Zelta AI (acquired by Pendo)

The team and I are ecstatic to announce that Zelta AI has been acquired by Pendo.io Two years ago, we started Zelta with the mission to help software companies understand their customers and build better products. In joining forces with Pendo, we’re accelerating this mission, and in our combined products, offering a truly one-stop shop for product builders. I am incredibly proud of what our team has accomplished in such a short space of time. I’m also grateful to our customers, investors, friends and family who supported us in building the best customer intelligence AI on the market. Building Zelta these past two years has been a whirlwind, an adventure and a privilege! Myself and the Zelta team will be joining Pendo as part of the acquisition. Todd Olson, Erik Troan, Rahul Jain, Trisha Price - we’re excited to get to work!

Today is a big day for Zelta because we’re joining the Pendo.io team ?? We built Zelta’s AI-powered customer intelligence platform because product teams were either drowning in data, or flying blind without it. Together, Pendo and Zelta can keep automating the manual tasks of product management and give companies the insights they need to build and launch successful products. We can’t wait to enter a new chapter of deeper customer intelligence and discovery. ??Learn more about the future of Zelta and Pendo:? https://lnkd.in/d9CB4MRt

We've seen a surge in demand for Zelta from companies deploying customer support AI with products like Intercom's Fin, and it's for an interesting reason... Customer support plays two crucial roles for software companies: ? Handling and resolving customer issues ? Providing insight to product teams about broken, confusing, or missing functionality Fin excels at resolving customer issues, but product teams can't hold monthly meetings with Fin or drop by its desk to understand where customers are struggling. This creates a problem. Without support teams acting as the bridge for customer struggles, product teams are left in the dark or forced to sift through closed tickets themselves. Zelta AI addresses this by ingesting conversations Fin has with customers and quantifies customer struggles for product teams With the power of AI, product teams effectively gain an army of analysts capable of instantly running complex queries and analyses across support data. For example, our customer have been running queries like: ? "Since we launched the new survey builder, has there been a reduction in support issues related to surveys? If so, by how much.." ? "What were the most frequent issues in the support channel last month? Provide a breakdown by product area, feature, and issue, including support conversations as backup for each." These are questions that would have been a burden on support teams to answer in the past and impossible to answer if there is no support team Interested to hear about others experience switching to Fin / customer support AI. Let me know in the comments ??

AI summaries of customer feedback are boring and useless The problem with AI summaries is… that they summarize. Averaging everything you’re summarizing into a boring grey goo For example, feeding Spotify app store reviews to GPT gives you something like: Spotify app reviews reveal a mix of praise and criticism. Positive Feedback: ? Music Discovery: Users appreciate Spotify's music discovery features and recommendation algorithms ? User Interface: The interface is praised for its ease of use, making it simple to find songs and create playlists Negative Feedback: ? Ads and Free Tier Limitations: Users on the free tier report frequent ads which can be frustrating? ? Subscription Issues: Some users experience difficulties managing subscriptions ? Content Recommendations: Some users feel the algorithm suggests unwanted genres This is boring/ useless… The purpose of a summary is to hit highlights, not to homogenize everything. A better version might look like: ?????????? ?????? ???????????? ???????????? ???????? ?????????????? ???????? ?????? ???????? ?????????? (???????????????? ???? ?????? ?????????? ??% ???? ??????????) "???????????????? ???? ????????????? ???? ?????????? ???????? ???????????? ?????????? ?? ?????? ??????????"? "?? ???????? ??????????????????, ????? ????? ???????? ????????????, ?????? ????? ???????? 4 ?????????? ???????? ???????? ?????? ???????? ???? ?? ??????’?? ????????????????? ????????????" "????? ?????????? ???? ???????????? ?????? ????. ?????????? ?????????? ?????? ??????????????" ???????????? ???????? ?????? ???????? ?????? ???? ?????????????? ???????????? ???????????? (???????????????? ???? ???? ?????????? ??.??% ???? ??????????) “?????? ?????????? ???????? ?? ???????? ???? ?????? ???? ?????????????????? ?????? ??’?? ???? ????? ???????????? ?????? ?????????? ???????? ???? ????? ???????????? ?? ??????? ???? ?????????????? ?????? ???????? ??????????? ??????’?? ??????? ??????????" “?? ??????? ????? ???????????????? ?????????? ???????? ???? ????? ???????? ?????????????????????? ?????????????? ?????????????? ?????? ?????? ????????” ?????????? ???? ?? ?????????? ???? ???????????? ???????? ?????????????? ???????? (?????? ??????????, ??% ???? ?????????? ???? ?????? ??????) "???????????????????? ?????????? ?????? ?????????????????????? ??????? ??????????????, ???????? ?????????? ??????? ??????? ???? ????????????????????."? "?????? ?????????? ???????????????????? ???? ?????????????? ???????? ???????? ??????? ?? ??????? ???????????????? ????????????????????." This is a sampling, not a summary of the most interesting specific comments made by users, including specific facts and statistics. Moreover it is actionable and gives a starting point as to where we might dig further. This is not something AI is good at out of the box. It requires the ability to quantify what is being reported by users, decide on the most important points and present an output that is suitable to the use case Others see the same?

How to increase user retention? Add “pain” to your onboarding flow… let me tell you why The most common problem our customers think they have is bad onboarding... as they see users sign up and then never truly activate Flow goes like this: ? User signs up? ? Clicks some buttons? ? Never comes back The typical response to this: “we need to improve onboarding”: ? Build guided onboarding process ? Add user to drip email campaign??? Here's the rub: the problem usually isn't onboarding… If users have a strong need they will endure lots of pain to get the job done and if they hit problems along the way they will let you know about it So what is the problem then? ? Attracting the wrong users (those who don't really have the need your product solves) ? Not communicating value to the right users In order for us to diagnose why users are dropping off, we need to first segment to those who truly have the need we are solving. The solution is to add some "pain" to your onboarding process... Users who are willing to endure some “pain” to use your product are communicating to you that they really have the need. Examples of positive onboarding “pain”: ? Sign up questionnaire? ? Upfront payment ? Book a demo ? Set up an integration to get started This friction serves too purposes, users who proceed through these steps: 1. ???????????? ???????? ?????? ????????: giving you a clean sample to judge your retention metrics 2. ???????? ???????? ???????? ???? ?????? ????????: making them more likely to stick around For any onboarding friction lovers out there, what have you found works? Let me know in the comments

Delighted to share this research paper, Zelta has collaborated on with Martian, G2, Copy.ai, 6sense, Supernormal and other AI leaders For anyone hoping to get into the weeds on automated prompt optimization and techniques to get more from LLMs, this is a must read

At Martian, we are fortunate to work with many of the world's most advanced users of AI. We see the problems they face on the leading edge of AI and collaborate closely with them to overcome these challenges. In this first of a three-part series, we share a view into the future of prompt engineering we refer to as Automated Prompt Optimization (APO). In this article we summarize the challenges faced by leading AI companies including Mercor, G2, Copy.ai, Autobound, 6sense, Zelta AI, EDITED, Supernormal, and others. We identify key issues like model variability, drift, and “secret prompt handshakes”. We reveal innovative techniques used to address these challenges, including LLM observers, prompt co-pilots, and human-in-the-loop feedback systems to refine prompts. We invite the broader AI community to collaborate with us on research in this area. If you are interested in participating, please reach out to us! https://lnkd.in/eyvfEe2X #AI #ArtificialIntelligence #PromptEngineering #APO #MachineLearning #AIResearch #Collaboration #Innovation #ModelVariability #ModelDrift #LLM #FutureOfAI #AICommunity #HumanInTheLoop #AIChallenges #AIsolutions

At Martian, we are fortunate to work with many of the world's most advanced users of AI. We see the problems they face on the leading edge of AI and collaborate closely with them to overcome these challenges. In this first of a three-part series, we share a view into the future of prompt engineering we refer to as Automated Prompt Optimization (APO). In this article we summarize the challenges faced by leading AI companies including Mercor, G2, Copy.ai, Autobound, 6sense, Zelta AI, EDITED, Supernormal, and others. We identify key issues like model variability, drift, and “secret prompt handshakes”. We reveal innovative techniques used to address these challenges, including LLM observers, prompt co-pilots, and human-in-the-loop feedback systems to refine prompts. We invite the broader AI community to collaborate with us on research in this area. If you are interested in participating, please reach out to us! https://lnkd.in/eyvfEe2X #AI #ArtificialIntelligence #PromptEngineering #APO #MachineLearning #AIResearch #Collaboration #Innovation #ModelVariability #ModelDrift #LLM #FutureOfAI #AICommunity #HumanInTheLoop #AIChallenges #AIsolutions

$10 billion business idea below ?? Software companies track everything users do in their products with analytics tools like Mixpanel, Amplitude, Heap, Pendo etc But “what people are doing” is just one side of the coin, we also care about “what people are telling us” .. the qualitative insight We have the data; recorded sales & success calls, interviews, surveys, emails, community forums and social media… oceans of it… growing every day Yet, most orgs utilize ~1% of it Everyday customers/prospects communicate: ? Their needs and goals to Sales ? Why they use/ or don't use your product to Customer Success ? Bugs, questions and feature requests to Customer Support ? Their pain points to User Research ? What they love or hate about your product on review sites ? Asks for help on community forums So much of what we do stems from this customer input, yet most orgs rely on periodic team meetings to share anecdotes of what they’ve heard A company should build an AI for this, one that listens to every Gong call, Zendesk Ticket, survey response, G2 review etc…maps it all to CRM and product analytics data and uses AI make sense of it all The AI would: ? Stack rank customer problems and their associated ARR ? Triage product issues to appropriate person automatically ? Automatically “close the loop” with customers/ sales when enhancements are shipped ? Draft segment specific marketing?content ? Tell us who our ICP is Call it something like "Customer Intelligence"… this would be a game changer for the industry… Anyone building this? ??

Thanks Melissa Perri! Product Ops teams are quickly becoming a key leader and thought partner in the AI strategies of our customers. Such an exciting time!