Today, we’re announcing that Airtable has acquired @doptcom to bring additional expertise in AI-driven, no-code user activation and product growth! 🤖
🔗 https://t.co/x6okaP8Rbb
Messaging users in-app can be fraught — it's all too easy to spam users with irrelevant messages. We want to change that.
We're working on a new feature we're calling channels.
Channels will enable you to build in-app messaging channels directly into your product once with our SDK. Then, anyone on your team (PMs, Marketers, Growth) can launch messages through it without a developer.
Controls like targeting, priority, and timing will ensure users get a quality experience.
And like the rest of Dopt, you can use your own components and build the channel seamlessly into your product's experience.
With channels, you could build reusable modals, home page banners, carousels, and embedded cards to reach users with announcements, alerts, and relevant nudges.
We're super pumped about this next step in our platform! If you're interested in trying it out, sign up for early access — link in the comments.
Join me tomorrow for the Ultimate Product-led Onboarding Playbook live event with Reforge!
I'll be sharing the playbook from my time working with 100s of companies while at Dopt and leading Growth Design at Dropbox.
Almost 3,000 people have registered already. Sign up link ↓
“You can move faster by 1) deciding to move faster and 2) using completely modern infrastructure” —@gabor at the Product Hunt meetup in Oakland yesterday
So awesome to see y’all! @chunonline@_heyglassy@chrismessina Thanks for hosting @philvb/@doptcom
Oakland / Berkeley builders!
Thursday evening @doptcom is hosting a Product Hunt meetup, and I want to see you there!
The Product Hunt events have been unique and electric, and I've been absolutely stoked on the energy. Can the East Bay measure up to SF proper? Let's see!
https://t.co/eLwckqZbPJ
⚡️ Announcing our new AI-powered, in-app help hub component! ⚡️
Build on-demand help and AI answers directly into your product to help users find resources and unblock themselves.
I love this conversation with @srir (Founding EM), and @owenauch (Billing Tech Lead) from @useOrb about why they decided to use @doptcom for product onboarding, education, and in-app messaging vs building in-house ✨
🎧 Check out an interview with Phil, our CPO, and Karthik, our lead engineer, about how we built AI Assist on the Building with AI podcast.
In the interview, they dig deep into:
🔹 How the product and design thinking helped us create "promptless" in-app AI assistance, moving away from traditional chatbot experiences
🔹 Leveraging in-product contexts, such as UI screenshots, to deliver highly relevant and context-aware support
🔹 Technical details about the opportunities and challenges of building multi-modals
🔹 How product, design, and engineering worked together to deliver AI Assist and how that's different than building non-AI features
Links to the full interview below!
Check out our latest example: AI assistance built directly into users' workflows to help them understand & resolve errors they encounter.
You can play with it on our examples page https://t.co/PSbYPlAqW3
Check out our latest interactive example: AI Assist powering an "Explain anything" mode. You can now ask follow up questions too!
https://t.co/TZ2QW8TbcO
Latest customer using Dopt! Here's ZenML's onboarding hub built with Dopt.
A few reasons I like this:
🔹 This pattern makes sense for ZenML: onboarding hubs are most successful in products that have longer setup experiences where users need more assistance, like dev tools.
🔹 The checklists give structure to the journey toward aha moment and the support, invite, and resource entry points help users get unstuck.
🔹 There are two separate checklists in the hub: a "Starter Setup" and "Production Setup". The truth is onboarding is more like a set of aha moments where the user realizes deeper levels of value. I like how these checklists work together to get a user all the way to production. We've considered something like this at Dopt -- it's cool to see our customers go live with it!
🔹 The hub may look like a lot, but it's all tucked into an entry point in the navigation. The user can use the rest of the app and easily return here when needed.
I wrote a full breakdown of the pattern on our blog, link in the comments!
Awesome to see @reforge's Dopt-powered announcement carousel live 🎉
In less than a day, they built an announcement surface area that non-devs can self-serve push targeted announcements to (and there are some exciting announcements coming to it soon 😀)
When building AI features you never have full confidence “it will work”. There’s an inherent risk:
You can't know if an AI result is good without building the feature to see the result.
But you don't want to build the feature only to discover the AI response isn't good.
So how do you determine if it's worth building?
Yesterday we launched AI Assist to the world. Over the past few months, we went from low-confidence early AI ideas to launching AI assist, our new tools to build AI-powered in-product assistance into your product.
Building AI Assist was unlike any other feature I've built in my career. It felt much riskier than non-AI features.
By navigating the challenges of building AI assist, we learned 3 product lessons for mitigating risk when building AI features:
Lesson 1️⃣ : It's too easy to dream. Get grounded with technical research.
Make time for designers and developers to do research to learn what’s possible. It may end up being a sunk cost (you’ll have to be comfortable with that risk), but research is the best input to creating AI solutions that are grounded in reality.
Lesson 2️⃣: Quantify risk to select opportunities
Unlike the traditional value vs effort prioritization of non-AI product work, AI requires a sense of the probability of success across a set of bets (kind of like a Growth team!). Quantifying the risk helps navigate where to invest time.
Lesson 3️⃣: Building is the only way to know what to build
When building non-AI products, you have high certainty a feature will work after discovery and definition. Not so with AI features. With AI features, you must iteratively build to increase the probability of success and continue or decide to cut your losses and move on.
I wrote a case study about building AI Assist and dug deeper into these lessons on our blog, link in the comments!
In just a few minutes we were able to integrate @doptcom's new AI assist feature and provide explanations for every feature in @AutoblocksAI to our users. Basically magic.
🚀 Announcing AI Assist! 🚀
Unblock your users and help them succeed with remarkably relevant in-product assistance.
We're live on Product Hunt and would love your support! Link ⬇️