In competitive markets, I see two options: try to win the game competitors set, or choose to play a different game. In the "Customer Agents" category, I’ve watched too many glossy, fabricated demos—especially around voice—mask the real challenges. Voice is just extremely hard. We all know the future of customer experiences will be Agent-driven voice, yet most of us haven’t actually spoken with a modern AI Agent when calling a business because the tech hasn’t been truly ready in the wild. Today, the bar moves.
What changed? There’s a live, public demo of cutting-edge voice tech you can stress test yourself—no smoke, no mirrors. I recommend taking it for a spin: https://fin.ai/voice. It’s fast, natural, and, yes, very, very good.
For context, yesterday brought Apex Flash, their newest and fastest model, built for the unique demands of low latency channels like voice. Today comes Fin Voice 2, a major upgrade to Fin Voice with over 20 new features, and the first product built on Apex Flash.
Here are the three things that stood out to me—and why they matter for customer support AI strategy and product strategy.
First — thanks to Apex Flash, Fin Voice 2 is now the fastest, most natural Agent for phone, with higher resolution rates and customer satisfaction scores than ever before. Apex Flash is trained on millions of customer experience interactions, fine tuned for customer service, and can be configured to understand all your knowledge and follow all your policies. The result is higher resolution at significantly lower latency—the best of both worlds for voice AI agent performance.
Speed and naturalness here aren’t accidental. Most voice AI products are slow because they convert speech to text, send it to a general model, get a text answer, and then convert it back to speech. Fin Voice 2 was designed to work differently, separating the real time layer that handles speech processing, and the layer that generates answers. That architecture is purpose-built for the demands of customer service on voice.
Powered by Apex Flash, Fin Voice 2 raises the bar on quality and speed—boosting resolution rates and guidance following while cutting time to first audio and semantic search latency, with a lift in CSAT too.
Second — Fin Voice 2 can handle complex queries end to end: taking actions in external systems, verifying callers’ identities, processing refunds, booking appointments, and more. Phone is a high-stakes channel, and Fin adapts to customers across emotional states, clarifies when needed, and confirms key details before taking action. Most of the time, Fin can resolve the query in full, and when it can’t, it seamlessly hands off to the human team, maintaining full customer context and history. You also get multiple improvements to call quality, plus proactive outbound calls to follow up on unresolved issues—all orchestrated by robust AI workflows.
Third — Fin Voice 2 gives you total control with industry-leading tools to configure and manage how Fin behaves. You get rich, detailed insights into call behavior and quality, the most common topics of calls, and one-click recommendations to improve. As with everything in Fin, you can fully self-serve and then manage it all with ease, without requiring professional services. Many vendors only let you set up their voice agent under supervision; with Fin, you get everything you need to iterate fast.
If you haven’t tried the demo yet, go check it out: https://fin.ai/voice. If you prefer to wait, don’t be surprised when you end up speaking with it at a favorite brand soon.
From a product management lens, this is what matters: latency is a feature customers feel; transparency builds trust in enterprise AI; and control is non-negotiable for CX leaders. The combination of a purpose-built, agentic AI architecture, measurable gains in resolution and CSAT, and true self-serve configuration signals that voice is moving from prototype theater to production reality. That’s the different game I want our industry to play.
Every moment of friction in a product carries a hidden cost: attention drifts, motivation wanes, and the next click becomes a support ticket—or worse, silent churn. Over the years, I’ve learned to treat “stuck” as an urgent product signal, not just an operational nuisance. When we unstick users in the flow, we protect revenue, brand trust, and the momentum that powers product-led growth.
Learn how Amplitude’s Global Support team uses AI Assistant to reduce support tickets, prevent user churn, and increase conversions.
I reference that line often because it captures a proven pattern: meet users where confusion peaks and resolve it instantly. In my practice, the formula is straightforward—pair behavioral analytics and session replay with a just-in-time AI Assistant, routed by clear driver trees. This transforms support from reactive firefighting into a proactive, in-product experience that accelerates onboarding and boosts user activation.
Here’s how I operationalize it. First, I use Amplitude analytics and behavioral analytics to surface high-friction steps—pages with elevated drop-off, loops, or rage clicks. Session replay clarifies the “why” behind the numbers, while cohort and retention analysis reveal who’s most at risk. Then I deploy targeted in-app guides and tooltip design to preempt known pitfalls, while an AI Assistant handles real-time questions with context from our knowledge base and product docs.
The AI Assistant is more than a chatbot. With well-structured AI workflows, it detects intent, pulls precise snippets from docs-as-code, and handles routine issues instantly. When complexity spikes, it executes a graceful handoff to consultative support via Intercom or a Zendesk integration—preserving conversation history and sentiment cues—so humans spend time where judgment matters. This hybrid model keeps response times low without sacrificing quality.
To de-risk changes, I lean on A/B testing and feature flags. I measure time-to-value, activation rate, and funnel conversion as leading indicators, while tracking ticket deflection, CSAT, and NRR as trailing indicators. The goal isn’t just fewer tickets; it’s faster learning loops and a compounding improvement in user outcomes. When we see activation curves steepen and onboarding friction flatten, we know the system is working.
Practically, I start with the top three friction points in onboarding, implement narrow in-app guides, and deploy the AI Assistant with strict guardrails and clear escalation paths. Weekly reviews align product, customer success, and solutions engineering around shared telemetry—so we tune prompts, content, and UI patterns together. Over time, I’ve seen ticket volume decline meaningfully, while conversion and retention rise as users experience fewer dead ends.
If you’re evaluating where to begin, identify the moments where confusion compounds—pricing configuration, integrations, and data mapping are common culprits. Then introduce targeted, context-aware help right where users hesitate. You’ll not only prevent “every stuck user” from turning into a ticket—you’ll convert friction into confidence, and confidence into growth.
Inspired by this post on Amplitude – Best Practices.
I keep asking myself a simple, high-stakes question: what does it take to build an AI customer support agent that actually knows when it can't help — and says so?
Recently, I dug into how Jamie Hall (Co-founder & CTO), Xharmagne Carandang, and Rona Wang at Lorikeet are answering that question for enterprises in regulated industries. Their target outcome is refreshingly concrete: an agent that responds like the best customer support you’ve ever had — one that knows you, gets things fixed, and hands off gracefully when it’s out of its depth.
What resonated first was the honesty about early missteps. The team explored reflection tools and information dashboards before a healthcare startup reframed the job-to-be-done with a blunt directive: just help us clear the inbox. The earliest prototype wasn’t flashy — a command-line script spitting out a CSV — yet it paved the way for a scalable, measurable foundation.
Today, the system runs on a dual-agent architecture: a Concierge that handles customer tickets end-to-end, and a Coach that helps customers configure, test, and continuously improve it. That split is more than a technical choice; it’s a product strategy that separates operational resolution from the meta-work of quality, guardrails, and evaluation.
The backbone principle is "AI humility" — defaulting to a human handoff when uncertain. In practice, this isn’t about avoiding responsibility; it’s about preserving trust. When an agent signals uncertainty, it protects brand equity and customer experience while still accelerating the path to resolution.
Lorikeet integrates with Zendesk and Intercom instead of replacing them. That decision respects the entrenched workflows and analytics ecosystems support leaders already rely on, and it reduces adoption friction while enhancing existing queues, macros, and reporting.
The UX has evolved from a workflow builder to a conversational interface — and yet the blank chat box is still hard. Guardrails, prompts, and example-led onboarding help teams get started without forcing them to be prompt engineers. When you’re aiming for low cognitive load, a hybrid of guided steps and conversational nudges works better than a pure canvas.
One of the most nuanced patterns is "resolution in the loop": how human agents unblock the AI without taking over a ticket. Instead of a full manual escalation, humans can provide a targeted nudge — a missing piece of data, a policy citation, a link to a system of record — and let the Concierge finish the job. That collaboration preserves productivity while keeping humans in the quality loop.
Guardrails turned out to be deeply domain-specific — a cannabis company’s support tickets famously broke the team’s first approach. That’s a crucial lesson for regulated industries: policy nuance often lives in the edge cases. Lorikeet responded by making customer-configurable guardrails a first-class capability through the Coach interface.
Even more interesting, they’re flipping the configuration workflow so customers define "what good looks like" before they ever write a standard operating procedure. By anchoring configuration in outcomes and test cases rather than prose SOPs, teams move faster, reduce ambiguity, and get to measurable quality earlier.
The platform leans into eval-driven development: using AI to diagnose failure modes in traces and automatically suggest fixes. A "Trace Diagnosis Agent" surfaces root causes and remediation paths, shrinking the feedback loop from discovery to improvement.
Culturally, the product engineering cadence is customer-obsessed: every engineer asks weekly what they learned from a customer. That lightweight ritual is a forcing function for continuous discovery and keeps prioritization tethered to real-world tickets, not just internal hypotheses.
Here’s how I translate these lessons for any customer support AI strategy in regulated environments. First, ship with opinionated "AI humility" and measure handoffs as a quality feature, not a failure. Second, separate resolution from configuration via a dual-agent architecture so each can evolve independently. Third, integrate where your customers already work (Zendesk, Intercom) to accelerate time-to-value. Fourth, make guardrails domain-native and customer-configurable, and start with evals that define "what good looks like". Finally, invest in trace analysis and automatic fix suggestions to shorten the learning cycle.
If you’re scaling support in healthcare, financial services, or any high-stakes domain, these patterns are practical, defensible, and ready to operationalize. Build the Concierge to resolve, empower the Coach to continuously improve, and let "resolution in the loop" bind humans and agents into one reliable system of service.
AI in customer service is no longer experimental—it’s the standard. In my work leading product and customer experience teams, I’ve seen the shift firsthand, and the stakes have never been higher for getting the foundations right.
Fin’s 2026 Customer Service Transformation Report found that 82% of senior leaders say their teams invested in AI for customer service over the last 12 months, with 87% planning to invest in 2026. Those investments pay off with 24/7 availability, multilingual support, major time savings, and faster resolutions. But there’s an unsung hero behind every AI-first support experience: knowledge management.
A Service Agent is only as good as what we give it to work with. If we’re using an Agent, like Fin, to resolve customer queries end to end, it needs an extensive pool of knowledge to draw from. We have to feed it accurate answers on our product, features, policies, and troubleshooting. Without these, the Agent can’t do its job—and our team ends up handling repetitive queries that should be automated.
A Fin-branded quote pairs with a friendly black-and-white portrait to champion smarter support. It reminds readers that time spent building knowledge and processes today compounds into fewer tickets and smoother operations.
In this guide, I’ll walk you through two phases of the journey. Phase 1 is about building a high-quality knowledge base from scratch or overhauling what you have. Phase 2 is about maintaining, optimizing, and scaling that knowledge so your AI performance keeps compounding over time.
Definition: Knowledge management is the process of creating, organizing, sharing, and maintaining knowledge in your business.
Fin’s quote card blends a friendly headshot with a message to think outside the box and tap new information sources to power an AI knowledge base—ideal inspiration for service teams leveling up knowledge management.
Your help center is the obvious example, but it’s only the tip of the iceberg. Effective knowledge management also means creating resources like FAQs, troubleshooting guides, onboarding and best-practice docs, internal support guidance, and learning materials that cover everything from everyday how‑tos to complex billing and account questions.
It means identifying content gaps—missing troubleshooting steps, unclear policy explanations, outdated feature details, or unanswered edge cases—before your customers find them. It means implementing systems so both your Agent and your support reps can access the right information at the right time. And it means developing processes so your content stays in lockstep with product updates, policy changes, and bug fixes.
From Fin's guide to knowledge management, this monochrome quote card urges teams to test their first deployment themselves so agents feel the same journey customers do, turning insights into faster, higher-quality support.
Your knowledge base now fuels your entire support experience, not just self-serve. It’s the key to accurately answering complex questions, reducing handle time, and delighting customers across channels.
Here’s the blunt truth I share with every team: your Agent is only as strong as what you feed it. A lack of information, messy structure, or stale documentation will tank accuracy and trust. No large language model (LLM) knows your business like you do. It doesn’t understand your customers’ needs, pain points, and use cases. That knowledge is unique to you and your organization, meaning you need to be the one to map it all out and make it available to your Agent.
Equip service agents with a clear playbook for damaged delivery reports. This procedure page outlines when to use the guide, how to verify evidence, and the next action to reorder—ready to test, save, and set live.
Every investment in knowledge also has compounding results. Think of it as a flywheel: when you improve your knowledge base, your Agent solves more cases and generates better data. That data shows you what to add, update, or refine next. The sooner you plant the seeds, the sooner you’ll harvest the returns.
Consider a simple calculation. If it takes 30 minutes to write a troubleshooting article for a common issue, that half hour often saves hours for your support reps, who no longer need to handle that query. You can estimate impact by multiplying the average time to compose a response by the frequency of the query. For customers, multiply the number of customers who ask this question by their average time to resolution to quantify time saved. Then monitor Agent involvement rate, resolution rate, and automation rate to see the compounding effect.
Give every seller instant, trusted answers with an AI-powered knowledge base that unifies docs, FAQs, and playbooks into a single source of truth—accelerating ramp, boosting call confidence, and improving every customer conversation.
Phase 1: Building your knowledge base is about getting your content durable and AI-ready. I start by prioritizing what to include, where to source it, and how to audit and triage before go‑live.
Data-driven tools can surface the right starting points. For example, platforms like Fin can surface knowledge gaps from real customer conversations where help content is missing, unclear, duplicated, or contradictory. A centralized knowledge hub then becomes your single source of truth for both customer-facing and internal content, with audience controls to ensure your Agent only uses the right materials for the right users.
AI elevates service when teams treat deployment as a learning loop. This Fin-branded quote visual introduces our ultimate guide to knowledge management for service agents—iterate from day one to improve customer outcomes and teammate efficiency.
Here’s how I prioritize content for the first wave. Support FAQs come first—billing changes, account updates, feature usage, troubleshooting, and policy questions. I mine the inbox and historical conversations to find the highest-frequency issues and turn them into crisp help articles the Agent can quote.
Next, I build onboarding and setup guides so new customers reach value fast. I collaborate with customer success and product to document the fastest path to “first win,” and I ensure the Agent can reference those steps in chat and in‑product guidance.
Keep your help content fresh. A Fin quote urges support leaders to audit and update their knowledge base so AI assistants and service agents surface accurate answers that genuinely add value.
Then I add troubleshooting and advanced guides for deeper issues and power-user workflows. I pull in product managers, engineering, and success managers to capture deeper diagnostics, known limitations, and recommended workarounds—exactly the details that prevent escalations.
Finally, I create content for specific use cases and customer segments. Different goals and configurations require contextual guidance, so I reflect language customers actually use and tailor examples to their jobs-to-be-done.
Smarter support starts with better knowledge. A testimonial highlights how Fin learns from website and help center content, showing that robust knowledge bases train AI agents, raise accuracy, and yield compounding gains.
When sourcing knowledge, I cast a wide net and consolidate it so the Agent and my team can use it reliably. That includes public help articles and troubleshooting guides; internal runbooks, escalation steps, and policy clarifications; curated snippets for short replies and exceptions; past conversations that expose gaps; relevant website pages; and documents like PDFs and DOCX with selectable text.
Before anything goes live, I run a structured content audit. The goal is twofold: prevent the Agent from learning from outdated information, and expose gaps that will cause escalations. I divide content by product area, assign clear ownership, and set a time‑boxed review window to update, consolidate, or retire content. Shared ownership turns a daunting clean‑up into a manageable sprint.
Why can’t knowledge content be an afterthought? This Fin visual pairs a grayscale portrait with a bold message: great Service Agents rely on a strong, current knowledge base to deliver accurate, evolving support. Explore the guide.
I also walk the customer journey myself—exactly as a new user would—so I can experience the Agent’s responses firsthand and spot missing topics or keywords. Where my platform supports it, I use preview and batch testing to validate coverage across common questions, then simulate more complex workflows to ensure handoffs and steps are properly defined before launch.
After 30 days of Agent activity, I dive into the data. I look for topics driving handoffs to humans, articles correlated with low resolution rates or CSAT, and content that customers view but still escalate. Those signals tell me exactly what to write or refine next—and where to tighten conversation design or retrieval.
Centralize your conversations, customer data, and knowledge in one place to sharpen context and speed resolutions. This Fin graphic pairs a monochrome portrait with a bold pull-quote highlighting unified platforms for better support.
Prioritization is where impact accelerates. I focus first on the content my team shares most: top help articles, troubleshooting steps, onboarding flows, and policies. I study conversation analytics to identify the most common questions, the longest handle times, and the lowest CX scores, then close those gaps with targeted content. I also review high‑view articles that haven’t been updated recently and refresh anything affected by changes to product, policies, or plans.
Resourcing matters. Building a high-performing Service Agent shouldn’t be a side gig. I explicitly allocate weekly time for frontline reps, support specialists, and product partners to work on content requests and knowledge improvements. A 5–10 hour per‑person cadence is a practical baseline, and it doubles as a powerful way to upskill the team for emerging AI roles.
Jumpstart smarter support with the #1 Agent—organize knowledge, speed answers, and automate routine work. Click Start a free trial to see how AI elevates your service team and delivers faster resolutions.
Writing for AI is writing for customers. I train the Agent to mirror the terms our customers use by analyzing search queries and real conversation language. I avoid internal jargon, expand acronyms, and clarify key concepts to eliminate ambiguity. When a topic invites yes/no answers, I restate the question and add the necessary context so the Agent doesn’t misinterpret shorthand. I always pair images or videos with clear explanatory text so the guidance is accessible and machine‑readable. And I structure content for scanning with crisp headings and short sections, avoiding hidden information that requires clicks to reveal.
When I have bite‑size answers—common edge cases, policy clarifications, repetitive high‑volume queries—I collect them into focused internal snippets or compact FAQs so the Agent can retrieve and deliver precise answers quickly.
Phase 2: Knowledge management is where the compounding value kicks in. Once live, I track the metrics that matter: resolution rate (conversations fully resolved by the Agent when it was involved), automation rate (total conversations handled by the Agent across overall volume), time saved (hours of manual work offloaded), Customer Experience (CX) Score comparisons across AI and human conversations, and CSAT parity.
Then I put those learnings to work. Inevitably, some problems won’t be solvable on day one. That’s a gift—it shows me where to refine workflows, add clarifying steps, and strengthen knowledge depth. The richest insights often come from where the Agent struggles or escalates; those friction points become my highest‑ROI content tickets.
Knowledge management is never one‑and‑done. As products, customers, and business goals evolve, so must the knowledge. I formalize an ongoing maintenance cadence with clear ownership, review intervals, and time blocks on the calendar. Wherever possible, I use AI‑assisted drafting to propose updates, summarize gaps, and accelerate review without sacrificing quality.
To sustain momentum, I create a simple intake for content requests—often a lightweight ticket workflow inside our support tools—so anyone in support, success, sales, marketing, engineering, or product can flag gaps and propose improvements. The teams closest to customers usually spot the patterns first; a good intake system ensures we don’t lose those insights.
I also bake knowledge work into every launch plan. New features, product updates, plans, and policies require Agent‑ready content at launch, not after. I partner with product, support, and product marketing to produce best practices and anticipated FAQs in advance, then I review early conversations post‑launch to spot recurring confusion and fast‑follow content needs.
Brand consistency builds trust across every touchpoint. I standardize terminology for products, features, plans, and policies so the Agent, the help center, and human reps all speak the same language. I proof for tone, spelling, and grammar, and I use templates so content feels cohesive. I also include clear contact options for customers who need them—what channel to use, when to use it, and what to expect—so we maintain confidence even when escalation is required.
Clarity about audience matters, too. If certain content applies only to specific roles, plans, or regions, I label it explicitly and, where my platform supports it, target content so the Agent uses the right guidance for the right segment.
Finally, I connect the dots. When conversations, customer data, and knowledge live in one place, every interaction becomes an insight loop. A connected Agent turns support into a retrieval-first pipeline, making it far easier to diagnose issues, improve accuracy, and continuously raise the bar on customer experience.
Behind every high-performing Agent is a rigorous, AI-friendly knowledge management practice. Treating knowledge as a core service function—not a project—creates systems that improve with every conversation. That’s how we transform support from a cost center into a compounding engine for customer satisfaction, operational efficiency, and growth.
When teams evaluate AI Agent options for customer service, I often see the rigor aimed at the wrong subset of criteria. After leading and observing dozens of proof of concept (POC) efforts with our customers and prospects, I understand why performance—accuracy scores, resolution rates, and benchmark tests on curated datasets—soaks up most of the attention. But those indicators alone won’t guarantee success once you leave the sandbox and face real customers.
If your POC only proves that the AI “works,” you’re missing the bigger picture. Here’s what else I look for to make the best long-term decision.
How does it handle your real-world setup?
Performance is table stakes, but it has to reflect the messiness of an actual support environment. The best-performing Agents don’t just get answers right—they exhibit resilient, human-like behavior under pressure. I watch how the Agent behaves when it doesn’t know an answer: does it recover or spiral? Does it stay on track through multi-step requests, and how gracefully does it hand off to human agents? If your knowledge base depends on a retrieval-first pipeline, test cross-source retrieval and grounding—not just single-document lookups.
When I build evaluation scenarios, I put the Agent through its paces with a broad, realistic mix:
Multi-turn queries that require the Agent to carry context across a conversation, not just answer isolated questions.
Vague or fragmented inputs, like typos, grammatical errors, and incomplete questions, because that’s how customers actually write.
Edge cases and sensitive scenarios, like billing disputes, frustrated customers, and questions that sit at the boundary of what the Agent is trained on.
Different phrasings of the same question. An Agent that handles one version well but fails on a rephrasing has a knowledge problem, not a performance problem.
Queries that require pulling from multiple knowledge sources. Real issues are rarely answered by a single help article, and an Agent that can only handle single-source questions will hit a ceiling fast.
Multilingual conversations, if your customer base requires it. Performance can vary significantly across languages and it’s better to discover that in testing than in production.
This preparation is worth the effort. Any Agent can look impressive in a demo; what matters is how it holds up as part of your team, serving your customers in production.
What does it feel like to interact with the Agent?
Two AI Agents can post the same quantitative scores—resolution rates, containment rate, and more—and still deliver very different customer experiences. Resolution rate tells me whether the Agent finishes conversations; it says nothing about how customers felt during them. I deliberately assess the experience, not just the outcome, because conversation design shapes trust and brand perception.
Here’s what I look for to ensure the AI Agent is enjoyable to interact with:
Is the tone natural and on-brand, or does it feel robotic and generic?
Does it build trust early in the conversation, or does it create friction that makes customers want to immediately request a human?
When it doesn’t know the answer, does it handle that gracefully?
When it hands off to a human, is that transition seamless, or does the customer feel abandoned?
As George Dilthey at Clay put it when evaluating their AI setup: “Keep what’s important to your business up front and center. For us, that was transparency and control over the customer experience.”
That framing is exactly right. The Agent represents your brand in every conversation. Customers don’t experience “accuracy,” they experience conversations. An Agent that’s technically accurate but tonally off-brand will erode customer trust over time.
I make the experience dimension explicit in my POCs. I have people on my team—and when possible, a small cohort of real customers—interact with the Agent under realistic conditions. Then I ask how it felt, not just whether it worked.
Can you keep improving it after launch?
This is the dimension most teams don’t evaluate at all, and it’s possibly the most important one. Choosing an Agent that works today and ensures you can continuously improve the customer experience over time requires more than a functional demo. You’re buying a system that must get better every week, not just during the first sprint.
The feedback loop
Can your team easily review conversations and identify where the Agent is underperforming? Can you pinpoint specific gaps (missing knowledge, incorrect tone, poor handoff decisions) and act on them quickly? The faster the loop between “something isn’t working” and “we’ve fixed it,” the more value compounds over time. In practice, that means instrumenting conversations, leveraging Agent Analytics, tagging misroutes and tone slips, and running targeted evals on known failure modes.
The speed of iteration
When you identify a gap, how quickly can you address it? This is partly a question of tooling (how easy is it to update knowledge, refine guidance, adjust behavior?) and partly a question of team capability. The teams getting the most out of AI are the ones that have changed how they operate and made continuous improvement a part of their everyday work. They’ve committed to going all-in for the long term, not just the first few weeks when launching their AI Agent. We treat this as eval-driven development: automate evaluations that mirror real tickets, tighten prompt engineering and retrieval settings, and ship small fixes daily.
The vendor partnership
The vendor behind the Agent matters just as much as the solution itself. You’re choosing a partner for transformation that will help you evolve how your business delivers customer experience. Ask:
How does customer feedback influence the product roadmap, and can they show you examples?
If you have feedback on limitations or weaknesses, do they engage transparently or get defensive?
What kind of support will you get post-launch?
Are they shaping where AI customer experience is going, or reacting to what others are building?
How a vendor responds to those questions tells you more about the long-term relationship than any benchmark result.
What a good POC proves
If your POC only proves “the AI works,” you haven’t done enough. A strong proof of concept tests performance in realistic conditions, evaluates the experience from the customer’s perspective, and validates the system that will support continuous improvement after launch. Done well, it sets you up for long-term operational success and builds organizational AI readiness—not just a flashy demo.
Default prompts are quietly sabotaging agent retention. I learned this the hard way while reviewing early funnels for our voice and chat agents—engagement looked great at the greeting, but the moment the agent stopped after a single reply, the conversation flatlined. The fix wasn’t a fancy LLM trick; it was a disciplined second message and a rigorous audit of defaults across every entry point.
When an AI agent opens with a generic, low-friction greeting and then waits, users hesitate. Cognitive load rises, intent stays fuzzy, and drop-off follows. A thoughtful second message—delivered quickly, with clarity and options—reduces ambiguity and gives people a low-effort path to progress. It’s a small behavioral nudge that pays off in outsized retention gains.
Here’s the pattern that consistently works for me. First, keep the initial default prompt short, confident, and specific to the channel and task domain. Then ship a fast follow-up if the user hesitates for a few seconds. That second message should clarify what the agent can do, present 2–3 concrete choices, and invite free-form input. I’ve repeatedly seen this simple sequence unlock a 2–3x retention lift in early sessions, especially for first-time users.
Auditing default prompts is where the leverage lives. I inventory every ingress—web widget, IVR, SMS, in-app, help center—and catalogue the exact default system, developer, and user-facing prompts. Then I inspect turn-1 and turn-2 transcripts in Agent Analytics to quantify where users stall: time-to-first-intent, clarification rate, option selection rate, and completion. This makes the drop-off visible and turns “vibes” into data we can A/B test.
Designing the second message is a conversation design exercise, not a copy tweak. My recipe: empathize with the user’s likely uncertainty, constrain scope so the agent appears capable, and apply choice architecture. For voice AI agents, I keep it shorter, use confirmation questions, and bias toward read-back for accuracy. For chat, I include tappable options and examples that mirror top intents. The goal is momentum without feeling pushy.
Operationally, I run controlled A/B tests on default and second-message variants, sized to a realistic minimum detectable effect. I segment by source (ad, organic, support), device, and use case, because the winning prompt for sales qualification rarely matches the one for customer support. With proper instrumentation in our analytics stack, we track retention curves over the first 3–5 sessions, not just single-session reply rates, to avoid optimizing for chatter over outcomes.
Strong prompt engineering underpins the experience. I keep system prompts stable and explicit about persona, tone, and refusal behavior; manage the context window so examples don’t drown live intent; and use a retrieval-first pipeline when domain knowledge matters. The most expensive mistake I see is shipping defaults like “How can I help you?” without guardrails or examples—great for demos, bad for real users.
If you’re starting fresh, begin with a prompt audit this week: list all defaults, map them to top intents, and pair each with a channel-appropriate second message. Instrument the funnel, launch two variants, and set a crisp success metric (e.g., turn-2 continuation rate to task start, then task completion). This is one of those rare changes that is simple to ship and compounds across onboarding, activation, and long-term retention.
The takeaway is straightforward: don’t let your best work stall after the first reply. A disciplined second message and a focused default prompt audit will lift engagement, reduce ambiguity, and create the kind of early momentum that sustains retention over time.
Inspired by this post on Amplitude – Perspectives.
Customer experience is now a core product strategy lever, not a downstream support function. In my work leading product teams, I’ve seen that the fastest path to durable growth is aligning CX strategy with product, data, and go-to-market—especially when we’re building AI-powered solutions that must scale responsibly.
Amanda Sime is the Customer Experience Strategy Lead at Amplitude. She shapes CX strategy and partners across orgs to design and scale AI-powered solutions.
That mandate captures what high-performing organizations are doing well: connecting behavioral analytics, product discovery, and customer success into a unified operating system. When CX leaders partner tightly with product and data teams, we turn insights into action—using Amplitude analytics to identify friction, journey mapping to prioritize moments that matter, and a unified analytics platform to close the loop from hypothesis to measurable outcomes.
Practically, the playbook looks like this in my teams: start with rigorous journey mapping and retention analysis to pinpoint where value realization lags; run targeted A/B testing to validate interventions; and deploy in-app guides and product tours to accelerate user activation. Layer in session replay and behavioral analytics to understand intent, then operationalize learnings into repeatable workflows that improve time-to-value and customer success. This is how we make product-led growth concrete rather than aspirational.
AI Strategy adds both leverage and responsibility. We design AI-powered experiences with privacy-by-design, clear value propositions, and eval-driven development so we can measure lift, not just ship features. Cross-functional partners—from support to solutions engineering—become critical here, ensuring we scale responsibly while improving the signal-to-noise ratio of feedback flowing back to product roadmapping.
The outcome I aim for is simple: faster cycles from insight to impact. With tight cross-org alignment, a shared metrics framework, and disciplined experimentation, we can transform CX from reactive problem-solving into a proactive growth engine. If your team is ready to operationalize this approach, start with one high-friction journey, build a sharp driver tree, and let data, not opinions, guide the next iteration.
Inspired by this post on Amplitude – Best Practices.
Today I’m introducing Operator, an Agent that works across both Fin and the Intercom helpdesk to help you manage your customer operations.
In practical terms, Operator manages help content, builds automation, does the ongoing work that determines how well Fin performs, and runs the operational work your human team doesn’t have time for. That combination is precisely what modern support teams need to move from reactive firefighting to proactive, consultative support.
Why does this matter? Running a customer operation means managing AI and humans simultaneously, and doing this well requires more capacity than most teams realistically have. I’ve felt that strain firsthand—competing priorities, constant context switching, and a never-ending queue that blurs strategic focus.
On the AI side, Fin’s performance is largely influenced by what surrounds it: the accuracy of your help content, the quality of your Fin configuration, and how well you understand what’s working and why. When product teams ship daily, keeping your help center current means finding every affected article before customers notice the gaps. When Fin gets a conversation wrong, diagnosing it requires reading through what happened, identifying the root cause at the configuration level, making the fix, and verifying it worked. Analyzing why your resolution rate dropped means pulling conversations, finding patterns, and tracing the cause back to something actionable. And beyond individual fixes, there’s the ongoing question of what to automate next – what your human reps are still handling repetitively, whether it’s worth building a Procedure for it, and how to test it before it goes live.
On the human side, the demands are just as continuous. When an incident hits, someone needs to identify every affected customer, draft the right response, and send it before the problem compounds. Team leads need visibility into rep performance across hundreds of conversations to coach effectively and prep for 1:1s. Reps need to know what to prioritize without spending the first part of their day figuring it out. In fast-moving environments, that operational overhead wastes energy you should be investing in better customer outcomes.
Meet Operator, the agent that explains your customer conversations. This Synthesia testimonial shows how simply asking Operator reveals what happened and makes refining Fin faster for support and enablement teams.
Too often, the work outpaces what teams can manage, so it happens reactively, or not at all. Operator was built to change that, giving teams a new way to understand, manage, and improve their customer operations. Here’s how I put Operator to work across AI workflows and human-led processes.
First, I use Operator to ask my data anything. Your support operation generates more useful data than most teams have time to process. Operator gives you direct access to it. You can ask it any question about what’s happening in your operation (why a metric changed, what’s driving escalations, how the team performed last week) and it returns structured answers with charts, breakdowns, and the ability to dig further. It analyzes samples of real conversations on the fly to surface patterns and identify root causes. If your head of product wants to know what customers are saying about a new release, you can ask Operator rather than spending half a day pulling a report together. It also works across your entire operation, analyzing Fin’s performance, your human reps’ performance, and customer sentiment.
Crucially, I don’t start from scratch every time. Give Operator ongoing work, like analyzing your automation rate every Monday, flagging anything that needs attention, and posting the report in your Fin workspace. It’ll run the analysis, write the report, and deliver it without you having to go looking for it. That’s the kind of agentic AI leverage that compounds week after week.
Second, I keep the knowledge base current without writing a single article. Your knowledge base is only as useful as it is accurate. When product teams ship fast, keeping pace with content updates is a substantial, ongoing job. Give Operator a brief about anything, from a new feature or policy change to release notes, and it finds every article in your help center that needs updating, drafts the edits in your tone of voice and style, identifies content gaps, and drafts new articles to fill them. It even handles localized versions. Every change is formatted as a proposal (Operator’s version of a pull request) for you to review, edit, and approve before anything goes live. It feels like adding several knowledge managers to the team overnight, without the ramp time.
See why teams choose Fin Operator for customer operations: accurate analysis, trend insights, and conversation debugging—going beyond basic LLM connectors. A Raylo testimonial spotlights daily, real-world impact.
Third, I build, test, and ship improvements to Fin directly through Operator. When Fin gets a conversation wrong because of a content gap or misconfigured rule, Operator can debug it by reading through the conversation, identifying what caused the problem, proposing a fix, and running simulation tests to verify it before you approve. You see what changed and why before anything goes live. Beyond debugging, Operator has deep knowledge of every Fin feature and capability, so you can ask it directly to help you configure whatever you need. If you need a Procedure for a specific query type, describe the outcome you want and Operator builds it, including triggers, multi-step instructions, edge case handling, and a simulation test, all from a single prompt. The same applies to configuring Guidance rules, data connectors, monitors, and workflows. You don’t need to know which feature solves your problem or how to configure it; you just describe what you want.
For teams looking to increase their overall automation rate, Operator can handle that strategically too. Ask it to analyze where your biggest automation opportunities are and it surfaces them by volume, along with an estimate of the weekly team time each one is consuming. Pick one, and it builds the solution for you to approve. That’s consultative support, productized.
Finally, I use Operator to effortlessly manage the human side of support. When an incident hits, Operator identifies every affected conversation, drafts targeted responses, and sends them proactively, turning what would normally be hours of reactive triage into minutes of review and approval. For ongoing management, a team lead prepping for 1:1s can ask Operator to pull each rep’s metrics, flag outliers, and surface what’s worth digging into. A rep coming back from a meeting can ask what to focus on next and get a prioritized queue based on urgency, customer value, and wait time. And because Operator sees patterns across everything your human team is handling, it can surface the conversations they’re still resolving manually, flagging your next automation opportunity before you’ve had time to go looking for it.
Here’s why this works. Operator isn’t a general-purpose AI model given access to your data. It’s built on a library of purpose-built tools that encode expertise specific to support operations, like how to pick the right attributes for a given analysis, search a knowledge base semantically, debug Fin’s reasoning in a specific conversation, or write and test a Procedure that will actually work. That specialized toolkit is what makes its recommendations trustworthy and its execution reliable.
Elevate customer service with Operator. The bold headline and vivid knot logo introduce a modern AI platform that streamlines workflows, speeds resolutions, and scales support operations without extra headcount.
The proposal (pull request) system makes this possible. When Operator updates content, adjusts configuration, or modifies how Fin behaves, it creates a proposal – a structured diff of what’s changing and why. You review it, edit if needed, and approve before it takes effect. Operator does the cognitive work; the human stays in control of what goes live.
More than 200 early users are already trying Operator, and every one of them is finding new use cases. It’s a genuine step change in capability, and I expect it will change the way support teams run their operation. We’re working towards a vision of Operator being increasingly agentic, expanding across every new role Fin takes on.
Operator is available in early access now. If you’re ready to transform your customer operations across Fin and the Intercom helpdesk with agentic AI, start here: https://fin.ai/operator.
Sometimes a corporate rename lands with such obvious inevitability—and such lateness—that it feels like a quiet confession. As a product leader, I’ve wrestled with that timing question: move early and risk confusion, or wait and risk stagnation. In this case, the industry finally received the clarity it has been circling for years.
The announcement was clear: “we’re changing the name of our company to Fin.” Crucially, the name Intercom will continue as the customer service software platform that many of the best brands rely on as their primary help desk. The team also “just launched a complete rebuild, Intercom 2,” and is doubling down investment in that product. In other words, the company brand now matches its leading customer agent platform—Fin—while Intercom remains the flagship product line.
From a product strategy and brand architecture perspective, this move aligns the corporate identity with the growth engine. I’ve seen too many winners of a prior era cling to yesterday’s positioning while markets shift under their feet. The phrase that keeps echoing in my mind—because it’s true in practice—is that “the only path to success in the future is through destroying your past.” Culture, pricing models, product lineup, investment priorities—those can evolve. But until the company name evolves, the market’s mental model often does not.
It’s telling that three years ago, when the team effectively created the service agent category, they led with Fin and kept Intercom in the background. That wasn’t indecision—it was smart category design. Humans don’t frequently remap old concepts; we add new ones. We don’t wake up reinterpreting what a chair is, but we do invest energy to understand a new kind of drone or an intelligent software agent. New categories deserve new names, or they’ll be dragged back into old expectations.
This is where product positioning meets competitive differentiation. Newcomers without legacy baggage enjoy a clean slate; they never have to convince the market they’ve changed because they never had an old position to defend. Even with provably superior technology, an incumbent can find itself explaining rather than advancing. I’ve led naming and repositioning work where the hardest task wasn’t shipping new capabilities—it was unseating the entrenched narrative in customers’ heads.
So, “baggage be gone.” Fin is clearly positioned as the future of the customer agent category and is poised to become the largest part of the business. Intercom, as a product brand, very much lives on—and with “Intercom 2” now in the world, the product roadmap and investment thesis are unambiguous. The core takeaway for product management leadership: align corporate naming with your category-creating bet, then let go. That’s how you turn momentum into market leadership.
For leaders working through similar decisions, here’s the lesson I’m taking to my own teams: rebrands aren’t about logos, they’re about narrative clarity and execution velocity. When the corporate name and the breakout product share the same story, go-to-market motions get sharper, customer understanding improves, and AI strategy integrates more naturally into customer support workflows. Naming follows strategy—not the other way around.
By the end of 2024, we were already all-in on Fin, and our customer support organization was deep in its own transformation. Resolution rates were strong, efficiency was improving, and for the first time, something new was emerging: capacity.
That newfound capacity wasn’t just a relief; it was a strategic opening. As we became less reactive day to day, I saw how support’s unique vantage point—rooted in customer needs and aligned with company goals—could evolve into a consultative function that actively drives value for customers and the business.
This is the story of how we built consultative support. I’ll walk you through how we got started, the results we achieved, and the lessons I’d carry forward if I were doing it again from scratch.
We didn’t begin from zero. A few years earlier, we partnered closely with research and data science to drive product adoption. In a project we called “next best step,” we tested offering proactive guidance inside already-established conversations. It worked well, and as Fin accelerated how we worked, we realized we were ready to push into broader, more ambitious opportunities.
Instead of dictating a solution from the top, I opened the floor. We hosted a support town hall and asked the team to share concrete ways support could directly drive company outcomes. The conversation was electric—practical, creative, and grounded in real customer moments.
Right there, we spun up campaign concepts. One idea was an always-on in-product banner offering a call with a member of our team to help customers set Fin up to the best of its ability. Another was the “Fin upsell campaign,” where, once a customer had a positive interaction with Fin and clicked the “that helped” button, a tailored message would share details about our own success with Fin and invite the customer to book a call to learn more and ask questions.
The energy from that session made one thing obvious: the team already knew how to help customers extract more value from the product. They just needed focus, permission, and a clear path to act.
We started small on purpose. I recruited a group of volunteers who dedicated part of their week to exploring new, proactive ways to support customers. We kept the group tight for two reasons: first, even with Fin freeing up significant capacity, we still had to deliver excellent day-to-day support; second, this was an experiment, and we weren’t going to overhaul a 100+ person organization without proof.
One of our first campaigns focused on proactive engagement with self-serve customers—those without a dedicated sales or success touchpoint. Our goal was to give this group direct access to teammates with first-hand experience in AI transformation and help them see the value they could get from Fin.
Early use cases included guiding customers through Fin trials, working with mature customers on optimization to get more out of Fin, and proactively identifying high-potential accounts that looked ready for Fin. None of this required a new team or a big budget—just attention and intention.
To make consultative support stick, we trained for a mindset shift. I encouraged the team to move beyond solving the immediate issue and instead probe deeper to understand each customer’s unique context. We leaned on our sales and success peers to refine our outreach—learning how to time our messages, frame value succinctly, and meet customers at the right moment rather than waiting for them to come to us.
To validate our approach, we needed data—not vibes. We built a simple but rigorous comparison: accounts we engaged with versus accounts we reached out to but didn’t hear back from. Over a six month period, we tracked feature adoption, Fin usage, and expansion revenue across both groups.
The result was clear: engaged accounts grew roughly twice as fast in both usage and expansion.
To further prove the value of proactive support, we also tracked direct Fin resolutions generated after consultative interactions, resolution and automation rate improvements across engaged accounts, and influenced expansion ARR across everything we worked on over the year.
Seeing those numbers was a turning point. This wasn’t a side project anymore—it was a repeatable motion with measurable business impact.
As results became visible, partnerships multiplied. Self-serve engineering teams saw the value of well-timed human touchpoints. Customer lifecycle marketing tapped us to handle responses to their campaigns. Product teams began partnering with us to identify high-impact engagement opportunities. We also deepened our collaboration with digital, scale, and high-touch success teams—stepping in where they lacked capacity and offering deep technical guidance to help customers get the best from the platform.
What began as simple outreach matured into targeted, strategic initiatives tied directly to company goals.
Within a year, our volunteer crew grew to ~16 teammates across regions—curious, motivated, and eager to try new things. We continued expanding the consultative support function and took on new projects end to end. Most recently, we assumed ownership of the new “sales assist” team to drive self-serve trial conversions and help new customers get the most from their first experience.
Here are the practices that mattered most in making consultative support real and durable:
Start with your team, not a strategy doc. The best ideas came from the people closest to customers. That town hall shaped our initial direction more than any top-down plan could have.
Don’t scale before you’ve proved it. A small, motivated group moved faster, learned quicker, and produced clearer results than a broad rollout. When you need organizational buy-in, a rigorous proof point beats a promising concept.
Train for a different mindset. Consultative work requires curiosity, commercial awareness, and the ability to hold broader context—not just product knowledge. Invest deliberately in coaching and frameworks that strengthen these muscles.
Measure against a control group. Without a control, you have a story. With it, you have a business case—and that’s what unlocks resources, headcount, and prioritization.
Lean into being different. It’s helpful to take cues from sales and success, but you don’t have to operate exactly like them. There’s real power in support’s distinct perspective and tone.
Building this consultative support function fundamentally changed how we think about our remit. Support is no longer just there to respond; it now drives adoption, influences retention, generates expansion revenue, and, for many self-serve customers, serves as the primary human touchpoint.
In an AI-first world, where Fin handles all of the transactional work, this kind of work becomes even more important. Because the question for support leaders is no longer “how do we handle more tickets?” but rather, “how do we use support to grow the business?”
In my work with product, operations, and support leaders, I’m often asked to help make sense of Agent Analytics—what to track, how to attribute outcomes, and where to invest. After reviewing countless dashboards and running experiments across human agents and AI agents, I’ve learned that some of the most common measurement beliefs are precisely the ones that lead teams astray.
What comes up in conversation with leaders about Agent Analytics, and why not everything is what it seems.
Below, I unpack four pervasive myths I encounter and share the data-centered practices I use to replace them. My goal is simple: help you upgrade the way you measure performance so you can improve customer outcomes, accelerate learning, and scale impact with confidence.
Myth 1: “Lower average handle time (AHT) means higher performance.” AHT is useful but incomplete. When teams optimize solely for speed, they often push complexity into repeat contacts, reopens, or escalations. In the data, that shows up as a weak or negative relationship between lower AHT and durable outcomes like first contact resolution (FCR), customer effort, or revenue per conversation.
Reality and what I measure instead: I right-size speed by pairing AHT with intent-level resolution and recontact rate. For simple intents (password reset, billing address update), shorter is usually better. For complex intents (tiered troubleshooting, multi-step verification), “right-speeding” wins—slightly longer interactions that prevent rework. Practically, that means segmenting by intent complexity using behavioral analytics, tracking weighted “intent resolution rate,” and monitoring repeat-contact windows (24–168 hours) to catch downstream pain.
Myth 2: “AI agent containment tells the whole story.” A high containment rate can mask failure modes such as unresolved intent, silent abandonment, or low-quality handoffs that frustrate customers and spike human workload later.
Reality and what I measure instead: I break containment into three parts for voice and chat flows: (1) intent resolution without escalation, (2) graceful handoff quality when escalation is necessary, and (3) post-handoff efficiency and satisfaction. For voice AI agent experiences, I also track escalation clarity (did the transcript summarize history and intent?), time-to-human, and customer satisfaction on the combined interaction. This provides a fuller view of customer support ai strategy effectiveness and avoids over-crediting automation for partial wins.
Myth 3: “Quality is subjective, so it can’t be measured at scale.” Teams often default to sporadic QA because they assume it can’t be standardized across channels or agent types. The result is noisy feedback loops and stalled coaching.
Reality and what I measure instead: Quality becomes measurable when it’s grounded in observable behaviors linked to outcomes. I use a rubric anchored in behavioral analytics (e.g., verified customer need, correct resolution path, policy compliance, empathy markers) and validate it via correlation with FCR, recontact, and retention analysis. To scale, I combine calibrated human reviews with AI-assisted scoring, check inter-rater reliability weekly, and use driver trees to connect quality levers to business results. This creates a consistent, coachable signal for both human agents and AI flows.
Myth 4: “If the dashboard is green after launch, we’ve won.” Early wins can reflect novelty effects, cherry-picked routing, or short-term incentives that don’t persist. Declaring victory too soon locks in fragile gains and hides regressions across cohorts.
Reality and what I measure instead: I treat go-live as the start of learning. I use A/B testing with a clear minimum detectable effect (MDE), stagger ramps, and hold out stable control cohorts for at least one full demand cycle. I track outcomes vs output OKRs—focusing on intent resolution, customer effort, and revenue/customer health over vanity metrics. I also monitor seasonality and channel mix shifts inside a unified analytics platform to ensure improvements generalize beyond the first week.
How I operationalize this day to day: (1) define intents and complexity upfront, (2) unify journey data across channels, (3) instrument resolution and recontact rigorously, (4) apply driver trees to isolate what actually moves outcomes, and (5) iterate via disciplined experiments rather than sweeping changes. This approach aligns product and operations, speeds up coaching, and ensures AI investments compound rather than decay.
If you’re rethinking your Agent Analytics stack, start by replacing each myth with a sharper metric: pair AHT with intent-level resolution, pair containment with handoff quality and satisfaction, pair QA with outcome-linked rubrics, and pair green dashboards with robust experiments. The payoff is a measurement system that earns trust, guides better decisions, and consistently improves customer and business results.
Today, I’m thrilled to share Fin’s next leap as a Customer Agent: ecommerce. When we launched Fin for Sales, Fin expanded further across the customer journey — and now we’re bringing that same intelligence to product discovery, checkout conversion, and post‑purchase support for Shopify merchants.
Fin for Ecommerce is a new role purpose-built for Shopify merchants that combines shopping assistance and ecommerce support. Fin is already the best Agent for customer service, resolving over a million queries a week for 8,000+ businesses. Now, it also guides shoppers to the right product, addresses concerns in the moment, and converts browsing into buying — all in one fluid experience.
Here’s what’s new and why it matters for conversion rate, average order value (AOV), and lifetime value:
A leading mattress retailer shares how Fin for Ecommerce acts like an expert associate—asking about sleep style and firmness, then recommending the best-fit product to boost confidence and drive conversions.
Fin helps shoppers find the right product. It asks thoughtful questions, narrows options across large catalogs, and compares products based on what the shopper actually needs — like a great in‑store assistant, at scale.
Fin helps increase order value. It recommends relevant add‑ons and higher‑value alternatives based on conversation context, keeps carts effortless to update, and guides shoppers smoothly into checkout when they’re ready.
See Fin for Ecommerce in action: a Product Discovery card curates three high-performance ski jackets with images, names, and prices, revealing how the customer agent guides shoppers and accelerates confident purchases.
Fin handles support without losing the sale. Returns, refunds, and order changes happen in the same conversation; once resolved, Fin brings shoppers right back to browsing so momentum isn’t lost.
Fin is integrated with Shopify. Connect your store and Fin syncs your catalog, order data, and APIs in minutes — no manual training or complex setup.
A customer spotlight from Ninja Transfers shows Fin for Ecommerce boosting sales: 10% of support chats convert, with order values 20% above average—proof that an AI customer agent can drive revenue while improving service.
In a great retail store, an attentive associate changes everything: they ask what you’re looking for, understand your preferences, answer the questions that matter, and walk you to checkout — and when you return, they remember you. That level of proactive, human‑quality assistance has never truly made it online.
Most ecommerce still looks like it did a decade ago: filters, FAQs, and self‑serve flows that assume the customer already knows what they want. Ecommerce offers scale and 24/7 convenience, but it’s passive — it can’t understand a shopper’s intent and actively guide them to a product that fits.
Fin for Ecommerce acts like a customer agent—checking shipping status, surfacing in‑stock color variants, and updating the order in the same thread—turning a jacket mix‑up into a quick, seamless experience.
Fin for Ecommerce changes that by bringing high‑quality shopping assistance to Shopify stores.
"Fin doesn't just recommend products — it asks the right questions about sleep position and firmness preference, understands what the customer actually needs, and guides them to the right decision. It sells the way we sell." Anthony Navarro, Market Sales Manager at Avocado
An Avocado Green Mattress customer experience leader shares how Fin for Ecommerce unifies support and sales—answering policies, selling products, and explaining the mattress break-in period—so shoppers get instant, agent-level help.
Here’s how it works in practice. When a shopper says "I need a gift for my partner" or asks "what running shoes work for trail and road?," Fin doesn’t dump them on a search results page — it starts a conversation. It asks about preferences, incorporates live browsing context, surfaces the most relevant options, and compares them based on what the shopper cares about.
This is powered by Fin Apex 1.0, the best-performing model for customer service, combined with a retrieval engine purpose-built for ecommerce. It handles vague, exploratory shopping questions and large product catalogs, helping shoppers find the right fit, faster.
Seamlessly connect Fin to your Shopify store. With one click, sync your product catalog, pull live inventory, and import store policies so your customer agent can answer questions and resolve orders faster.
In practical terms, this is agentic AI meeting ecommerce: Fin plans, retrieves, and reasons through complex product questions and next best actions to move the shopper forward confidently.
Based on the conversation, Fin recommends complementary or higher-value options, keeps carts easy-to-update, and guides shoppers into checkout when they’re ready.
Customer testimonial from Groupsumi spotlights Fin for Ecommerce: rapid, high-quality support with minimal setup, powered by Shopify as the single source of truth, helping teams cut complexity and focus on growth.
"Fin for Ecommerce is already driving meaningful revenue, with 10% of conversations converting to orders averaging 20% above our store AOV." Matt Satell, Director of Ecommerce, Ninja Transfers
Fin for Ecommerce is built on the same AI platform that powers Fin for Service. Fin understands whether a conversation requires shopping assistance, support, or both, and moves between them seamlessly without the customer noticing.
Meet Fin for Ecommerce, your always‑on customer agent. This bold hero invites you to add Fin to your store so shoppers get instant answers, higher confidence at checkout, and fewer support tickets.
This means the same Agent that helps shoppers buy also handles the hard and complex post‑purchase work including refunds, exchanges, order changes, tracking, and shipping questions. It can make changes in real time, within the same conversation, using the same context and data.
"The handoff between support and sales is so smooth I can't tell the difference without checking the filters. Fin talks policy, sells products, and references our mattress break-in period all in one conversation. It handles both the way our best agents would — but without the customer waiting to be passed between people." Kurt Dwiggins, Customer Experience Manager at Avocado
Fin for Ecommerce is purpose-built for Shopify merchants. Connect your Shopify store and Fin establishes a live connection to your entire catalog – products, variants, content, and order data – ensuring every response reflects your latest inventory and shoppers only see what’s actually available.
You can add the Messenger to your store and set Fin live in minutes without any manual training or technical expertise. When connected to Shopify’s API, Fin can handle even your most complex customer requests like tracking orders, processing returns, and updating subscriptions via Procedures. Fin automatically drafts Procedures for common ecommerce support queries based on your Shopify account and customized to your company policies.
You review, adjust, and publish, allowing Fin to start handling real queries in minutes.
"What surprised us most about Fin for Ecommerce is how quickly it delivers high-quality support with minimal, non-technical setup. Using Shopify as the single source of truth reduces operational complexity and allows us to focus on core business execution." Arnau Jiménez, Chief Technology Officer, GroupSumi
Fin is now a Customer Agent, with multiple roles that work seamlessly across the customer lifecycle. When a single Agent can guide a shopper from "I need a gift for my partner" to checkout, and handle a return weeks later without losing context, that’s a fundamentally better customer experience. It’s one Agent that deeply understands your products and your customers, and supports them throughout their entire journey with your business.
Leading ecommerce brands, including Avocado, WHOOP, Shutterstock, Flaviar, Carvana, Nuuly, MPB, Pure Electric, and Goodbuy Gear, already trust Fin to create standout experiences for their shoppers. I’m excited to continue expanding Fin’s roles as a Customer Agent and share more soon.
Ready to see it in action? Visit fin.ai/ecommerce and add Fin to your Shopify store today.
