Ai4 2026: Three Days on the Floor, One Question

We spent three days at Ai4 2026 and, by the end of day one, the pattern was hard to miss. AI is here...nobody we talked to was still debating that. What people wanted to talk about was how to run it safely, at scale, and across every team.

Ai4 is the largest gathering of applied AI technologists and users in North America. More than 12,000 attendees from over 90 countries, more than 1,000 speakers, and over 400 exhibitors. The scale was the headline. And standing in the middle of it for three days, the more interesting thing was the shift underneath. Enterprises have stopped asking whether AI belongs in production. They are now working out how to govern it once it gets there.

That shift ran through the sessions we sat in and nearly every conversation we had. Here is what stood out to us.

#On the main stage

The keynote everyone came for brought Geoffrey Hinton, Fei-Fei Li, and Andrew Ng to the same stage. Three people who shaped modern AI, together for one conversation. It ranged across frontier research and human-centered design, but it kept coming back to governance: how organizations keep control while capability keeps accelerating. Hearing that emphasis from the founders of the field, on day one, set the tone for the rest of the week.

The enterprise track is where the practical lessons lived. Leaders from PayPal, US Bank, Cisco, Uber, Mayo Clinic, and Ford all walked through what deployment looks like inside a large organization. They kept landing on the point the keynote did. Models are the easy part. The hard part is access, cost, audit, and control across many teams and multiple providers.

Our Co-Founder and CEO, Rafi Khardalian, joined a panel alongside speakers from JP Morgan Chase, Wayfair, and DigitalNet.ai. The session, "The Orchestration Layer: Coordinating Models, Tools, and Workflows," focused on what it takes to run multiple models and agents through one governed path instead of a tangle of point integrations. A video of the session will be available soon on the Actualyze AI web site and the Ai4 YouTube channel.

#Why we were there

We came to Ai4 having emerged from stealth that same week. On Monday, August 3, as the pre-conference programming began, we introduced the company and the product publicly for the first time. So, this was our first look at whether the solution we had been building in private resonated with the people we built it for.

Actualyze is the foundation for enterprise AI. It sits between your applications and your inference, built to govern, secure, operate, and optimize every call, enforced automatically and invisible to your code. It is built around 4 key pillars. Govern sets access, approvals, and budgets in one place. Secure applies inline guardrails, redaction, and a tamper-evident audit trail. Operate catalogs and deploys every model your teams use. Optimize routes each request to the best model by capability, cost, and latency, with automatic failover.

The problem it solves is one every enterprise we met at Ai4 recognized right away. As Rafi puts it, in too many AI deployments "the bill arrives before the budget." He and our Co-Founder and CTO, Sean Lynch, started from a simple question drawn from years inside building solutions for large enterprises. What would we need to run AI inference safely, cost-effectively, and securely, with the best model for the job on every call?

Cost came up in nearly every conversation we had. On cost, the Actualyze platform looks at the real per-token price and gives teams hierarchical budgets they can cap by project, application, or team. A rogue agent that goes off the rails hits a budget limit and stops. No burning through ten or twenty thousand dollars first. Budgets stay independent of a company's formal reporting structure, so the limits follow the work even when projects cross organizational lines. One number we shared at the booth landed every time we said it out loud. In an analysis we ran, a single user fully exercising a flat-rate AI subscription could consume roughly twelve thousand dollars of inference. That gap makes the case for controls on its own.

Security was the other topic that was top of mind for virtually everyone we spoke with. Actualyze sits in the path of every request, so policy is enforcement rather than an honor system. A purpose-built model watches for the shapes of sensitive data, from PII and PHI to API keys, passwords, and Social Security numbers, and either logs the detection or redacts the content before the request ever reaches a model provider. The provider only sees the clean version.

Actualyze is deliberately provider-neutral, and we said it a lot over three days. Our view is that the place where AI is governed should not belong to any single model vendor, because enterprises will keep running a growing mix of frontier, specialized, and open-weight models. The goal is to let organizations use the best model for each job while spend, security, and auditing stay in one place. As Rafi framed the longer arc, the biggest benefit arrives when inference becomes commoditized enough to resemble bandwidth.

While speaking with attendees and presenting the Actualyze platform, repeatedly, we heard the same story: thousands of people using AI across an enterprise, with no central place to see it, govern it, or pay for it. The gaps came up in a predictable order, conversation after conversation. Visibility into who is calling which model. Guardrails that apply to every request instead of every application. Spend attributed before the invoice arrives. Regulated teams in government, banking, defense, and healthcare added one requirement above all others which was deployment control; on-prem (coming soon to Actualyze!) and data that stays inside their boundaries. Others told us they are a few months out and want the foundation in place before the sprawl starts. Consultancies asked a different question entirely: how do we bring this to our own clients? Governing enterprise AI is no longer a future problem, and the people closest to the workloads already know it.

While giving our demos, there was almost always a moment partway through where the person watching stops asking questions and just nods. We saw it a lot. What people were reacting to was not really the demo. It was seeing their own problem on the screen. How do we manage AI usage across an entire enterprise without becoming the bottleneck that slows everyone down?

There was one scenario that played out a few times and summed up the event very well for us. People came to our booth who had taken a photograph of the Ai4 sponsor list, fed it into ChatGPT along with a description of what they were looking for, and had Actualyze AI come back as the answer.

#About Actualyze AI

Before founding Actualyze AI, co-founders Rafi and Sean built Metacloud, a managed private cloud company acquired by Cisco in 2014, so they have already watched one infrastructure market mature from novelty into utility. We're backed by a $7M seed round from Storm Ventures, Canaan Partners, Morado Ventures, and Jerry Yang's AME Cloud Ventures, and we're working with organizations across multiple industries, including highly regulated ones, through our Early Access program.

#The agents are already in production

Ai4 removed any doubt: this is no longer a future problem. Agents are shipping, the budgets behind them are getting scrutiny, and the auditors have started asking harder questions. The teams that figure out how to move fast without losing control are the ones the next few years will belong to.

To everyone who came by the booth, sat through a demo, or grabbed us in a hallway to compare notes: thank you. See you at the next one.

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