Israel Cauich Viñas

The market doesn't want you to sell them an AI solution. That's the default. The market wants you to reinvent their business with it. That's the expectation.

Day 1 of 500 Silicon Valley Takeover. @khailee opened with the stat that should reframe every international founder's ambition: SF raises $2–3B per week in early-stage deals alone. Most countries don't see that in a year. @albertlee (MyFitnessPal, $475M exit) closed it: "Moving motivation is very, very difficult. Almost all of our time was spent reducing friction." "If users succeed, we succeed. That's all that matters." 20 more days to go.

Damn, lately it feels like the second you open @claudeai, you've already hit the session limit. So frustrating.

Most companies are building AI agents that nobody can fix when they break. Not because the technology failed. Because a human never understood the logic in the first place. A COO told me this today. He was right. And that is exactly the problem we built Neuraan to solve. Your agent should run inside your business rules. Not on top of them. Every decision traceable. Every rule editable by the ops team, not IT, not the vendor. When something breaks, a human fixes it in minutes. Because they built it. Because they own it. Black box AI is not an AI problem. It is an accountability problem. Write me a DM or at i@neuraan.com if this is a conversation your team is already having.

Before: AI as copilot. Individual productivity up. Coordination cost unchanged. Org chart intact. Hierarchy still routing every decision through 4 layers. After: AI as the coordination layer. Q1 closed in 47 min. 260K hrs/year recovered. Ops team runs it. @jack just published why copilot is the wrong bet. We built the other thing at @neuraan.

If you are evaluating AI agents and want to understand what an outcome-based deployment looks like in your operation, I will walk you through it. Real numbers, real process, no token math. Reply or DM me.

The shift happening in enterprise AI is not about better models. It is about who carries the risk. Outcome-based agents put the risk back on the vendor. That is where it belongs.

The AI vendors charging you per token have no idea if their product is working. They get paid whether your operation improves or not. That is not a business model. That is a subscription to a science project.

Our build process: do it manually first. Find where it breaks at scale or where it slows you down. Automate the one task that bottlenecks everything. Then make the whole thing agentic. Most teams skip step one. That is why their agents fail.

On AI products, intelligence is becoming a commodity. The winners won't have the best models. At Neuraan we believe the right model depends on the task. They'll win by making the accountant, the ops lead, the teacher ten times more effective. Adoption is not sold. It's earned, one person at a time.

Mi cofundador @mario_campos_ dio una plática ayer en Mérida sobre cómo operamos @neuraan con IA agentica. Título: "IA que Trabaja Sola (Casi)" El "casi" es la parte importante. Nuestro stack no es un wrapper de GPT. Mezclamos lógica simbólica + LLMs para que los agentes creados en Neuraan no alucinen en procesos críticos. Philosophy: → Claude Code para lo que puede volar solo → Cursor cuando retomo el volante → "Confío en mis agentes, más en mi criterio cuando el cambio es crítico" La IA no reemplaza al ingeniero. Lo convierte en director de infraestructura de software.

The thing that will make a VC want to fund you is not how well you match their (claimed) investment thesis. It's the same thing that will make every other VC want to fund you: how well you're doing.

@John0lven @StanfordGSB @hiveflow_ai Great news @John0lven! Incrementing your success odds.

Big news. 🚀 I’ve been accepted into Cohort 3 of the LBAN Startup Accelerator at @StanfordGSB . 9 weeks. World-class faculty. Latino founders building what’s next. Bringing everything back to @hiveflow_ai . 🚀 #Stanford #LBAN #LatinoFounders

Turns out the future of enterprise AI isn't a fancy interface. It's a text file both humans and agents can actually understand. Low status. High usefulness. That's where adoption lives.

We're building enterprise AI at Neuraan and our "secret weapon" is markdown files. Sounds boring as hell. No one's going to demo markdown at a conference. But here's the thing: our agents read the same product docs that we read. Same markdown guides. Same instructions. When a finance analyst updates a workflow doc, the agent instantly knows the new rules. No retraining. No tickets to IT. No black box.