Meet Chatgipity: a Unified AI Platform for Our Company

A little over a year ago, my friend Dan Shapiro, CEO of Glowforge, showed me what his team had built internally with AI tools. Not one-off ChatGPT experiments. Not an AI pilot program. A real, unified infrastructure where the whole company was using AI through a common platform with shared agents, shared memory, and shared integrations. I walked away from that conversation knowing we needed to do the same thing.

A few weeks later, our VP of Technical Operations, Martin Hempstock, stood up an instance of LibreChat — an open-source AI platform that brings together all the major LLMs into a single, customizable interface. Martin had it provisioned under a new subdomain within days.  It’s changed the way we work.

The most entertaining part of this story? The name.

How a Five-Year-Old Named Our AI Platform

Our Board member Jake Heller’s five-year-old son had been hearing his dad talk about ChatGPT. Except he couldn’t quite pronounce it. What came out was “Chatgipity.” When Martin was setting up the platform and needed a subdomain for the URL, he just ran with that. And so Chatgipity was born — a name so perfectly imperfect that no one ever wanted to change it.

What LibreChat Actually Does

For those unfamiliar, LibreChat is an open-source platform (MIT licensed, 34k+ GitHub stars) that gives your organization a unified AI interface with some critical capabilities:

• Model-agnostic. You’re not locked into one LLM. We use Claude, Gemini, GPT — whatever model is best for a given task. When a new model drops, we plug it in. When one gets discontinued, we swap it out. No vendor lock-in.
• Agent creation. Anyone in the company can build, share, and publish AI agents internally. This is key. You’re not waiting for IT to build things for you — marketing builds marketing agents, product builds product agents, the CEO builds his own agents (more on that in future posts).
• App connections. Through MCP (Model Context Protocol) and integrations, our agents can talk to Google Workspace, Slack, Jira, Grafana, Confluence, Salesforce — basically the full stack of tools our team uses every day.
• Security. SSO through Google, enterprise authentication, rate limiting, moderation tools. Your data stays in your environment. For a European company like ours that takes GDPR seriously, this matters.
• Shared memory and context. Agents can share knowledge and reference the same underlying data, so you’re building institutional AI intelligence, not individual silos.

Why This Matters Waaaaay More Than Individual AI Subscriptions

Here’s the thing most companies get wrong: they buy a bunch of ChatGPT Enterprise licenses and tell people to go use AI. That’s like handing everyone a word processor and calling it a content strategy.

What you actually want is a unified framework — a common infrastructure where agents share context, where integrations are built once and used by everyone, and where the organization’s collective intelligence compounds over time.

One of the best examples at our company is the Customer Migration Tool. We needed a single source of truth for customer data across multiple systems — CRM, support, billing, product usage. Instead of building separate integrations for each system, one member of our team built one agent that pulls it all together. Built once, used by everyone.

We’ve had people build a Grafana Reporter that queries dashboards in natural language. A Calendar + Email Scanner that preps you for every meeting. A Confluence Pal that makes our documentation actually searchable. A Product Feedback agent. Even a Strength & Conditioning agent and a Berlin Public Transport agent (because why not).

Within weeks of launch, we had 48 active users sending nearly 2,000 messages. Today, it’s woven into how we work — from drafting customer emails to generating ERP RFPs to running competitive analysis.

My Take

I’ll write more about my personal agents in a future post, but I want to be clear about something: I use this every day. I’m not the CEO who mandated an AI platform and then never logs in. I build agents. I break agents. I file bugs with Martin. I write prompts. This is how I think CEOs need to operate in this era — not delegating AI to the IT team, but being a hands-on user and evangelist.

If you’re running a company and you haven’t stood up a unified AI infrastructure yet, you’re leaving an enormous amount of productivity and institutional knowledge on the table. The tools exist. They’re open source. They’re ready.

And if you need a name for yours, I know a five-year-old who might have some ideas.