reliable enough to run AI workflows, territory planning, and outreach from…without second-guessing what's underneath. That gap between what your CRM says and what's actually true? That's what DataAgent closes. 👉 Read the full story: https://lnkd.in/eRsvGuZb #GTM #RevOps #DataAgent #CRMAccuracy #SalesOps
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Jacki Leahy 🪄 nails it. One-off fixes will never outrun the rate of data decay. CRM hygiene shouldn't be a project you come back to. It should just happen. Continuously, automatically, in the background. All the time. That's what DataAgent does. It monitors your CRM for outdated records, job changes, and duplicates, and resolves them on an ongoing basis. No manual cleanup. No complex logic. No broken workflows. So when AI acts on your data, it's actually working from something accurate.
The pace of AI is moving faster than the pace of our CRM data. Every team I work with has a list of one-off fixes — a duplicate to merge here, a job change to update there. It feels productive but it's not. It's onesie-twosie work that will never catch up to the rate of decay. Fixing records one at a time feels productive, but decay always moves faster than cleanup. The folks at Common Room recently walked me through DataAgent. What stuck with me wasn't any single feature — it was seeing duplicates, job changes, and outdated records get cleaned up continuously, without anyone having to chase them down. This is how CRM hygiene should be handled. (or, since i'm in London this week... SORTED! 💂 )
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Your CRM data has a half-life. And the problem is, your AI doesn't know that. It takes whatever's in the system and executes. Wrong title? Bad decision. Stale company? Scaled embarrassment. Duplicate record? Your buyer just got three LinkedIn DMs and now they definitely know it's automated. Fractional RevOps Leader Jacki Leahy 🪄 calls it the "uncanny valley" of AI outreach. We call it what happens when teams treat data cleanup as a project instead of an operating discipline. AI is only as powerful as the data foundation it's built on. And right now, most CRMs are quietly decaying underneath every workflow teams are trying to scale. The fix isn't another cleanup sprint. It's continuous data trust. Read how Jacki thinks about it, and what a system that actually maintains itself looks like in practice. 👇 https://lnkd.in/g6Jqc5UE
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Your intent vendor just flagged Acme Corp as high priority. Someone there is researching your category. Pricing page visit, topic surge. The whole signal package. Then your rep opens the account, pulls up LinkedIn and starts guessing which of the 11 people at that company is actually behind it. That's intent data. But buyer intelligence tells you it's the VP of Revenue, three weeks into her new role, whose last company ran your product. That's not a signal. That's a first line. What's your team's process when an intent signal lands with no contact attached? 👉Read more: https://lnkd.in/e_eSCT4s #GTM #BuyerIntelligence #IntentData #RevOps
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Grateful for the mention, Mario Moscatiello, and love seeing the Airbyte playbook spelled out this clearly. This is exactly what Common Room is built for: to know which accounts are worth your team's time before you ever reach out. Signal first, outreach second. Worth a listen. 🎧 👇
Mario Moscatiello doubled Airbyte's pipeline growth rate in his first year as VP Marketing. The hot take? Almost none of it came from sending more emails. We had him on S2E5 of the GTM Engineer School podcast. Before Airbyte, Mario led growth at Pusher, GitBook, and also wore VC hat at Flex Capital. He's been around devtools long enough to know the difference between volume and signal. I've also known the guy for more than 10 years — we met at our first startup back in London in 2016 — he's a real friend and of my favourite humans that I can (also) talk about work to. ❤️🔥🤌 I digress. In the pod, we chat about: 1) Foundations Before Automation Is The Real Step Change GTM engineering is a step change only when product market fit is in place and revops data is clean. AI is a multiplier of whatever foundation exists. Bad data equals bad signal equals bad results, and AI in the mix is a multiplier effect. 2) Owning Workflows End-To-End Is The Leverage Unlock Every team member can now own a workflow from idea to live. Paid SEO goes from idea to keyword research to blog post with assets and published in an hour. The SDR manager goes from prospect list to email copy to launched campaign in the same hour. AI compresses the wait between specialists. The right framing is force multiplier per role, not headcount replacement. 3) Warm Outbound Is Signal Triangulation, Not Message Volume The doubling of Airbyte’s pipeline growth rate did not come from blasting cold sequences. Two specific plays. First, events: dump the post-event lead list into Common Room, rank by who has signed up for the product or used the open source repo, and let SDRs only call the warm subset. Second, PLG signups: when an engineer signs up, outreach to them, AND prospect for decision-makers in the same org, AND warm those decision-makers with ads BEFORE the SDR call. 4) Hire Barrels, Not Ammunition. Then Outsource The Deep Expertise Mario hires generalists who can take a project end-to-end without a playbook over narrow specialists. The best SDR he ever hired was selling pest control door to door. Four traits to look for. Agency: just do the thing, do not tell me you will plan to do the thing. Curiosity: the playbooks that worked five years ago do not work now. Taste: AI brings the cost of writing copy and code to zero, and taste is what differentiates. Chip on the shoulder: something to prove. Tune in to learn more about Mario and his operators' hard-earned learnings on his journey — truly worth your time. Full episode with Mario here: https://lnkd.in/ewGf3WfT
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Somewhere out there right now, a RevOps team is celebrating their new AI workflow. But next quarter, they'll be asking why reps stopped trusting it. Not because the model failed. Because nobody asked what the model was actually running on. Stale contacts. Duplicate records. Routing logic held together with Zapier and good intentions. A CRM that hasn't been touched since last quarter's cleanup sprint (that never finished 😬). You get the picture. AI doesn't fix bad data. It just moves faster with it. The teams getting real leverage from GTM AI aren't just building better agents. They're building on top of something solid: a complete, continuously updated picture of the buyer that AI can actually operate on. Without that foundation, you're not creating efficiency. You're creating faster chaos. 👉 New blog: The real GTM question isn't build vs. buy. It's whether your AI has anything solid to stand on. https://lnkd.in/euUiF7GR #GTM #RevOps #AIAgents #BuildvsBuy #B2BSales #SalesOps