Bagel AI

POV: You're a Product Manager ☕️

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@gnawingdeath We never asked 🤷🏻‍♂️

@trybagel I don’t give a fuck

Say hello to Everything by Bagel AI. Sure, AI can do plenty. But point it at your raw customer data and it burns a fortune to give you answers you can't trust. Regular AI isn't enough here. Wrong tool, and a pricey one. Everything AI is one decision loop: higher accuracy, more than 90% lower token cost, every source in one place. Then it feeds the decision straight to Claude Code, Cursor, or Codex. You build the right thing. Every time. Go bagel everything → bagel.ai/platform/every…

@TheRealidan YOU DID WHAT?!

Karri, you told everyone not to do this. I did it anyway. In your honor: bagel.ai/compare/bagel-…

I started my career building internal enterprise systems at Amdocs. HR tools, custom workflows, the whole thing. We built everything ourselves because that's what you did. Twenty years later, I'm watching product teams fall into the exact same trap. Except now it takes an hour instead of a quarter, so it feels like progress. I sat down with the Product-Led Alliance to talk about what I'm seeing across the market. The short version: every team is shipping 10x faster, and most of them have no idea if they're shipping the right things. The new metric isn't impact. It's time to impact. Same ambition, compressed timeline, much higher stakes on every decision. We talked about context debt, why domain expertise is the PM's only real moat, and why "we fall in love" with the things we build internally even when they stop serving us. Watch the full session: bagel.ai/blog/the-conte… @Nasi_R @trybagel

You are not ready...

Are you ready for what's next?

You don't need something. You need EVERYTHING. 🥯 Soon.

Everything is coming...Soon

Let's say your'e a product manager, and a really good one. And let's say that in the company you work for, there are other product managers who are also really, really good. Because your'e a really, really, really good product manager, you want you and your team to understand how to make a significant impact on our product. So, you take Zapier, connect Salesforce, Gong, the tickets from Zendesk, and you even ask to connect to Snowflake because you go above and beyond and you have a Slack channel connected to all this stuff too and all of this feeds straight into the company's sweet little Claude, and you start asking it a bunch of questions, and you get answers. Even good answers. Well, you should know that according to a study by Anthropic (not mine, and not Bagel AI's, but it totally validates us big time), Claude was right / accurate only 21% of the time. So they tried again and fed it thousands of past queries, made sure it read them, and the accuracy moved by less than 1%. In 80% of the cases, the answer was sitting right in front of its eyes, and Claude still missed it. Their conclusion: the bottleneck wasn't access to the information, but its structure. The moment you add a managed layer between the question and the data, accuracy jumps to 95%, and in some places almost to 99%. Long story short, the model isn't the problem. The problem is the pile of raw data. Especially for us, with piles of data and feedback, it's not enough to just connect Zapier to Salesforce and Gong to Claude and ask a question. You actually need someone to do the laundry: sort the colors from the whites, wash it, fold it, and put it in the closet exactly where it belongs, so you aren't chasing features nobody needs. This is exactly where you need Bagel AI to do this laundry for you, but let's move on :) Anthropic are nice like that and offers a few ways to handle these failures: 1. Building Data foundations: Boiling it down to a single canonical dataset (a "single source of truth"), aggressively deleting duplicates, and storing the data code together with the metadata in one place to prevent downstream breakages. 2. Managing sources of truth under human control: Using a semantic layer for uniform metric definitions. They even emphasize: let Claude write the documentation, but leave the ownership of the definition in human hands to prevent mistakes (fully automated generation was found to be detrimental). 3. Adding Skills is a game-changer: Because they encode the agent's workflows and spike the accuracy level from 21% to 95% and up! It's critical to update them alongside the models to maintain accuracy over time. 4. Combining regular Offline evals with Adversarial review: An audit layer that challenges the AI's assumptions before the answer goes out. At Anthropic, this increased accuracy by 6%, but cost 32% more tokens and 72% higher latency. Meaning, accuracy costs money, and you have to decide what each percentage point is worth to you. More here: bagel.ai/blog/21-accura…

9. "The proof loop only works if a person reads it." Automating the evidence is easy. Owning the result is the part that stays human. Decide what you delegate to AI, what you keep, and how a decision the machine made gets reviewed before it ships. —— P.S. We built Bagel to finish the product decision work before your team walks in Monday: signal triaged, opportunities validated, dev-ready artifacts a single MCP call away: bagel.ai/platform-overv…

TL;DR : Winning H2 comes down to one thing: can you say why you shipped something and what it moved. Everyone builds now, not just product and eng, so your job is making sure they build what matters. Which makes this the best time yet to be a PM. You finally get to be the domain expert you always wanted to be. 🧵 1. "No one is impressed that you shipped faster." AI made building cheap, so speed stopped being the thing that sets you apart. Ship the wrong feature faster and you just reach the wrong answer sooner, after more people built on top of it.