# Bagel AI > Bagel AI – The first AI-native Product Intelligence Platform Make every feature count Drive revenue, growth, and business impact by turning every piece of feedback and customer interaction into actions. --- ## Pages - [Service Agreement](https://bagel.ai/service-agreement/): This SERVICE AGREEMENT (the “Agreement”) is entered into by and between Bagel Technologies, Inc. , a Delaware corporation, together with... - [Privacy Commitment](https://bagel.ai/privacy-commitment/): We understand the importance of protecting your sensitive data and are committed to ensuring your peace of mind when integrating... - [Privacy Policy](https://bagel.ai/privacy-policy/): Introduction We at Bagel Technologies Inc. (“us“, “we” or “Company“) respect the privacy of our users (each, a “User” or... - [Terms of Use](https://bagel.ai/terms-of-use/): These Terms of Use, together with any other agreements or terms incorporated by reference, including the Privacy Policy available at:... --- ## Posts - [AI-Native Product Manager | Part 2: Your AI Workflow Starter Pack ](https://bagel.ai/blog/ai-native-product-manager-part-2-your-ai-workflow-starter-pack/): Key Takeaways Why This Isn’t About Prompts (and Why It Still Kind Of Is) This post isn’t about learning to... - [Fixing CS - Product Friction with AI: Insights from Bagel Talk](https://bagel.ai/blog/fixing-cs-product-friction-with-ai-insights-from-bagel-talk/): TL;DRAlignment between Product and Customer Success shouldn’t be an uphill battle. In this conversation, Bagel AI CEO Ohad Biron and... - [Stop Tagging Feedback Manually (Seriously, Stop)](https://bagel.ai/blog/stop-tagging-feedback-manually-seriously-stop/): Manual feedback tagging might seem like a necessary evil, and it is one of the most inefficient workflows plaguing product... - [The AI-Native Product Manager | Part 1: How to Think Like an AI-Native Product Manager](https://bagel.ai/blog/the-ai-native-product-manager-part-1-how-to-think-like-an-ai-native-product-manager/): Key Takeaways (TLDR) What This Series Is About Welcome to “The AI-Native Product Manager,” a series for product managers who... - [Product Feedback Trends You’re Missing (Until It’s Too Late)](https://bagel.ai/blog/product-feedback-trends-youre-missing-until-its-too-late/): Staying ahead of product feedback trends isn’t just about staying competitive. It’s about building the right features at the right... - [Productboard vs. Bagel AI: What Product Teams Actually Need From AI Product Tools](https://bagel.ai/blog/productboard-vs-bagel-ai-what-product-teams-actually-need-from-ai-product-tools/): AI has officially entered the product stack. But just because a tool uses AI doesn’t mean it changes how product... - [The Real Reason Your Product–GTM Alignment Is Failing](https://bagel.ai/blog/the-real-reason-your-product-gtm-alignment-is-failing/): Let’s break down the real causes behind this issue and how platforms like Bagel’s AI Product Intelligence help companies fix... - [Enterpret vs Bagel AI: What Product Teams Actually Need from AI](https://bagel.ai/blog/enterpret-vs-bagel-ai-what-product-teams-actually-need-from-ai/): The AI Gap That Matters Most AI is flooding the product stack. But not every tool labeled “AI-powered” actually moves... - [The Problem with Product Feedback? It's Buried in 6 Tools](https://bagel.ai/blog/the-problem-with-product-feedback-its-buried-in-6-tools/): Awareness of the Chaos Product teams today are inundated with feedback from multiple sources: surveys, social media, customer support tickets,... - [The Hidden Cost of Cutting PMs: Why AI Can’t Replace Product Context](https://bagel.ai/blog/the-hidden-cost-of-cutting-pms-why-ai-cant-replace-product-context/): I posted on LinkedIn recently about a clip from ElevenLabs where Luke Harries says they don’t have product managers. Engineers... - [Sentiment vs. Revenue Metrics: Five Numbers Product Teams Should Track Instead of NPS](https://bagel.ai/blog/sentiment-vs-revenue-metrics-five-numbers-product-teams-should-track-instead-of-nps/): Why Product Teams Are Moving Past Vanity Metrics Every product org has been there: reporting on traffic spikes, social followers,... - [Missing Features Cost You Deals: Here's How to Catch Them Early](https://bagel.ai/blog/missing-features-cost-you-deals-heres-how-to-catch-them-early/): Every product team has been there: a major deal slips through because your product lacked one critical feature. Frustrating? Sure.... - [Bagel AI Raises $5.5M to Bridge the Gap Between Product and GTM Teams](https://bagel.ai/blog/bagel-ai-raises-5-5m-to-bridge-the-gap-between-product-and-gtm-teams/): We’re thrilled to share that Bagel AI has raised a $5. 5 million Seed round to continue building the world’s... - [10 ChatGPT Prompts That Save Product Managers Hours Each Week](https://bagel.ai/blog/10-chatgpt-prompts-that-save-product-managers-hours-each-week/): 1. Instant PRD Builder You finally got buy-in for a new feature idea. Now you’re staring at a blank doc... - [How to Know If Product Ops Is Actually Working](https://bagel.ai/blog/how-to-know-if-product-ops-is-actually-working/): Product operations has a funny way of becoming the glue that holds things together... without anyone really noticing. It’s the... - [Automated Feedback Triage for Busy Product Teams](https://bagel.ai/blog/automated-feedback-triage-for-busy-product-teams/): Why Manual Feedback Triage Is Slowing Down Product Teams Manual triage, the process of reading, tagging, and prioritizing customer feedback... - [AI Tools Product Managers Should Use Daily in 2025](https://bagel.ai/blog/ai-tools-product-managers-should-use-daily-in-2025/): AI will not build your roadmap, but it will remove the busywork that blocks clarity. The tools below are practical... - [Why Radical Candor is a Game-Changer for Product Managers and How To Do It The Right Way](https://bagel.ai/blog/why-radical-candor-is-a-game-changer-for-product-managers-and-how-to-do-it-the-right-way/): Product management is a constant balancing act, juggling technical feasibility, sales urgency, customer demands, and leadership expectations, all while driving... - [GTM Metrics & Terms: A Must-Know Guide for Product Manager](https://bagel.ai/blog/gtm-metrics-terms-a-must-know-guide-for-product-manager/): Product Managers and Go-To-Market (GTM) teams work best when they’re aligned, it makes growing the business and keeping customers happy... --- # # Detailed Content ## Pages - Published: 2025-06-26 - Modified: 2025-06-26 - URL: https://bagel.ai/service-agreement/ This SERVICE AGREEMENT (the “Agreement”) is entered into by and between Bagel Technologies, Inc. , a Delaware corporation, together with their affiliates (“Bagel”), and (the “Customer”). Each of Bagel and the Customer may be referred to herein as a “Party” and together as the “Parties”. This Agreement shall be effective upon the start date designated on the Order Form (as defined below) (the “Effective Date”). If the undersigned is entering into this Agreement on behalf of an organization, such organization is deemed to be the Customer and the undersigned represents that he/she has the power and authority to bind such organization to this Agreement. 1. Subscription. Subject to the terms and conditions of this Agreement and the Customer’s payment of applicable fees to Bagel, Bagel hereby grants the Customer a limited, non-exclusive, non-perpetual, non- sublicensable, non-transferable, subscription-based and fully revocable right to access and use Bagel’s service (the “Service”) for the Customer’s internal purposes. The term “Service” also includes Bagel’s application programming interfaces (the “APIs”), any general availability release documentation (the “Documentation”) provided to the Customer in connection with the Service’s operation and any Additional Services (as defined below), if applicable. The Customer may only use the Service in accordance with the Documentation, subject to the use limitations indicated in that certain Order Form pursuant to which the Customer subscribes to the Service (the “Order Form”), the terms of this Agreement and applicable law. 2. Additional Services. Bagel may provide additional products or services in connection with the Service, including... --- - Published: 2025-05-13 - Modified: 2025-05-14 - URL: https://bagel.ai/privacy-commitment/ We understand the importance of protecting your sensitive data and are committed to ensuring your peace of mind when integrating your data with Bagel AI. We employ robust data minimization techniques to limit the information we store and process. Specifically, we offer two key mechanisms to control the data that enters our system: Integration Filters at the Record Level: When you connect your data sources to Bagel AI, you can define filters that prevent specific records from being saved within our system. We strive to apply these filters at the API query level whenever possible, ensuring that only the relevant data is retrieved from your sources. In cases where this is not supported by the data source integration APIs, Bagel AI filters the records internally before any data is saved. PII Redaction for Data Fields: Bagel AI provides you with in-product settings that allow you to mask entity types that may contain personal data (PII). You can apply this redaction to specific fields across all integrated records, and to data entered directly within Bagel AI's go-to-market applications. This allows you to control which potentially sensitive information is stored. Importantly, both integration filters and PII redaction are applied before any data is permanently saved in the Bagel AI databases. This ensures that we only store the minimum necessary data required for our services, while adhering to your specified privacy preferences and maintaining the security of your information. Our Privacy Policy > Our Trust Center > Our Terms of Use > --- - Published: 2025-04-13 - Modified: 2025-04-22 - URL: https://bagel.ai/privacy-policy/ Introduction We at Bagel Technologies Inc. ("us", "we" or "Company") respect the privacy of our users (each, a "User" or "you") and are committed to protect the privacy of Users who access, visit, or engage with our website, available at https://bagel. ai (the "Website"), our platform, available at: http://app. bagel. ai ("Platform") and any product, service or feature provided through the Website or the Platform (together with the Website and Platform, the "Services"). This privacy policy ("Privacy Policy") will help you understand what types of information we collect through the Services, how the Company uses it, and what choices you have. We encourage you to read this Privacy Policy carefully and use it to make informed decisions.   This Privacy Policy is integrated into and forms part of our Terms of Use.   From Whom Do We Collect Personal Information? This Privacy Policy applies to the Company's collection, use, and disclosure of the Personal Information (as this term is defined below) of the following categories of Users: Website Visitors: Individuals who visit our Website and who may volunteer certain contact data (such as their email address) to receive communications from us.   End-Users: Those who register on their own or on behalf of an entity or organization to use the Platform.   Collection and Storage of Your Personal Information We collect and use the following types of information about you: Non-personal Information Non-personal Information is non-identifiable information that, when taken alone, cannot be used to identify you. As such, we... --- - Published: 2025-04-13 - Modified: 2025-04-23 - URL: https://bagel.ai/terms-of-use/ These Terms of Use, together with any other agreements or terms incorporated by reference, including the Privacy Policy available at: Privacy Policy (the "Terms") together forms a binding agreement between Bagel Technologies Inc. , a Delaware corporation (the "Company," "us" "our" or "we") and you (the "User," "you" "your"), the person who uses our Website at: https://bagel. ai (the "Website") our platform, available at: http://app. bagel. ai ("Platform") and any product, service or feature provided through the Website or the Platform (together with the Website and Platform, the "Services"). By accessing or using our Services, you agree to these Terms. Your use of the Services is expressly conditioned on your compliance and consent with these Terms. If you do not agree to any of the provisions of the Terms you should immediately stop using the Services. Use of and access to the Services are void where prohibited by law. By using the Services, you represent and warrant that you are 18 years of age or older (or above the age of majority as determined by the applicable law in your jurisdiction of residency), and that your use of the Services does not violate any applicable law or regulation. REGISTRATION AND USER ACCOUNT If you wish to use our Platform, you will be required to register and establish an account with the Company ("Account"). When creating your Account, you must provide us accurate and complete information. You agree to keep your Account information up to date and accurate. When establishing your... --- --- ## Posts - Published: 2025-07-15 - Modified: 2025-07-15 - URL: https://bagel.ai/blog/ai-native-product-manager-part-2-your-ai-workflow-starter-pack/ - Categories: AI in Product Work, AI Product Intelligence, Best Practices, How-To Guides, Learn & Explore, Product Management, Uncategorized - Tags: ai for product managers, ai in backlog grooming, AI product management, ai prompts for product managers, ai tools for pms, AI tools for product managers, ai-native product manager, best ai practices for product teams, decision-making with ai, how pms can use ai, how to use ai in product management, product discovery with ai, product intelligence, product management, product management with ai, product manager ai habits, using chatgpt in product workflows Key Takeaways You don’t need more AI tools. You need to know where AI fits in your decision-making flow. There are four high-impact phases where AI makes your product work sharper, not just faster. Each section includes examples, insights, and one move that can save you hours of second-guessing. Why This Isn’t About Prompts (and Why It Still Kind Of Is) This post isn’t about learning to “prompt better. ” That’s not the point. But prompting is how you interface with AI. It’s your side of the conversation-and most product managers are having the wrong one. When you ask vague questions, you get vague answers. When you treat AI like a shortcut to writing specs or summarizing tickets, you’re missing the bigger win: letting it reduce your cognitive load at the right time in your workflow. This post maps where AI fits into actual product work - not your tool stack. If you're looking for specific tools to support these workflows, check out our companion guide: AI Tools Product Managers Should Use Daily in 2025. It’s structured around four core moments where thinking is hard, time is tight, and AI can give you traction: Discovery Prioritization Planning Validation 1. Discovery: Turn Messy Feedback into Strategic Input What it solves: You have feedback from 14 places. Everyone's shouting. You’re overwhelmed. Use AI to: Identify themes that show up across sources, not just one Compress voice-of-customer signals into business language Spot where user problems are misaligned with what your team thinks is... --- - Published: 2025-07-08 - Modified: 2025-07-09 - URL: https://bagel.ai/blog/fixing-cs-product-friction-with-ai-insights-from-bagel-talk/ - Categories: AI in Product Work, AI Product Intelligence, Communication & Culture, Cross-Team Communication, Customer Success Insights, Data to Decision Workflows, Internal GTM Alignment, Learn & Explore, Product Management, Product-GTM Alignment, Team Feedback Practices, Transparency in Decision-Making, Using AI to Prioritize Work, Voice of Customer at Scale, Webinars - Tags: AI product intelligence, B2B SaaS, Bagel AI, CS-Product Collaboration, customer feedback, Customer Success, Feature Prioritization, Go-To-Market Strategy, GTM alignment, Guy Galon, Ohad Biron, Product Gaps, product management, Product Roadmap, revenue alignment, SaaS growth, Voice of Customer TL;DRAlignment between Product and Customer Success shouldn't be an uphill battle. In this conversation, Bagel AI CEO Ohad Biron and Obrela Chief Customer Success Officer Guy Galon dig into why the disconnect happens and how AI can rebuild trust, reveal customer context, and connect product decisions directly to revenue outcomes. Watch the full recording https://youtu. be/g1TDgsOvDjA Why This Moment Matters Teams are being asked to do more with less. Leaders are under pressure to prove ROI in weeks, not quarters. AI is no longer a nice-to-have table stakes. In this environment, the friction between CS and Product isn’t a workflow issue. It’s a growth blocker. Why the CS-Product Gap Still Exists Product teams think in roadmaps, scalability, and long-term bets. CS teams think in retention, QBRs, and immediate customer outcomes. Both are right. And both often struggle to speak the same language. “They speak different languages. They follow different metrics. They have different incentives. ” Ohad Biron, CEO, Bagel AI Guy highlights that CS is often the voice of the customer but when that voice gets lost, delayed, or deprioritized, everyone suffers. Product feels blindsided. CS feels unheard. Customers feel frustrated. Guy explains: “Trying to fight churn three months before the renewal is not a lost battle, but it’s a bit late in the game. ” Ohad responds: “And yet, CEOs today are asking Product to show ROI in the next quarter not in a year. That pressure’s real. ” What Alignment Really Requires The path forward isn’t about adding... --- - Published: 2025-07-01 - Modified: 2025-07-01 - URL: https://bagel.ai/blog/stop-tagging-feedback-manually-seriously-stop/ - Categories: Automating Feedback Analysis, Data Hygiene & Governance, Data to Decision Workflows, Feature Development, Feedback Loops, Prioritization & Roadmapping, Product Management, Product Operations, Tooling & Integration, Voice of Customer at Scale, Workflow Optimization - Tags: AI product intelligence, customer insights, feedback analysis, feedback automation, product development, product feedback, product management, product operations, roadmap prioritization, tagging workflows Manual feedback tagging might seem like a necessary evil, and it is one of the most inefficient workflows plaguing product teams today. From inconsistent taxonomy to missed insights and delayed responses, the cost of this outdated process is higher than most realize. The Real Cost of Manual Feedback Tagging Inconsistent Across Teams and Sources Each department has its own language. Support logs an "auth failure," sales flags a "login issue," and marketing notes an "account problem. " Without a unified taxonomy, these fragmented inputs lead to conflicting tags, muddy data, and misaligned priorities. Product teams are left reconciling a confusing mess of terms, making it difficult to identify which feedback clusters truly matter. Inaccurate and Time-Consuming Manual tagging relies heavily on human consistency and stamina. Accuracy plummets as tag lists grow, and key context gets lost. Studies show manual tagging can dip below 60% accuracy, undermining trust in any analysis. Doesn’t Scale Manual workflows might handle a few dozen pieces of feedback per month. Once volume spikes, teams get overwhelmed. This is a critical failure point for growing organizations. The feedback backlog becomes insurmountable, valuable insights go unnoticed, and opportunities are lost. Delayed Visibility and Missed Trends Feedback delays mean emerging issues are missed or deprioritized. Imagine not catching a recurring churn driver in time because it took two weeks to manually tag and analyze feedback. That delay can cost hundreds of thousands in ARR. Learn more about this in The Problem with Product Feedback. Product Operations: Manual Tagging Is... --- - Published: 2025-06-24 - Modified: 2025-07-15 - URL: https://bagel.ai/blog/the-ai-native-product-manager-part-1-how-to-think-like-an-ai-native-product-manager/ - Categories: AI in Product Work, AI Product Intelligence, Best Practices, How-To Guides, Learn & Explore, Product Management - Tags: ai for product managers, ai in backlog grooming, AI product management, ai prompts for product managers, ai tools for pms, AI tools for product managers, ai-native product manager, best ai practices for product teams, decision-making with ai, how pms can use ai, how to use ai in product management, product discovery with ai, product intelligence, product management, product management with ai, product manager ai habits, using chatgpt in product workflows Key Takeaways (TLDR) Most product managers are using AI like a copywriting crutch. That’s not a strategy. Smart product managers use AI to compress context, spot patterns, and challenge assumptions. AI is most useful when it's prompting you to think sharper. You don’t necessarily need new tools. You need better habits, and clearer thinking. What This Series Is About Welcome to "The AI-Native Product Manager," a series for product managers who are ready to upgrade their relationship with AI. Whether you’ve played with ChatGPT, used it to rewrite a PRD, or tried to get insights from a Gong call transcript, this series is about helping you turn that occasional dabbling into real, practical habits. Each post gives you no-jargon guidance, repeatable habits, and zero tool worship. No hype. Just the mindset and methods behind better product thinking with AI. This first post sets the stage. It’s about what most product managers get wrong with AI and what to do instead. Why Most PMs Are Using AI Wrong You’re staring at a backlog that’s 400 tickets deep. Sales just Slacked "any ETA on that thing customers keep asking for? "Your last "customer insights" doc is a Notion graveyard from Q1. So you open ChatGPT. You type:"Summarize the top customer requests from our support tickets. " It gives you something vaguely correct, lightly repetitive, and weirdly confident about things you didn’t ship. You skim it. Copy-paste a few lines. Move on. This is what we’ll call performative AI usage. It feels productive.... --- - Published: 2025-06-18 - Modified: 2025-06-18 - URL: https://bagel.ai/blog/product-feedback-trends-youre-missing-until-its-too-late/ - Categories: AI in Product Work, Automating Feedback Analysis, Feedback Loops, Product Management, Team Feedback Practices - Tags: AI product intelligence, Bagel AI, customer feedback analysis, feedback loop, product feedback trends, product management, product strategy, revenue alignment, roadmap prioritization, SaaS growth Staying ahead of product feedback trends isn’t just about staying competitive. It’s about building the right features at the right time, with the right insights. The teams that miss the signals hidden in customer conversations, tickets, and sales notes are the ones building in the dark. And they’re the ones falling behind. Product feedback today is messy, fragmented, and overwhelming. Yet it holds the clearest signals about what your customers need next and what will drive your revenue forward. That’s where Bagel AI's platform overview comes in: turning scattered noise into strategic clarity and helping product leaders move from guesswork to growth. 1. The Shift to Real-Time, Actionable Feedback Customer expectations have changed. Surveys that arrive weeks after a support issue are out. Teams now rely on real-time feedback captured as the customer interacts with your product or GTM teams. In-the-moment insights from support tickets, sales calls, and product usage patterns offer context you can't replicate later. Bagel AI pulls from every source such as Salesforce, Zendesk, Gong, and Jira, and gives product teams a live stream of feedback, ranked by urgency and business impact. Actionable alerts when revenue is at risk or product friction appears help you fix problems before churn happens. Action Item: Audit your current feedback sources and identify where real-time insights are missing. Explore integrations that bring GTM and product data into a single view. Deep Dive: Many teams still rely on quarterly feedback reviews. By the time those insights surface, it’s too late to change... --- - Published: 2025-06-17 - Modified: 2025-07-13 - URL: https://bagel.ai/blog/productboard-vs-bagel-ai-what-product-teams-actually-need-from-ai-product-tools/ - Categories: AI in Product Work, Automating Feedback Analysis, Bagel AI News, Behind the Product, Feature Development, Frameworks & Methodologies, Go-To-Market Strategy, Learn & Explore, Product Management, Product Operations, Product Tips - Tags: AI for product teams, AI product management, AI product roadmap, B2B product feedback software, Bagel AI vs Productboard, GTM alignment tools, product feedback automation, product intelligence platform, product management comparison 2025, productboard alternative, productboard vs bagel, revenue-driven product decisions, roadmap prioritization AI, salesforce integration product tools AI has officially entered the product stack. But just because a tool uses AI doesn't mean it changes how product decisions get made. Productboard and Bagel AI both promise to help you manage product feedback, but only one is built for true revenue impact. Compare Bagel AI vs. Productboard to see which fits modern teams best. , but only one of them actually helps you connect that feedback to revenue. Productboard offers structure, custom frameworks, centralized ideas, stakeholder views. But structure without intelligence still leaves teams interpreting feedback manually. Bagel AI was built differently. It's not a roadmap tool with AI features tacked on, it's a revenue-connected, feedback-aware product intelligence layer that helps you decide what to build, when, and why. This post breaks down where the platforms differ and why Bagel AI is the better fit for modern product teams looking to stop guessing and start growing. 1. Tagging Is Just the Start, Insight Is the Finish Line Productboard lets you centralize customer feedback. But once it’s there, you still need to tag, analyze, and prioritize it yourself. Bagel AI reads the full signal: Zendesk tickets, Gong calls, Salesforce notes, Slack messages and surfaces grouped product gaps, with customer names, revenue context, and urgency attached. You don’t just get tags. You get direction. 2. From Feedback to Revenue, Without the Handoff Productboard supports prioritization via custom formulas like RICE or WSJF but it doesn’t tell you if a request affects a $500K renewal. Bagel AI ties every request to... --- - Published: 2025-06-17 - Modified: 2025-06-17 - URL: https://bagel.ai/blog/the-real-reason-your-product-gtm-alignment-is-failing/ - Categories: AI in Product Work, AI Product Intelligence, Cross-Team Communication, Data to Decision Workflows, Go-To-Market Strategy, Internal Collaboration, Internal GTM Alignment, Prioritization & Roadmapping, Product Management, Product Operations, Product-GTM Alignment, Sales & Product Collaboration, Workflow Optimization - Tags: customer feedback, GTM strategy, product intelligence, product management, product–GTM alignment, revenue impact, SaaS growth Let’s break down the real causes behind this issue and how platforms like Bagel’s AI Product Intelligence help companies fix it with minimal effort. What Is Product–GTM Alignment? True alignment means your product, marketing, sales, and customer success teams work in sync toward shared customer and revenue goals. When that breaks, the results are messy: confused buyers, stalled deals, and internal blame games. The "85/85 Alignment Gap" Here’s a stat that should stop you in your tracks: 85% of teams believe they’re aligned, yet 85% also report misalignment. That’s not just ironic, it’s a silent killer of growth. It’s what we call "Alignment Exhaustion Syndrome" at Bagel: too much effort spent trying to align, not enough spent making decisions. Why Misalignment Happens 1. Team Silos Departments like product, sales, and support work in isolation. They use different tools and success metrics, creating inconsistency. This results in duplicated work and mixed messages across the buyer journey. 2. Disconnected Insight Loops Each team captures feedback differently, leading to fragmented data. Critical product gaps go unnoticed or get buried in Slack, Gong, or Zendesk logs. Without a single source of truth, decisions rely on hunches. 3. Unclear Decision Ownership Lack of role clarity leads to indecision and missed market windows. Different teams optimize for different KPIs, causing finger-pointing when goals aren’t met. The Cost of Misalignment AreaImpactRevenueLost deals due to ignored customer needsCustomer ExperienceConfusing handoffs and mixed messagingEmployee MoraleFrustration from inefficient workflowsSpeed to MarketLaunch delays and lost competitive advantageResource WasteTime wasted on redundant... --- - Published: 2025-06-08 - Modified: 2025-07-13 - URL: https://bagel.ai/blog/enterpret-vs-bagel-ai-what-product-teams-actually-need-from-ai/ - Categories: AI in Product Work, Bagel AI News, Behind the Product, Feature Development, Feedback Loops, Learn & Explore, Prioritization & Roadmapping, Product Management, Product Operations, Product Tips, Tooling & Integration - Tags: AI feedback analytics comparison, Bagel AI, Enterpret alternative, Enterpret vs Bagel AI, feedback prioritization, feedback to revenue tool, GTM alignment, product feedback AI tool, revenue-driven product insights The AI Gap That Matters Most AI is flooding the product stack. But not every tool labeled "AI-powered" actually moves the needle. Both Enterpret and Bagel AI claim to use AI to make sense of customer feedback. But what product teams need isn’t another layer of surface-level tagging - it’s answers they can use. Enterpret gets points for organizing qualitative feedback. But when teams still have to decode, interpret, and align that feedback themselves, it's only half the job. Bagel AI is built differently - from the ground up to surface revenue-impacting signals, prioritize based on urgency, and close the loop between what customers say and what gets built. This post unpacks the difference and why Bagel’s approach to product intelligence is better suited to modern product teams. What Product Teams Actually Need from AI Feedback Tools 1. Accuracy at the Case Level, Not Just NLP Tagging Enterpret leans on NLP tagging with custom taxonomies. Useful? Sure. But in practice, many teams find themselves manually reviewing outputs or cross-checking with support and sales. Bagel AI learns from your data: historical tickets, call transcripts, CRM notes - and improves over time. It groups related feedback under themes you already recognize with none of the manual upkeep. 2. Revenue Alignment Is Built-In Tagging themes is the first mile. But what about knowing which feedback actually affects deals, retention, or upsell? That’s where most tools stop - and where Bagel AI begins. Bagel integrates directly with Salesforce and Gong, tying feedback to account... --- - Published: 2025-06-08 - Modified: 2025-06-08 - URL: https://bagel.ai/blog/the-problem-with-product-feedback-its-buried-in-6-tools/ - Categories: AI in Product Work, AI Product Intelligence, Automating Feedback Analysis, Data Hygiene & Governance, Feature Development, Feature Monetization, Feedback Loops, Frameworks & Methodologies, Prioritization & Roadmapping, Product Management, Product Operations, Team Feedback Practices, Tooling & Integration, Using AI to Prioritize Work - Tags: ARR-driven decisions, Bagel AI, customer insights, feedback centralization, GTM alignment, product feedback analytics, product management tools, roadmap prioritization, SaaS product strategy Awareness of the Chaos Product teams today are inundated with feedback from multiple sources: surveys, social media, customer support tickets, product reviews, and more. This “feedback chaos” leads to: Overwhelming Data Volume: Collecting feedback from thousands of customers across various channels quickly becomes unmanageable. Diverse and Conflicting Preferences: Different customers have unique needs, making it difficult to identify common trends or prioritize actions. Stakeholder Confusion: Multiple teams and stakeholders may have conflicting priorities, causing paralysis or misaligned product strategies. Missed Insights and Delayed Actions: Without a clear system, important feedback can slip through the cracks, leading to missed opportunities for improvement and delayed decision-making. Every product team has faced it-the avalanche of customer feedback that feels impossible to untangle. Each stakeholder has their own list of must-have features, driven by the diverse needs of their audiences. The result is a battle of opinions, where the loudest voices often dictate the direction. Feedback isn’t missing-it’s just buried. And most of it never sees the light of day. Even when teams put in the work to tag and triage it manually, the signal gets lost. By the time a recurring theme is noticed, it's either too late (the customer churned), or too abstract (another spreadsheet that doesn't connect to revenue). What a Product Feedback Analytics Tool Should Do Centralizing feedback is about bringing all this scattered information into one place, making it accessible, analyzable, and actionable for everyone involved. Key benefits include: Visibility and Transparency: All feedback is visible to relevant teams,... --- - Published: 2025-06-04 - Modified: 2025-06-04 - URL: https://bagel.ai/blog/the-hidden-cost-of-cutting-pms-why-ai-cant-replace-product-context/ - Categories: AI in Product Work, AI Product Intelligence, Communication & Culture, Cross-Team Communication, Internal Collaboration, Learn & Explore, Product Management, Product Operations, Product Tips, Product-GTM Alignment, Using AI to Prioritize Work - Tags: AI in product, AI in SaaS, Bagel AI, context driven, customer insights, GTM alignment, PM role, product context, product leadership, product management, product ops, product strategy, roadmapping, SaaS growth I posted on LinkedIn recently about a clip from ElevenLabs where Luke Harries says they don’t have product managers. Engineers own the roadmap. They build, ship, and analyze results. No PMs. Except... they do. They just call them "growth. " That post hit a nerve. The comments were fast, deep, and from people who’ve actually lived it. So I wanted to go deeper here: What really happens when you remove PMs? Can AI actually fill the gap? And what’s at stake when context goes missing? https://youtube. com/shorts/DYBSa6HrYpE? feature=shared The Temptation to Flatten Early-stage teams, especially in SaaS, want speed. They want autonomy. PMs get folded into growth, or vanish altogether. Engineers pick up more responsibility. AI tools start doing the jobs humans used to do manually. In theory, it sounds efficient. In practice, you start to lose the plot. You ship fast, but not always smart. You respond to noise instead of signal. You chase requests instead of strategy. And the roadmaps? Full of ideas that sound good but never land. Why Context Is the Whole Game Product managers don’t exist to slow things down. They exist to connect the dots. They connect what users are saying to what sales is hearing to what actually moves the needle for the business. They connect customer pain to roadmap priorities. They say no to the cool-but-pointless features and yes to the quiet ones that drive retention. And they live in a mess. In the friction between teams. In the nuance of why... --- - Published: 2025-06-03 - Modified: 2025-07-01 - URL: https://bagel.ai/blog/sentiment-vs-revenue-metrics-five-numbers-product-teams-should-track-instead-of-nps/ - Categories: Data Hygiene & Governance, Feature Development, Feedback Loops, Product Management, Product Metrics & KPIs, Product Operations, Product-GTM Alignment - Tags: AI product tools, B2B SaaS, Bagel AI, churn reduction, customer insights, customer retention, expansion signals, feature adoption, GTM alignment, LTV, MRR, NPS alternatives, product analytics, product intelligence, product management, product metrics, product-led growth, revenue impact, roadmap prioritization, SaaS growth Why Product Teams Are Moving Past Vanity Metrics Every product org has been there: reporting on traffic spikes, social followers, or an impressive NPS. These numbers offer a quick hit of confidence, but they rarely answer the question that matters most: what's the impact on revenue? Metrics like NPS give a quick pulse on sentiment and often rise alongside growth. That said, sentiment alone doesn’t tell you whether customers are actually using a new feature, inching toward renewal, or showing expansion potential. And In a landscape where poor product–GTM alignment directly erodes revenue, product leaders can’t afford that kind of ambiguity. That’s why more teams are making the shift to metrics that link directly to outcomes: retention, expansion, win rate, and monthly recurring revenue. And increasingly, these teams are using purpose-built product intelligence tools to track and act on these insights without manual analysis or siloed workflows. Sentiment Metrics vs. Revenue-Linked Metrics Metric TypeExamplesWhy It MattersSentiment MetricsNPS, site traffic, followers, clickthroughUseful health check but limited for product decisionsRevenue MetricsFeature Adoption Rate, churn-risk delta, expansion triggers, MRR, LTVTied directly to customer behavior and business health Five Metrics That Replace NPS and Actually Move the Business 1. Feature Adoption Rate Feature Adoption Rate tracks how many users adopt and consistently use a new feature. It’s one of the clearest signals that what you're building is actually solving a problem. As Glassbox notes, adoption metrics help prioritize the roadmap around user value. High Feature Adoption Rates often correlate with lower churn, stronger engagement,... --- - Published: 2025-05-29 - Modified: 2025-05-29 - URL: https://bagel.ai/blog/missing-features-cost-you-deals-heres-how-to-catch-them-early/ - Categories: Feature Development, Feedback Loops, Frameworks & Methodologies, Prioritization & Roadmapping, Product Management, Product Metrics & KPIs, Uncategorized - Tags: AI for product teams, feature gaps, feedback to revenue, GTM alignment, lost deals, product and GTM alignment, product intelligence, product strategy Every product team has been there: a major deal slips through because your product lacked one critical feature. Frustrating? Sure. Avoidable? Definitely. If you build a system to catch and act on feature gaps before they block revenue. This post answers a question product leaders ask AI tools all the time:“How can I avoid losing deals due to missing features? ” Let’s break down what causes these gaps and how to fix them using a structured, data-backed, AI-augmented workflow. Why Feature Gaps Derail Deals (and How to Spot the Patterns) Feature gaps hurt because they show up late and loud: Competitor Advantage: If a key feature exists elsewhere, you’re out of the running. Lost Trust with Prospects: If feedback isn’t acknowledged, buyers assume the same for their future needs. Sales Desperation: Sales might overpromise or push requests that aren’t vetted, putting pressure on product to react. "We lost a $300K deal because the competitor had a dashboard view. Can we build that next quarter? " Instead of reacting, use these signals proactively. How to Catch Feature Gaps Before They Cost You 1. Systematically Track Lost Deals (with Real Context) Use your CRM (like Salesforce or HubSpot) to standardize loss reasons. Replace free-text with structured fields like: “Missing Feature,” “Price,” “Integration Required. ” Make notes mandatory. Reps must specify what feature was missing and what the competitor offered. Use tools like Gong or Chorus to review call transcripts. Sometimes the customer says more than what ends up in CRM. AI-enhanced Tip:... --- - Published: 2025-05-21 - Modified: 2025-05-29 - URL: https://bagel.ai/blog/bagel-ai-raises-5-5m-to-bridge-the-gap-between-product-and-gtm-teams/ - Categories: AI in Product Work, AI Product Intelligence, Bagel AI News, Milestones, Revenue-Driven Roadmaps - Tags: AI in product management, Bagel AI, company news, funding, GTM alignment, product intelligence, product strategy, product-GTM collaboration, revenue-driven roadmaps, SaaS funding, seed round, startup growth We're thrilled to share that Bagel AI has raised a $5. 5 million Seed round to continue building the world’s first AI-native Product Intelligence platform. Our mission is simple: connect product decisions to real business impactת automatically. Led by and joined by Demo Capital, Loyal VC, CS Angel, and notable angels like Oded Gal (former CPO at Zoom), Udi Mokady (Founder, CyberArk), and Sean Regan (ex-Atlassian), this round will help us scale fast: more customers, stronger tech, and a deeper footprint inside the tools product and GTM teams already use. A $3 Trillion Problem We're Built to Solve Product and GTM teams are misaligned and it’s costing companies more than missed revenue. It leads to failed launches, churn, and wasted roadmaps. Feedback is scattered across Gong, Jira, Salesforce, and Zendesk. Context gets lost. Teams rely on gut feel instead of real insight. Bagel AI flips that. Instead of drowning in disconnected data, Bagel uses company-specific AI models to surface high-impact product gaps, customer pain points, and deal blockers and ties them directly to revenue. We don’t just unify feedback. We quantify impact and deliver decision-ready insights into the workflows where teams already operate. Bagel AI is proving its impact where it matters most helping product and GTM teams uncover insights and turn them into results. Customers get value quickly, and the platform becomes a natural part of how teams work. Roni Bonjack, Venture Partner at at. inc/ Growth With Clarity > Growth at All Costs “As companies begin shifting away... --- - Published: 2025-05-20 - Modified: 2025-05-28 - URL: https://bagel.ai/blog/10-chatgpt-prompts-that-save-product-managers-hours-each-week/ - Categories: AI in Product Work, AI Product Intelligence, Automating Feedback Analysis, Frameworks & Methodologies, Product Management, Product Metrics & KPIs, Product Operations, Tooling & Integration, Using AI to Prioritize Work, Workflow Optimization - Tags: AI in product work, AI prompts for PMs, ChatGPT for product managers, feedback analysis, PRD writing, prioritization tools, product management automation, product operations, product strategy support, release notes automation, stakeholder updates, time-saving tools for PMs, triage automation, workflow optimization 1. Instant PRD Builder You finally got buy-in for a new feature idea. Now you’re staring at a blank doc wondering how to pull together all the context, scope, and goals before the next sync. You don’t need inspiration, you need a head start. You’re a seasoned PM. Draft a concise Product Requirements Document for {feature idea}. Include: • Problem statement • Why now • Goals & success metrics • Assumptions • Scope (in/out) • User flows (bullets, not wireframes) • Open questions Limit to 600 words. 2. Customer‑Feedback Synthesizer You’ve got a Notion doc, a Gong playlist, and three Slack threads full of “customer feedback” , and no time to read all of it. But the team’s asking, “What are people actually struggling with? ” and you need to find the gap. Fast. You’re a data whiz. From the feedback snippets below, surface the top 5 recurring pains, tag each with persona and frequency, and suggest one quick win per pain. Feedback: {paste Gong/Intercom/Jira snippets} Return a markdown table: Pain ▸ Persona ▸ Frequency ▸ Quick win. If you’re using Bagel AI, skip the prompt. Feedback is already tagged, grouped, and quantified , sorted by persona, volume, and revenue , without you lifting a finger. 3. Outcome‑Based Roadmap Draft Leadership just finalized OKRs. You’re on the hook to turn vague goals into product initiatives that make sense. Instead of starting from a blank slide, you want a quick draft that ties actions to outcomes. Act as a product strategy... --- - Published: 2025-05-20 - Modified: 2025-05-28 - URL: https://bagel.ai/blog/how-to-know-if-product-ops-is-actually-working/ - Categories: Cross-Team Communication, Data Hygiene & Governance, Experimentation & Iteration, Frameworks & Methodologies, Internal Collaboration, Product Management, Product Metrics & KPIs, Product Operations, Product-GTM Alignment, Revenue-Driven Roadmaps, Workflow Optimization - Tags: Bagel AI, feedback loops, GTM alignment, product management, product metrics, product operations, product ops best practices, product strategy, roadmap prioritization Product operations has a funny way of becoming the glue that holds things together... without anyone really noticing. It's the function that makes launches smoother, decisions faster, and feedback loops tighter. But here’s the hard part: how do you actually know if it’s working? It’s not always obvious. Product ops can fade into the background, quietly cleaning up processes, connecting dots, and making things flow. Helpful? Absolutely. Easy to measure? Not so much. So what should you be looking for? 1. Increased alignment Are teams working off the same page? Literally? A good product ops function creates a shared source of truth. That means roadmaps that reflect company goals, tools that give visibility across functions, and way fewer "Wait, what are we building? " moments. Real-world moment: One B2B SaaS company we spoke with had a 3-month delay on a critical feature because Sales and Product were working off two different assumptions. Once product ops introduced a unified roadmap with auto-sync to Salesforce and Slack, the confusion dropped and so did the delays. 2. Incremental reuse If every team is starting from scratch each time they launch a feature or run a beta, something's off. Product ops should make it easier to build on what’s already been done. Reusable frameworks. Clear processes. Templates that actually get used. 3. Efficiency gains Can PMs move faster with fewer check-ins? Are GTM teams unblocked without needing a round of calls? These are signs your processes are actually supporting speed, not slowing it down.... --- - Published: 2025-05-19 - Modified: 2025-05-28 - URL: https://bagel.ai/blog/automated-feedback-triage-for-busy-product-teams/ - Categories: AI in Product Work, Automating Feedback Analysis, Product Management, Product Operations, Tooling & Integration, Using AI to Prioritize Work, Workflow Optimization - Tags: AI in product ops, AI tools for product managers, cost of manual triage for product teams, customer feedback analysis, feedback automation, GTM alignment, how to automate product feedback triage, manual triage, product intelligence, product management tools, product ops time-saving tools, reducing backlog triage time, sales and CS collaboration, time cost of manual work, zendesk alternatives Why Manual Feedback Triage Is Slowing Down Product Teams Manual triage, the process of reading, tagging, and prioritizing customer feedback across tools like Zendesk, Gong, Slack and other tools feels like part of the job. But for product managers, it's an invisible time sink. Behind every tagged support ticket or summarized sales call, there is a cost: context-switching, duplication, and missed insights. These small tasks steal time from what matters most: building the right product. The Real Cost of Manual Triage in Product Management Manual triage may seem manageable early on, but it comes with hidden costs that can scale quickly as feedback volume increases. 1. Time and Resource Drain Every support ticket, Slack thread, and sales call highlight adds minutes to a PM's day. When triage is done by hand, those minutes compound. In a typical week: 40 Zendesk tickets x 2 minutes 10 Gong snippets x 3 minutes 15 Slack threads x 2 minutes That is over 3 hours per PM per week, often spent copying, pasting, tagging, and guessing. Multiply that across your product team, and you're losing dozens of hours a month on work that doesn't move the roadmap forward. 2. Low Scalability Manual triage does not scale. As volume grows, the ability to keep up drops off. One person can only support so many triage cycles before backlogs build and priorities blur. Product teams become reactive instead of strategic. 3. Delayed Decisions and Poor Prioritization Without automation, product decisions are driven by whoever speaks loudest,... --- - Published: 2025-05-19 - Modified: 2025-05-28 - URL: https://bagel.ai/blog/ai-tools-product-managers-should-use-daily-in-2025/ - Categories: AI in Product Work, Communication & Culture, Frameworks & Methodologies, Internal GTM Alignment, Product Management, Sales & Product Collaboration, Tooling & Integration, Using AI to Prioritize Work - Tags: AI tools for product managers, Bagel AI, product management AI tools AI will not build your roadmap, but it will remove the busywork that blocks clarity. The tools below are practical and fast. Each one includes a quick overview, a real PM use case, an example, and a tip to get started. No fluff. No hype. Just AI you will actually use. Writing, Research, and Strategy ChatGPT: From Raw Notes to Clear Thinking Turns raw text into structured content. Drafts specs, rewrites backlog tickets, explains SQL, or challenges fuzzy ideas in plain language. Use in a PM's day to day: Clean up discovery notes into stories and acceptance criteria. Example: Paste five pages of interviews and get the top three user pains, ranked by frequency. Tip: Save your product vision as a custom GPT so replies land in the right tone and context. Claude: Long-Context Risk Review Reads long documents without losing the thread. Useful for deep reviews, risk audits, and ideation. Use in a PM's day to day: Load a full spec and ask for blind spots or edge cases. Example: Feed it a 50-page compliance doc and extract the five sections that matter for your feature. Tip: Start the thread with past incidents. Claude will apply that lens to new ideas. Notion AI: Summaries That Stay on Brand Summarizes, drafts, and answers questions within Notion. Keeps styling and structure intact. Use in a PM's day to day: Summarize PDFs and surface insights next to your roadmap. Example: Drop a Gartner guide and ask for a SWOT matched to your... --- - Published: 2025-05-18 - Modified: 2025-05-28 - URL: https://bagel.ai/blog/why-radical-candor-is-a-game-changer-for-product-managers-and-how-to-do-it-the-right-way/ - Categories: Communication & Culture, Cross-Team Communication, Culture of Curiosity, Leadership & Influence, Product-GTM Alignment, Team Feedback Practices, Transparency in Decision-Making - Tags: communication best practices, cross-functional collaboration, decision-making frameworks, feedback culture, GTM alignment, prioritization strategy, product leadership, product management, roadmap transparency, sales and product tension, team alignment Product management is a constant balancing act, juggling technical feasibility, sales urgency, customer demands, and leadership expectations, all while driving growth. The pressure to please everyone often leads to misalignment, unclear priorities, and wasted effort. The best product managers don’t just mediate- they communicate with Radical Candor, a framework that blends honesty, empathy, and directness to align teams, build trust, and drive impact. At Bagel AI, we’ve seen how poor communication between product and go-to-market (GTM) teams costs companies millions. Radical Candor isn’t about being blunt or abrasive, it’s about ensuring the right conversations happen at the right time, with full transparency, so product decisions drive real business outcomes. Research underscores the stakes: a well-optimized Product Manager can boost company profits by 34. 2%, yet 56. 4% struggle with competing objectives, and 50. 8% cite lack of time as a major hurdle. This makes open, honest, and actionable feedback critical to success. What is Radical Candor? Popularized by Kim Scott, Radical Candor is a feedback approach that balances two critical dimensions: 1. Caring Personally - Building strong relationships and showing genuine concern for your team. 2. Challenging Directly - Providing clear, honest feedback, even when it’s uncomfortable. Too often, product leaders fall into one of these ineffective communication styles: Ruinous Empathy - Being too nice and avoiding tough conversations. Obnoxious Aggression - Being brutally honest but lacking care. Manipulative Insincerity - Being political, passive-aggressive, or avoidant. Radical Candor cuts through these barriers, creating an environment where people feel safe to... --- - Published: 2025-05-18 - Modified: 2025-05-28 - URL: https://bagel.ai/blog/gtm-metrics-terms-a-must-know-guide-for-product-manager/ - Categories: Cross-Team Communication, Deal Blockers & Churn Signals, Prioritization & Roadmapping, Product Management, Product Metrics & KPIs, Product-GTM Alignment, Sales & Product Collaboration - Tags: churn signals, cross-functional teams, customer insights, deal blockers, GTM metrics, product KPIs, product management, roadmap planning, sales collaboration Product Managers and Go-To-Market (GTM) teams work best when they’re aligned, it makes growing the business and keeping customers happy much easier. But let’s be honest, deciphering the language of Sales, Customer Success (CS), Support, and Marketing can sometimes feel like learning a whole new dialect. This guide is here to help! We’re breaking down the most important GTM terms and metrics so you can confidently navigate cross-functional conversations and make smarter product decisions, complete with sharp, real-life examples that show how these concepts work in practice. Sales Terms and Metrics Monthly Recurring Revenue (MRR) Definition: The predictable total revenue generated from all active subscriptions in a particular month. Why It Matters for Product Managers: MRR helps Product Managers track revenue trends and the impact of pricing strategies. Example: If 1,000 users subscribe to a $50/month plan, MRR is $50,000. How Product Managers Can Use It: Monitor how feature releases or promotions impact revenue growth. Annual Recurring Revenue (ARR) Definition: The total recurring revenue a company expects from customers in a year. Why It Matters for Product Managers: ARR helps Product Managers understand the revenue impact of features and product improvements. Example: A Product Manager at a subscription-based design tool releases a new AI-powered feature. If 500 customers upgrade to a $200/year premium plan, ARR increases by $100,000. How Product Managers Can Use It: Identify features that drive premium conversions and influence pricing strategies. Customer Acquisition Cost (CAC) Definition: The total cost of acquiring a new customer, including marketing and... --- ---