There’s a reality that’s starting to take shape: for some teams, AI is already handling the majority of customer conversations. At our recent Fin x @AnthropicAI meetup, we shared how this is already happening in practice. Fin now resolves over 80% of support queries end to end. The technology is here, but adoption is still catching up. That progress is unlocking something bigger. Fin is moving beyond support toward a broader vision, where one agent can deliver a consistent, high-quality experience across the entire customer lifecycle, from day zero to year ten. Watch more below to hear how close we are to that future, and what it takes to get there.

“Trust has been a multi-year journey.” At our recent Fin x @linear meetup, we explored what it actually takes to build trust in AI Agents, and why it starts with giving systems room to prove themselves, limiting their blast radius early on, and gradually expanding their role as customers see what they can reliably do without risk. Watch more below to hear how teams are measuring and earning that trust in practice.

What does it actually take to move from deploying AI to using it in ways that change how a business operates? That was the focus of our recent Fin x PostHog meetup in San Francisco. The conversation explored where companies are making real progress (and where things are still unsettled) – from the declining importance of traditional interfaces, to the realities of outcome-based pricing, and the challenge of driving true adoption inside organizations. A big thank you to Tim Glaser, Co-founder of PostHog, and Junan Pang, VP of Customer Success and Solutions at Fin, for a candid and thought-provoking discussion – and to everyone who joined us and brought sharp questions to the room. If you’d like to join a future Fin meetup, subscribe to our Luma calendar to see what’s next.

“You only pay for Fin when it does the job.” At our recent Fin × @AnthropicAI meetup in Paris, this came up as a practical answer to a bigger question: how do you actually build trust in AI agents? For Jordan Neill, it starts with aligning incentives. Fin is designed to resolve customer problems end to end, and you only pay when it does. No usage-based ambiguity or paying for potential. Only outcomes. It’s a simple idea, but it changes the relationship entirely. Trust is built into how the product works. Watch the clip below. To join a future Fin meetup in your city, subscribe to our Luma calendar.

Quality in AI isn’t just always a clean, single metric. It’s often something you feel when it works the way you expect, consistently. That’s what we explored at our Fin x @Linear meetup in London. @brian_donohue (Fin) and @matthijswolting (Linear) shared how teams are building for quality in practice, from combining AI scoring with human review, to creating visibility into what agents are doing and where they fall short. The throughline: trust is built by giving teams the tools to understand, test, and improve AI systems over time. Watch the highlights below. And if you want to watch the full conversation, see the link in the comments. If you’d like to join a future Fin meetup, subscribe to our Luma calendar to see what’s next.

Earlier this week in London, we partnered with @attio to explore how modern GTM teams are actually selling with AI, and where today’s approaches are still falling short. The conversation, led by @rati_zv (Fin) and @nicolasosharp (Attio), alongside @ciaran_nolan (Fin), focused on a growing mismatch: buyers are arriving faster, with more context and higher intent, but the systems they meet are still built for a slower, linear world. That gap set the stage for something new. We introduced Fin for Sales. It works like the ultimate salesperson: engaging prospects at just the right moment, guiding product discovery, suggesting tailored options, and qualifying in real time so no lead gets left behind. We also heard from Fin's @destraynor and Bethany Clark, and Attio's Will Jones, who shared how teams are starting to rethink the top of funnel, from static forms and handoffs to real-time, AI-led engagement. One theme that stood out was the idea of “lossless context.” Not just within a single interaction, but carried from the very first website visit all the way through to long-term customer relationships. As AI takes on more of the execution, the opportunity is to design systems that remember, adapt, and build on every interaction, so nothing gets lost as prospects become customers. From rethinking the role of the sales funnel to the emergence of new roles like GTM engineers, the discussion made one thing clear: this is more than simply adding AI to existing workflows, it’s redesigning them from the ground up. Learn more about Fin for Sales at the link in the replies 👇

Great to partner with you, and be in such great company!

On the road to 2x, one of our biggest bottlenecks was PR approvals. Manual processes can't keep up. So, we created a PR review Agent. Today, over 19% of our PRs are auto-approved with no human reviewer in the loop. It might sound reckless but our data shows otherwise.

"I don't know." That's the most powerful thing you can tell a customer. At our recent Fin × @AnthropicAI meetup in Paris, Ryan O'Holleran shared why honesty beats confidence when it comes to building trust — especially now, when AI can generate a plausible answer to almost anything. Fluency is easy. Knowing when not to answer is much harder. The best AI systems don't just get things right. They know when to pause, when to escalate, and when saying nothing is better than saying something wrong. Find the full conversation in the comments. And if you want to join a future Fin meetup in your city, subscribe to our Luma calendar 👇

New from @karenchurch – If Claude Code can do our jobs, what are we for? "AI commoditises execution. It does not commoditise judgment. And for research and data science teams, that's the difference between being relevant and being replaced." Read the full article here 👇 linkedin.com/pulse/claude-co…

What does ‘high quality’ actually mean in AI? At our recent Fin × @linear meetup in London, @matthijswolting (Engineering, Linear) put it simply: a lot of it is a vibe. You know it when an Agent does what you expect consistently, reliably, and without needing constant steering. The challenge is that feeling is hard to capture in numbers. @brian_donohue (VP Product, Fin) added the second layer: when it’s your product, quality also needs to be measured: resolution rates, real outcomes, whether the work actually gets done. That tension between what you can measure and what you can feel is where the best teams operate. Watch the clip below, and find the full conversation in the comments to go deeper.

You can watch the full conversation between Jordan and Ryan here: piped.video/6sR9KJFncrQ

“Last year it was: how do I make sure these AI models don’t hallucinate in a demo I’m showing internally? This year it’s: how do I make sure millions of customers are using my product, and I can sleep through the night?” That’s the moment AI is in right now, ensuring it can be trusted with real work. Because once AI is customer-facing, it becomes your brand. At our Fin × @AnthropicAI meetup in Paris, Jordan Neill (COO, Fin) and Ryan O'Holleran (GTM Leadership EMEA, Anthropic) kept coming back to the same idea: the technology is already there. What’s missing is the confidence to let it operate on your behalf – at scale. And that changes how you build. You design systems that know when not to answer. You draw hard lines around where humans stay in control. You build trust into the model, the product, and the way you bring it to market, not as a layer, but as the foundation. Watch their conversation below.

Watch their full conversation here: piped.video/AVVKO9cbRtw

Building an AI Agent for one company is straightforward. But making it work across every team, system, and dataset in any enterprise is where things get challenging. At our recent Fin x @Snowflake meetup in San Francisco, Jordan Neill (COO, Fin) and Baris Gultekin (VP of AI, Snowflake) broke down why context is the real bottleneck. As models improve, the challenge shifts to helping agents understand the nuances of each business (think data, language, and definitions) so they can operate reliably at scale. You can watch the full conversation at the link in the replies, and if you’d like to join a future Fin meetup, subscribe to our Luma calendar.

Complex queries are a small percentage of your support volume, but they take up most of your team’s time, and leave the biggest impression on customers. AI Agents have automated simple queries, but as soon as processes become complex and multi-step, things get harder to build, test, and maintain. You're typically told you need a forward-deployed engineer and weeks of engagement just to get started. And then more waiting every time something needs to change. We built Fin Procedures so your team owns this end to end, from creation to testing to monitoring. And we're not stopping there. We've been continuously shipping improvements (for example: AI-powered procedure review, failure reporting, version history, and email simulation) with more on the way. Since launching, Procedures has handled over 1.5M conversations across industries, with a ~28.9% proven improvement in CSAT. And now Procedures is available to every Fin customer.

On March 31 we had a full room of CX and product leaders and builders in London for our Fin x @linear meetup on delivering high-quality AI experiences. Together with @brian_donohue (VP Product, Fin) and @matthijswolting (Engineering, Linear), we explored what it takes to move beyond demos and build AI systems people actually trust in production. One theme came up again and again: quality isn’t just a metric. It’s a combination of signals including resolution rates, CSAT, and the instinct you build from reviewing real conversations and outputs over time. We also got into the trade-offs that come with shipping AI at scale. From faster responses reducing trust, to new features temporarily impacting performance  and why oversight has to be built in from the start, not added later. Thanks to everyone who joined, asked thoughtful questions, and pushed the conversation forward. If you’d like to join a future Fin meetup, subscribe to our Luma calendar to see what’s coming next.

“It’s always easy to build demos and POCs, but the real world is messy.” That tension defined our recent Fin x Snowflake meetup in San Francisco, where we explored what it really takes to deploy Agentic AI in the enterprise. Jordan Neill (COO, Fin) and Baris Gultekin (VP of AI, @Snowflake) dug into the “iceberg” behind production systems – why demos are easy, but real-world deployment depends on shared context, strong evaluation loops, and architectures that can handle messy, unpredictable environments. As Agents move from internal tools to enterprise-wide systems, reliability (not raw capability) is what makes the difference. The shift is already underway. The technology is here, but adoption now depends on trust, feedback loops, and organizations rethinking how work actually gets done. Take a look at the highlights below, and if you’d like to join a future Fin meetup, subscribe to our Luma calendar.

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Trust in AI isn't a feature. It's something you have to earn. That was the thread running through our conversation with @AnthropicAI at Fin Labs Paris, and it's harder than it sounds. Jordan Neill (COO, Fin) and Ryan O'Holleran (GTM Lead, EMEA, Anthropic) dug into the gap between what AI can do and what teams actually deploy. The technology isn't the bottleneck. Confidence is. When AI represents your brand, reliability, predictability, and knowing when not to answer matter as much as capability. Including the decision to say "I don't know." Want to be in the room for the next Fin meetup? Subscribe to our Luma calendar.