Inari surfaces customer insights and revenue generating product opportunities from your customer data auto-magically using AI. Instead of sifting through 100’s of user interviews or 1000’s of pieces of customer feedback manually, Inari automates the process of highlighting interesting quotes, identifying trends, uncovering impactful feature requests, and tying helpful prioritization metrics with features so your team can spend less time analyzing and more time building products that customers love.
External link for Inari (YC S23)
169 Madison Ave
#2423
New York, NY 10016, US
Inari (YC S23) reposted this
🚨 Big news: Amplitude acquired Inari (YC S23)! Their founders, Frank Lee and Eric Kim, are our kind of builders: customer-obsessed, high-ownership, and creative. Frank and Eric are joining the #AmplitudeAIAgents team to help make self-improving products a reality. Learn more: https://lnkd.in/e-ee2Uya
Inari (YC S23) reposted this
I’ve been super quiet for a minute… so figured I’d break the silence and announce that Inari (YC S23) has been acquired by Amplitude!!! When we started Inari, our mission was to help teams build better products by using AI to understand customer feedback at scale. As we iterated on Inari while working with thousands of teams, it became clear that feedback is just one piece of a larger product puzzle. We learned teams needed the full spectrum of digital analytics – product analytics, session replays, experiments, guides and surveys, AND customer feedback – unified in one holistic platform for AI to generate the most relevant insights and actions. And that’s why Eric and I are thrilled to be building AI agents @ Amplitude, the pioneer in digital analytics. We’re accelerating our dream of helping teams better understand customers and transform data into improvements, without the manual work. Eventually, our work at Amplitude will make "self-improving" products a reality! As excited as we are to join Amplitude, we're feeling a lot of emotions closing the chapter on Inari. Relief that we’re landing at an amazing spot. Excitement since we get to continue tackling a problem space we love with way more support. But also some sadness that we couldn’t crack the code on building Inari into the breakout product we dreamed of. Startups are f*cking tough. No amount of operating can ever reallllly prepare you for the ups and downs of founding: - Most days you’re writing crap code, fixing bugs, and firefighting customer escalations - Many days you’re writing cringe posts on social media desperately trying to find users - Some days you’re relying on ChatGPT to help hopefully file your taxes accurately 😬 - Every day, especially while working weekends, you’re questioning why you’re doing this in the first place But those random Slack messages we’d get from customers telling us some version that “Inari is dope”, “Inari saved me a bunch of hours reviewing feedback this month”, or “can I PLEASE get some more credits, this thing is insane"... Priceless. Anyways… Eric and I are deeply grateful that you gave Inari a chance 🙏🏼. And we’re hyped to make AI agents for product teams real @ Amplitude then share what we build with y’all soon! Let’s get it 💪🏼
Inari (YC S23) reposted this
I know I’m late but this might be the best thread from r/ProductManagement that went viral in the last week (377 upvotes and 105 comments 👀) and a potential alternative path for prototyping for PMs. OpenAI’s new 4o image model shipped a step-function change with generating high-quality images and designs and improved instruction following. Yes we all know we can generate Ghibli images, but IMO we’ve totally underestimated its ability to prototype user flows, mockups, and simple UI components. With short prompts like “Create an image of the listing screen for a hotel booking app”, “Add a tab bar at the bottom of the screen to navigate to different views of the app”, or even “Generate a lo-fi mockup of the same design”, 4o was able to generate reasonably realistic UI mocks and wireframes with minimal instructions. TBH it’s still up in the air on whether generate images via image models or just generating throwaway code in ChatGPT Canvas, Lovable, or Bolt is the most effective path for product teams to prototype, but it’s awesome to have a new and more precise path for exploring ideas. I’ve been playing around with it a ton for Inari (YC S23). The capabilities have crossed the chasm and I’m excited to see more product teams try this out! Props to r/lixia_sondar on the thread.
Inari (YC S23) reposted this
We quintupled Inari (YC S23)’s monthly growth rate since our last launch so Eric and I have been heads down, with no time for yapping (😅), shipping fixes to keep the insights flowing for 100’s of new product teams. We were able to sneak in a few new features on top of the 100's of fixes under the hood so I wanted to share a few today! 💻 Ingest feedback, customers, and companies into Inari via API The #1 request we receive in Inari continues to be easier ways to pull in new data for analysis so we’ve opened up our API for Growth and Enterprise orgs to push or edit feedback, customer, and company data programmatically (docs in comments). 🔗 Ingest Linear or Jira issues then auto-link them to relevant feedback Connect Linear or Jira to ingest then sync existing issues with context from your customer feedback and CRM. View prioritization metrics, deal size, and browse related customer context to improve the quality of your PRDs. We've been iterating on improving the accuracy of what gets linked. 🔍 Browse top highlights across feedback in a unified flow Skim through all top customer quotes across your feedback, user interviews, sales conversations, and other sources in a unified and searchable highlight browser. If your team has other requests, bugs, or just want to jam on product problems top of mind that Inari can support with, DM me!
Inari (YC S23) reposted this
I’ve been researching word of mouth growth this week for Inari (YC S23) and thought this Lenny’s podcast with Nilan Peiris, CPO of Wise, had a super tactical playbook on how they drove 70% of their traffic via WoM: 1. Wise experimented on different growth channels but realized the only way to make their economics work was via word of mouth since they’re a low price and low margin product. 2. They sent out NPS surveys asking "Would you recommend Wise to a friend?” and found there was an exponential increase in WoM invite rates at the top end. There were low invite rates from 0 to 6. Invites doubled from 7 to 8. Doubled again 8 to 9. Then doubled again 9 to 10. When they modeled it, ROI on NPS became insanely huge. 3. The NPS results were sent internally to the whole company for maximum visibility. They sifted through thousands of comments and pulled out the 3 themes that led to the 9 and 10 NPS comments: price, speed, and ease of use. People became “evangelical” only when they had a meaningfully cheaper, faster, and simple experience. So they built their product pillars and KPIs around these 3 themes. 4. Wise’s second NPS learning: minor price reductions didn’t lead to WoM improvement. They only “got advocacy when we were 8 to 10 times cheaper. That’s when people started talking about it”. Because of the magnitude needed to “wow” someone and drive this NPS bump, their goal became figuring out how to “move money instantly” and “drop the price all the way from 6 down to 0.35” 5. I thought Nilan and Lenny’s take was interesting: if you want to “blow your users’ socks off” and “give them an experience they didn’t know was previously possible”, you probably can’t just take what somebody else has done and improve on it. You have to take the harder route, work backwards from an ideal experience, then build something new to achieve an amazing and differentiated experience. 6. Lastly, Wise took their core themes and rebranded their mission and company around it. Surprisingly they got the most customers from an email ever by making their mission clear, personal, and resonant with those customers based on those themes. There were no CTAs but people just forwarded the email to others saying “you should check out Wise”. Pretty cool case study on how talking to customers and converting their emotions and wants into core product pillars led to so much growth 😁
Inari (YC S23) reposted this
I realllly love this thread by Emmett Shear from 2021 on his top learnings from founding Twitch. It didn’t fully resonate with me 4 years ago when I was working purely on product. But after working on Inari (YC S23), many of his takes hit completely differently and I would have saved months of headaches if I had internalized his lessons before founding. 1️⃣ “Make something 10 people completely love, not something most people think is pretty good.” Easiest mistake is being a “feature factory” and building everything customers ask for but doing nothing particularly well. It’s 10x harder to prioritize down to only the most acute problem for a few folks then executing on that solution so well that you become unignorable. 2️⃣ ”If your product is for consumers, either it’s a daily habit, it’s used consistently in response to an external trigger, or it’s not going to grow.” Growth is tough. Ideally embed your product into a frequent consumer habit and design an effective growth loop, otherwise finding new and engaged users literally feels like pushing a rock up a hill. 3️⃣ ”Three ways to have a startup idea: something you want, something you’ve directly experienced others needing, something you’ve invented through analytic thought. They are listed in order of increasing risk.” Keep a note on all the things you want or problems you face in life or work - those are great opportunities to tackle. And I'd maybe add a filter for whether what space you build in is already or can grow into a huge market. 4️⃣ ”If you’re a first time manager, you suck. That’s ok, everyone sucks. Apologize to your employees, get a coach or join a support group, read books, and generally treat management like a new important skill you can master.” I wish I internalized this tip but not just for management. Founders have to do everything, so naturally you will suck at everything to start. We could have moved 10x faster by tapping others for help and coaching earlier on. 5️⃣ ”Presume deals won’t close and manage accordingly. Not only do deals fall through as a default, if you need the deal to close it impacts negotiations and actually makes it less likely to close.” I’m still learning this but a lot of sales is actually just relentless project management. You have to constantly push the next step, manage the urgency, otherwise opportunities die by inertia by default. 6️⃣ ”Company cultures are reflection of their founders. To change your company's culture, seek to change how you behave. To change your company's values, seek to change what you value.” It’s funny in hindsight that all of Inari’s mistakes came about due to my own weaknesses. Whenever I’ve been hesitant to learn sales or be cringe, growth stalls. If I’m ever lazy and stop managing the details, quality suffers. My results are always just a byproduct of the cumulative decisions and actions I’ve made.
Inari (YC S23) reposted this
Anthropic launched Claude 3.7 Sonnet, a new SotA hybrid instant AND reasoning model, but also unintentionally dropped the most important model benchmark that I’ve ever seen. AI Plays Pokemon. Anthropic equipped Claude with memory, vision, and function calling in order to press buttons and engage with Pokemon Red. With extended thinking and planning, Claude was at least able to beat Lieutenant Surge in Vermillion City. At least we know FOR SURE that we haven’t yet achieved AGI because apparently it took Claude 3.7 “tens of thousands” of interactions in order to reach and beat Lieutenant Surge. Even 6 year old me figured out that you can just brute force all of Pokemon Red by ONLY leveling up your Charizard or Blastoise, find an HM donkey, and repeatedly talk to each NPC 100 times in order to figure out what to do next to get through the game 🙄😂 Jokes aside - hitting 62%-70% on the SWE-bench looks incredibly impressive (vs 49% from o3-mini high) so I’m stoked to play around with Sonnet 3.7 for coding this week for Inari (YC S23). Congrats to Anthropic on a killer launch!
Inari (YC S23) reposted this
One of the single hardest tasks for product builders is deciding when to spend time prioritizing and building surprise and delight features. On one hand: teams are resource and focus constrained so it makes complete sense to deprioritize "nice to have" work and not bloat the core product experience. On the other hand: customers absolutely rave about tasteful and fun details included in products they never expected to exist. Many of the fastest growing and beloved products like Linear excel at those little things, and it sets them apart from bigger players shipping only essentials. If you kill the “delightful” or “nice to have” features in your backlog, your product probably won’t be “delightful” or “nice to have” either. Lmk what surprise and delight features you're inspired by! I'd love to steal them for Inari (YC S23) 😁
Inari (YC S23) reposted this
Inari (YC S23) is building a junior AI product manager. The canonical startup advice is “talk to your users” and “deeply understand customer problems,” but executing it is easier said than done. The volume of customer feedback and qualitative data is exploding, living fragmented across hundreds of tools, making it difficult and time-consuming to convert customer data into useful insights and product opportunities. Without a clear view of what customers are saying, product teams miss out on signals that can improve their product, reduce churn, and drive revenue growth. Missed signals mean your engineering team wastes time building features customers don’t actually need. Inari automatically surfaces actionable insights and product opportunities from your customer feedback, CRM, and backlog. This helps your team prioritize and build products that users love — without having to sort through 1,000’s of sales calls, support tickets, or feedbacks manually. Using Inari, you can explore accurate customer insights, unify your feedback and analyze its scale with fine-tuned AI models, sync and prioritize issues from Jira or Linear by auto-linking it with your feedback and CRM data, then prototype with AI easier by generating PRDs and prompts grounded in customer context. Congrats Frank Lee and Eric Kim on the launch! https://lnkd.in/gy2JeiqR
Inari (YC S23) reposted this
I really enjoyed this 3-min read from Matt Mochary, CEO coach for many top founders in Silicon Valley, consolidating his favorite takes on how to innovate while building products. Marty Cagan on new school method of building products: - Talk to new or potential customers - Identify top problems then focus on those with bad existing solutions - Prototype solutions for those problems and focus on getting feedback - Iterate until users love it then formally build and polish the new product Rahul Vora from Superhuman on honing in on signs of PMF: - Interview users to understand if they’d be “not”, “somewhat”, or “very” disappointed if they can no longer use the product and why - Segment users by those that would be “very disappointed” then dive into what they love about the product and feel is missing - Narrow the focus and be maniacal about improving the product based on feedback from specifically that "very disappointed" segment - Iterate and improve until you have high PMF in that segment Hot take from Wei Deng and Clipboard Health on restructuring for speed: - Create a new product team with a direct reporting line - Surprisingly, create a separate corporate entity that operates the new product team so they don’t deal with existing bureaucracy - Even more counterintuitively, create a second product team solving the same problem then reward both for their experimentation efforts - Productionize what bets work It’s cool to see how new tech is making these tactics more accessible for more teams: - There’s a ton of analytics and survey tools like Posthog, Mixpanel, and Sprig that make capturing feedback through email, surveys, in-app feedback, and other methods possible and simple. - LLMs and discovery tools like Inari (YC S23) make tasks like consolidating feedback, synthesizing insights, and segmenting users more accurate and less cumbersome than before. - Design tools like Figma or AI-coding agents like Replit and Bolt make generating usable prototypes for internal teammate or customer and customers much easier. More prototypes → more feedback -> faster innovation cycle. We just need some technology that gives humans more time in the day to experiment and build more 😅