Everything AI
Product opportunities, validation, and your agents, connected.
Everything by Bagel AI is an autonomous decision loop. It reads your customer signal, surfaces the opportunities worth building, validates every bet, and feeds each decision to your team and your agents, then keeps running as new signal lands.
Building got cheap.
Deciding didn’t.
Agents ship fast. They don’t know what to build.
An agent can build a feature in a day. It can’t tell you whether a customer needed it. As building gets cheaper, the costly mistake moves upstream: spending a week of agent cycles on the wrong thing.
Everything AI shows you what to build, and proves it.
It reads your customer signal, surfaces the opportunities worth building, and validates each one against sourced evidence, then hands the decision to your team and your agents. The same speed, aimed at the right target.
What’s inside Everything AI
AI Product Opportunities
The bets you’d never find, surfaced from your customer signal, quantified and prioritized before anyone files a request.
Discovery OS
Bring any hypothesis and get it back validated in seconds, with the evidence and business impact already attached.
Bagel MCP
Every decision, exposed to your AI stack. Claude, Cursor, Codex, and any MCP client build against the same evidence your team uses.
Everything AI, in two minutes.
Watch the whole loop run: Bagel surfaces an opportunity from real customer signal, validates it, and serves the decision to the tools already doing the building.
Run the product decision loop, autonomously.
Bagel’s Everything AI ingests customer signal from 100+ sources and resolves it into one entity model, then runs the loop on top: prioritized opportunities, hypotheses validated against sourced evidence, and every decision exposed to your agents over MCP. Your team works in the UI. Claude, Cursor, and Codex query the same layer through the protocol.
Some good words from our customers
“Bagel AI helps us uncover blind spots bringing evidence from different channels (sales calls, support teams etc.) making our product decisions sharper and more aligned with growthaligned with growth.”
“Bagel AI has been a crucial tool for us in turning GTM team feedbackturning GTM team feedback into quantifiable insights.} It provides clear evidence to justify investment in new features, ensuring we make informed, high-impact product decisions.”
“Bagel AI is unmatched in the industry! The bespoke model built for HoneyBook helped us prioritize the most impactful featuresprioritize the most impactful features cutting through the noise of our large user base. Highly recommended!”
“Completes the long-time missing part between revenue, product and the customer. It is a product market fit pulseproduct market fit pulse on autopilot”
“Every SaaS company hits a point where feedback piles up but decisions stall. Bagel AI turns that mess into clear, revenue-focusedclear, revenue-focused priorities.”
See Bagel AI In Action!
Pick a time. We’ll show you what the autonomous decision layer looks like with your data behind it.
Enterprise grade security. Built for scale.
Bagel AI ensures top-tier security and compliance, protecting your feedback, roadmaps, and outcomes while seamlessly integrating with your tools. Focus on impact with confidence.
FAQ- Everything AI
Everything AI is Bagel’s autonomous product decision loop. It reads your customer signal, surfaces the opportunities worth building, validates each one against sourced evidence, and exposes every decision to your team and your AI agents. Three capabilities run on one engine: AI Product Opportunities, Discovery OS, and the Bagel MCP.
You can start with one. Most teams begin with AI Product Opportunities or Discovery OS, then add the MCP as their agent workflows mature. Because the three share the same engine, each one you turn on makes the others sharper.
A feedback tool stores what customers said. Everything AI decides what to do about it. It quantifies each opportunity by reach and revenue, validates it against real evidence, and hands your team a prioritized call instead of a tagged backlog to sort through.
Gong, Salesforce, Zendesk, Slack, Jira, Linear, your product analytics, review sites, and 100+ more. Every signal resolves to a single entity, so one question returns the whole picture without double-counting.
Through the Bagel MCP. Every decision Everything AI produces is exposed over the protocol, so Claude, Cursor, Codex, and any MCP client query the same evidence your team works from. Setup takes minutes and needs no custom integration.
Product leaders who own the roadmap, and the AI and engineering teams building against it. Product teams use the UI to decide what to build. Technical teams wire the same decision layer into their agents through MCP.
Yes. Bagel meets enterprise security and compliance standards and integrates with your existing tools without exposing raw data.
Book a demo and we’ll show you all three capabilities running on signal like yours. Most of the setup is connecting your sources. There’s no manual tagging to maintain.
