Introducing Laminar

When we started SRE․ai, we named the company after Google's Site Reliability Engineering discipline. Much of our team spent years at Google, building global-scale infrastructure, tooling, and the engineering practices that powered products like Search, Maps, Chrome, and Translate.

Early in our Y Combinator days, we saw a disconnect between the engineering rigor we had accepted as the standard and the day-to-day reality of delivering enterprise systems.

This is where we decided to devote our attention.

We chose the name SRE․ai to pay homage to the fundamental principles we believe are essential to any world-class engineering team and adopted this as our core belief: the systems running your business (CRM, ERP, HCM, ITSM) deserve the same engineering rigor, reliability, and continuity as a hyperscaler product used by billions.

It’s these principles that have enabled us to work effectively alongside technical teams to transform Salesforce delivery in some of the most complex enterprise environments.

We have approached Salesforce layer by layer (discovery, sandboxing, scratch orgs, packaging, data deployments, OmniStudio, CPQ-to-RCA migrations, Vlocity, monitoring, governance, etc.) and embedded principles from the Well-Architected Framework, Apex best practices, and core SRE tenets into our product at every layer.

In doing so, we’ve helped deliver transformational change and learned that technology alone can’t be the sole catalyst for lasting organizational impact.

This requires a fundamentally human approach.

It means working hand-in-hand with the most innovative teams inside enterprises to collaborate, push, and catalyze movement away from the status quo, solving for misaligned incentives, cost arbitrage, time-zone handoffs, and the tribal knowledge hoarded by those whose business depends on customers’ continued dependency.

Our mission is simple: enterprise digital sovereignty.

For us, this means empowering enterprises with full ownership and command of the systems that run their business.

The deep technical principles of SRE․ai remain central to how we deliver.

But our ambition has long outgrown the name.

The larger problems are systemic, and we're pioneering the solutions to them.

This growing ambition deserved a new name that captures what flow looks like when friction disappears: Laminar

What's next?

Something rare is happening. The thesis we've been advocating is resonating everywhere we go, we have never seen more appetite for change than we do right now.

The age-old adage, nobody ever got fired for buying the incumbent, is quietly inverting. The surest way to get left behind is to continue the status quo: the same business models, the same dependencies, the same handoffs dressed up as partnerships, the same time-and-materials billing.

The two biggest things that are happening:

1. You can see it most clearly with agents. Teams now understand that you don't get a working agent by buying ten hours of configuration a month. The purpose of an agent isn't to ship some software or stand up an automation; it's to drive business value, and that value is only real once the agent is in production and you're watching its resolution rates, the hours it actually offsets, the work it genuinely takes off your plate. Effort and outcome have come apart completely. Paying for hours buys you nothing but the risk of a badly-built agent quietly costing you every time it runs.
2. We've seen even more conviction in this in the DevOps world. We knew early that enterprise would become multi-system, and we built for that from the start. Whether it's Salesforce change sets, ServiceNow update sets, or SAP transports, the fundamental principles hold. And we made an early, contrarian bet: build outside the platform, not inside a walled garden of managed packages. That bet has paid off in spades. Real organizations have enormous sprawl across their CRMs, ERPs, and SaaS tools, and governing it all requires a layer that sits above any single org. More than that, as platforms like Salesforce move toward a headless model (one piece of a larger AI stack), the dev tool baked into each platform simply can't handle the complexity and can't scale with where these organizations are actually going.

In the coming months, we will be sharing more about how Laminar is evolving, new thinking, capabilities, and people making it happen.

If you are a current customer, a prospective partner, or someone who has followed our work, thank you.

We’re just getting started.