And when this context is scattered across an organization, teams struggle to:
Prototypr.ai is an AI platform designed around four pillars of the product development lifecycle: Research, Build, Measure, and Learn. Explore ideas, create prototypes, measure performance, and curate learnings into AI knowledge base that your team and agents can reuse.
Save the problems you researched, the ideas you tested, the results you measured, and the decisions your team made so people and agents can build on what you’ve learned.
Connect Agents to Memory API →Connect the data sources your team already trusts, then use AI to ask better questions, generate dashboards, and understand what changed across your business.
MCP
Google Analytics
BigQuery
Search Console
SendGrid
WordPress
Stripe
Scheduler
After connecting Google Analytics via MCP, prototypr.ai creator Gareth Cull collaborated with the AI Growth Advisor to analyze onboarding drop‑offs, identify activation friction, and redesign the experience — increasing Day 1 retention by 53%, from 12.18% to 18.69%.
“When your AI understands your product data, growth decisions become clearer, faster, and compounding.”
Build, measure and learn faster with frontier models such as Google Gemini 3.5 Flash, Claude Opus 4.8 and GPT-5.5.
Start building with AI todayUse prototypr.ai to explore ideas, create prototypes, measure performance, and curate learnings into AI memory so the system gets smarter with every cycle.
Collaborate with the world's best AI models to explore ideas with AI Chat or Search, generate product surveys and experiment plans that help shape hypotheses before you build.
Generate landing pages, wireframes, dashboards, and user experiences with AI so your team can move from prototype to impact faster.
Scroll through high-converting landing pages, dashboards, and responsive user experiences designed within prototypr.ai Studio. Accelerate your design workflow with high quality HTML templates from our community marketplace.
VIEW HTML TEMPLATES IN MARKETPLACEBring analytics, search, email, revenue, and lifecycle reports into the workspace so your team can see how prototypes, pages, and product changes perform.
Save experiment outcomes, product decisions, analytics insights, user feedback, and AI conversations into a searchable knowledge base that helps future decisions start with context.
Scale your design workflow, connect product performance metrics, and build a unified product knowledge base that compounds over time.
AI Memory API DOCUMENTATION
import requests
import json
import os
url = "https://www.prototypr.ai/api/v1/agents/memory"
payload = {
"user_id": "your_user_id",
"workspace_id": "your_workspace_id",
}
headers = {
"Authorization": "Bearer " + os.getenv("API_KEY"),
"Content-Type": "application/json",
"Accept": "application/json"
}
response = requests.post(url, data=json.dumps(payload), headers=headers)
print(response.json())Learn how teams can use prototypr.ai to research ideas, build prototypes, measure performance, and turn learnings into AI memory.
A demo video that showcases how to use AI Search from within the prototypr.ai Workspace.
Learn how to design and edit experiment plans using AI.
Learn how to generate a landing page with AI from your mobile device using prototypr.ai.
See how prototypr.ai uses production-ready design systems and design tokens to generate functional UI prototypes.
Learn how to generate custom Google Analytics dashboards in Dashboard AI.
Connect the GA4 API to AI and ask questions about your Google Analytics data in natural language.
Learn how to use MCP to chat with your Google Search Console data inside prototypr.ai.
See how an AI Growth Advisor can help analyze product data and support better product and growth decisions.
A transparent and user-controlled way for your AI Growth Advisor to reason across analytics data, UX analysis, experiments, user feedback and conversations over time.
Read tactical lessons on data analysis, conversion design, and product validation directly from our building team.
Let's build the foundation of your AI knowledge base around five core inputs: conversations, analytics, experiments, user experiences, and user feedback.
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Get started with building a simple MCP server that can act as a bridge between systems. Create a pure python MCP server built on top of Flask and learn how you can modify it for your own purposes. Code included.
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Go beyond prompt engineering and discover how the FACT framework helps you structure data context for smarter, more capable data agents. With practical examples and code.
Read articlePartner directly with the creator of prototypr.ai, Gareth Cull, to explore how you can accelerate building a test and learn culture at your company and start validating your ideas faster with frontier AI models.
Everything you need to know about getting started with prototypr.ai.
Prototypr.ai is an AI product development platform that helps teams move from idea to validation faster by bringing prototyping, research, analytics, and decision-making into one workspace.
Its workspace is inspired by the product development lifecycle and built around four key pillars: Research, Build, Measure, and Learn. Teams can explore problems and ideas with frontier AI models, generate user experiences, connect data sources such as Google Analytics to measure performance, and curate learnings into a growing AI knowledge base that people and agents can reuse to support future decision-making.
Prototypr.ai is designed for product managers, founders, designers, growth teams, and operators who need a more connected way to move from idea to validation across research, prototyping, analytics, and decision-making.
It is especially useful for teams that already think in terms of assumptions, hypotheses, experiments, and measurable outcomes. Prototypr.ai helps these teams prototype ideas, measure how users respond, document what they learned, and preserve decision context so future product and growth decisions can build on what has already been tested.
Most AI design and analytics tools focus on one part of the product development workflow, such as generating user interfaces, creating dashboards, or analyzing performance data. Prototypr.ai takes a more connected approach by bringing research, prototyping, measurement, and learning into one AI workspace.
In practice, this means teams can use prototypr.ai to explore problems, build testable prototypes, connect to Google Analytics and measure how users respond, and save what you learn into a searchable AI knowledge base that supports future product and growth decisions.
The AI Knowledge Base is what makes prototypr.ai especially differentiated. As teams curate more context, including analytics reports, saved conversations, hypotheses, user feedback, experiments, and decisions, AI models can reason with a deeper understanding of what the team has already tried, what changed, and offer up better suggestions around what may be worth doing next. The knowledge base can also be accessed through the prototypr.ai API, making it possible to bring reusable product context into other AI tools, agents, and workflows outside of prototypr.ai.
An AI knowledge base is a structured collection of information that helps AI understand context, answer questions, and make more grounded recommendations. Instead of retrieving isolated facts, it helps AI reason across connected context, similar to how retrieval-augmented generation uses relevant knowledge to produce better responses.
In prototypr.ai, your AI Knowledge Base helps the AI models reason across analytics reports, saved conversations, hypotheses, user experience context, and customer feedback. As you add more context, it can remember what you have tried, connect new signals to past decisions, and help suggest clearer next steps over time.
If you want to read more about how to set up your knowledge base inside prototypr.ai, please check out this article: Building an AI Knowledge Base - A practical guide.
There are a few different ways to connect your data to prototypr.ai:
1. Connect to data sources using MCP: prototypr.ai has designed a number of different open source MPC servers to help companies connect their data. These data sources include: Stripe, Google Analytics, Google Search Console and more. For a complete list, please visit our MCP Data connectors page to learn more.
2. Connect Google Analytics to AI using the GA4 API: Authenticate with the Google Analytics API, and chat with your GA4 data or start generating GA4 dashboards with AI. For step by step instructions for how to connect to Google Analytics, please check out this tutorial: How to Connect prototypr.ai to Google Analytics.