AI

AI Assist – Not another chatbot, SDKs to build AI-powered assistance

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AI Assist – Not another chatbot, SDKs to build AI-powered assistance

Hey everyone! We just shipped AI Assist — SDKs to build AI-powered assistance into your product. AI-Assist captures multi-modal in product context, and gives you drop-in React components and flexible SDKs that make it easy to ship personalized and contextual assistance that unblocks your users. We built AI assist because the current state of the art for AI-powered in-product assistance is the chatbot, and there’s an opportunity to do far better. Chatbots are unhelpful because: 1. prompting is imprecise and very chaotic in the sense that small changes in prompts cause a huge change in answers 2. the user (the question-asker) is burdened with solving the problem of identifying and producing all relevant context for their question 3. questions and answers are not integrated into the app itself but instead surfaced indirectly in separate and often disruptive UIs We discovered that you can solve 1) and 2) by automatically capturing multi-modal in-product context for the user, and 3) by providing flexible SDKs and drop-in React components that make it easy to build assistance directly into your product’s experience. Here’s how AI-Assist works: 1. crawls and indexes your documentation and other static product context 2. retrieves relevant sources based on users’ dynamic in-app contexts (e.g. annotated screenshots, semantic HTML, runtime information, and user and company attributes). 3. uses sources as well as users’ context as grounding for a multimodal LLM to generate meaningful responses to augment in-app experiences With AI Assist, you can easily build: An explain anything mode — Let users point at anything in your product to get relevant help Proactive error assistance — Surface extremely contextual guidance any time a user encounters an error Contextual help — Surface relevant docs based on where the user is and what they’re doing. Dopt’s platform powers self-serve product experiences for companies like Superhuman, Reforge, and Productboard. We’ve seen folks get up and running in <20 minutes. - You can play with a live example of AI assist (including code) on our website at https://www.dopt.com/examples/ai-assistant - Sign up for free at https://www.dopt.com/ai - And read more about how we built it at https://blog.dopt.com/more-than-just-chatbots We’d love your feedback if you give it a try!

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, context, code · Missing: mac, agents, macos
96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: para, including · Missing: supports, reddit linkedin, podcasting
92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: personal, answers, users · Missing: mobile apps, ios, entrepreneurs
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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