Monterey AI

Monterey AI

Software Development

San Francisco, California 2,887 followers

Copilot for product insights.

About us

Copilot for product insights.

Industry
Software Development
Company size
2-10 employees
Headquarters
San Francisco, California
Type
Privately Held

Products

Locations

Employees at Monterey AI

Updates

  • Monterey AI reposted this

    View profile for Kyle Thomas, graphic

    I Help Ambitious Startup Job Seekers Land Career-Accelerating Dream Roles at World-Changing Startups | "De-Risk" the Search w/ Proven Methods & Investor-Grade Data | Apply to our Startup Job Search Accelerator Below

    Here's a new list of 38 startups founded by former Uber employees. All are HIRING: 1) Metaview - (London, NYC) 2) Mnemonic, Inc - (Remote US) 3) Monterey AI - (Remote US) 4) Motif - (Mountain View) 5) Motto - (Paris) 6) Natter - (London) 7) Naya Homes - (Mexico City) 8) Nightfall AI - (Remote US/CAN, San Francisco) 9) Onehouse - (Sunnyvale, Bangalore, Remote US) 10) OnLoop - (Singapore) 11) Opsera - (San Jose, Remote US) 12) Outgo - (Seattle) 13) Pagaleve - (Sao Paulo) 14) Palenca - (Mexico City, Sao Paulo) 15) Palm - (Remote SWE/NLD) 16) October Health - (Johannesburg) 17) Parallel Fluidics - (Boston) 18) Partment - (Cairo) 19) PermitFlow - (New York, Remote US/CAN) 20) Plato Systems - (San Francisco, Remote) 21) Powerdot - (Lisbon, Paris, Brussels, Warsaw) 22) Prive - (San Francisco, Remote) 23) Probo - (Gurugram) 24) Railway - (Remote) 25) RedCircle - (Boston, Los Angeles) 26) Replo - (San Francisco) 27) Sway (formerly Returnmates) - (San Francisco, Los Angeles, San Diego, Austin, Miami, Dallas, Houston, Salt Lake City) 28) Sardine - (Remote US/CAN/UK/BRA/ARG) 29) Scaler - (Amsterdam, Sydney, London) 30) SFR3 Fund - (Georgia, Missouri, Remote) 31) Shaped - (New York) 32) SHARELOCK - (Paris) 33) SINAI Technologies - (Remote US/BRA) 34) Solo - (Seattle) 35) Stably AI - (Remote US/CAN) 36) StarTree - (Mountain View, India, Remote US) 37) Subject - (Chicago, Beverly Hills) 38) Synctera - (San Francisco, Remote US/CAN) Big shout out to Allison Barr Allen and Trail Run Capital. She put together an extensive list of ex-Uber founders. Go get that job and build something awesome 💪 --------------------------- For more lists, follow me, Kyle Thomas. Want personalized lists of startups for your job search? Use the link in my profile to apply to our Startup Job Search Accelerator. ♻ Share this to help startup job seekers find a new role.

  • Monterey AI reposted this

    View profile for Drew Breunig, graphic

    Vice President Strategy @ Precisely

    I've started sorting #AI applications into just 3 buckets: gods, interns, and cogs. "Gods" are the super-intelligent, artificial entities that do things autonomously. Science fiction at the moment, but so much work, money, and hype is being put towards making them real. "Interns" are the #copilots. Their defining quality is that they are used and supervised by experts. They do grunt work and can make mistakes – because the experts can fix them. GitHub Copilot is an intern for programmers. Adobe Firefly and Visual Electric are interns for designers. Grammarly is an intern for writers. Monterey AI is an intern for product managers. "Cogs" are comparable to functions. They're designed to do one task, unsupervised, very well. They are components inside larger pipelines or applications, running alongside bog-standard functions or frameworks. Cloud platforms – like Databricks, Snowflake, Microsoft Azure, Amazon Web Services (AWS), and others – have sprinted towards delivering tools and hardware for building, testing, and running cogs. They are the cog foundries. Sorting the ocean of AI use cases into the God, Intern, and Cog buckets has helped me tremendously. It’s easier to navigate the noise, ask questions about new products, and identify the bottlenecks holding back each category. (And I lied, there's a fourth use case: Toys. But you can read more about that below...)

  • Monterey AI reposted this

    View profile for Drew Breunig, graphic

    Vice President Strategy @ Precisely

    Monterey AI’s weekly summary for your app’s consumer feedback is secretly the best example of AI being used to deliver a dashboard with incredible signal-to-noise. It’s proof LLMs can deliver insights you can actually act on, at scale.

    View profile for Chun Jiang, graphic

    Copilot for product insights

    😍 a lowkey flex on our email open rate. we deliver high quality emails when there are product updates, new insights, anomaly, and weekly summary. great job team Jacob Hubbard Monterey AI

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  • 😎 New integration alert: Chorus by ZoomInfo Chorus serves thousands of customers as a leading system of record for prospect calls, customer conversations, and internal communication voices. We're thrilled to integrate Chorus calls, driving product insights from this source of valuable data. Thanks to the Chorus team for their excellent support!

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  • Merci pour le shoutout ! Nous sommes ravis de façonner le paysage des outils d'insights natifs IA de nouvelle génération. 😍

    View profile for Maxime Dupuy, graphic

    Unlock customer knowledge to enhance experience - Cofounder @Blumana

    Quelques outils IA "nouvelle génération" qui peuvent vous aider à mieux apprendre de vos interactions clients : Blumana (FR) : analyse de tous types d'interactions écrites ou orales pour maximiser la transformation et la satisfaction client Monterey AI (US) : analyse des feedbacks pour les équipes product Kraftful (US) : analyse des feedbacks pour les équipes product Glanceable (FR) : analyse des feedbacks orientée satisfaction client Deep Talk (FR) : analyse des feedbacks orientée satisfaction client Screeb (FR) : collecte et analyse des feedbacks orientée satisfaction client Gravite.io (FR) : analyse des conversations Attention (US) : insights pour les sales Modjo (FR) : insights pour les sales Actionable (FR) : analyse des interactions pour prédire la satisfaction InMoment (US) : analyse des feedbacks orientée CX

  • Monterey AI reposted this

    View profile for Claudia Cafeo 🏳️‍🌈, graphic

    Community Specialist @ Zapier | Founder of Floxies Community | Building D.A.M.N. awesome Discord communities

    Hey LinkedIn, you get me twice today! 😏 I just realised it's -10 until Zapier ZapConnect, so I collected a few talks you won't want to miss 🔥 ⬇️ 🎙️ Keynote: 𝐀𝐈 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐖𝐨𝐫𝐤 - "Become unstoppable with Automation for everyone" presented by: ⚡️ Dharmesh Shah Co-Founder and CTO at HubSpot and Wade Foster, Co-Founder and CEO at Zapier 🎙️"Too many cooks? Finding the right recipe for citizen development" by: ⚡️ Benjamin Bailey, Sr. Research Systems Manager at Frame.io 🎙️ "Ways to uplevel your workflows with Zapier and ChatGPT" by: ⚡️ Jan Beke, GTM Director at OpenAI 🎙️ "Proactive Revenue Operations: 5 steps to turn chaos into impact" by: ⚡️ Christopher Stuart Smith, Automation Specialist at Flow Digital 🎙️ "Automate your SaaS onboarding with Zapier and AI" by: ⚡️ Angela Ferrante, CEO & Founder at Laudable 🎙️ "Streamlining HR: Elevate your hiring process with automation" by: ⚡️Mohamed Swellam, Founder at GeekyAir These are just a few of the awesome, awesome talks you'll get to learn from on the day - I feel like I'm upscaling just by reading these titles 🧠 ✨ We will be topping it all off with a powerful AI Panel on "How AI automation is transforming industries", featuring special contributions from Ankur Goyal (Braintrust), Arvind Jain (Glean), Chun Jiang (Monterey AI) and last but not least Reid Robinson (Zapier). I don't know about you, but I'd grab a ticket. Right. Now.! 🔥 ⬇️ https://lnkd.in/dCYAjqYd

    Register for ZapConnect 2024

    Register for ZapConnect 2024

    zapier.com

  • Voices of Customers Voices of Customers Voices of Customers 🖤 We are here to help you scale in a fast, accurate, and fun way!

    View profile for Kayla Eliaza, graphic

    Translating user needs for engineering minds

    Recently I’ve had the joy of nerding out in several conversations about Voice of the Customer.  Shockingly, listening to our customers en masse is still an emerging field, despite it’s potential to shape better products and services.  In these recent conversations I’ve found myself describing my approach to VoC work as four sequential components: collecting data, analyzing data, doing something with your data, and closing the loop.  There’s so much to dive into in each step, so get ready for a series! Part 1: Collecting data Every interaction with a user is a feature request.  Sometimes these are obvious (“I want you to build X, Y, and Z”), other times, they’re less explicit (“Where’s the button for that?” “How did this person edit my page?” “I want my money back”), but every question and every request is feedback on the products we have built, and the policies and pricing packages we’re implementing around them.  When we think of every interaction as a feature request, we are met with a plethora of sources.  Every tweet, app store review, sales inquiry, Reddit post, question to a CSM, question to a chatbot, and (my bread and butter) every support ticket, is a sneak peak into what customers want when they use our products.  So… what do we do with it all? First, take a breath.  If you work with customers, start with just your team; if you don’t work directly with customers, pick 1-2 teams to work with. In either case, begin to collect data in a way that you will be able to analyze.  As you collect data, try to do so in a single place that can be shared across teams.  This may begin as a Notion database, Google sheet, or Asana project, and may scale to a tool like Enterpret or Monterey AI.  Keeping everything in a centralized place will help avoid the need to switch tools later (spoiler: you’re probably going to switch tools later).   Tag your insights in a way that would make sense to your customers.  This way, tags will also make sense to your customer-facing teams, including vendors (you’ll be able to translate for engineers and PMs later).  Take early steps to make sure that the same tags, or categories of tags (likely features), are being used across sources.  Automate where you can.  The more automated the process, the more likely it will be to get done (especially when your teams are busy).  If you are using macros for your email or chat support, you can likely automate the application of tags, just set the expectation with your agents that they double check, especially if you have a complex product. What key steps and tricks have you found most helpful in collecting user feedback?  Comment to let me know! Next step: Understanding the quality of your feedback. #VoiceOfTheCustomer #VoC #CustomerFeedback #CustomerExperience #CustomerInsights #DataDriven #QuantitativeData #QualitativeData #CustomerSuccess #UserFeedback

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