Why We Built io.Intelligence (And Where We’re Taking It Next)

When I stood up at our Client Forum in New York this October to talk about io.Intelligence, I realized something: in less than a year, AI had gone from an “interesting experiment” to a top-level mandate for every client in the room.

But I also heard the same worries:

• “We’ve done a pilot, but how do we get this into production safely?”
• “We don’t want to bolt on another UI that sits off to the side of our existing workflows.”
• “We’ve invested years in interoperability and FDC3—how does AI fit into that?”

io.Intelligence is our answer to those questions.

This post is my attempt to share why we created io.Intelligence, what it actually is, and where we are right now on the journey.

We Are Not an AI Company

Let me start with an important disclaimer: we didn’t spin up io.Intelligence just so we could add “AI” to our logo.

At our core, interop.io is still about interoperability and workflow enablement. Our mission hasn’t changed.

As we spent more time working with AI in 2024 and 2025, it became obvious that interoperability is the missing foundation for most AI initiatives. You can’t have useful copilots or agentic workflows if they can’t reliably:

• See the right context from your existing applications
• Take action across your actual trading, research, or servicing tools
• Respect your security, compliance, and governance constraints

We didn’t need to reinvent ourselves as an AI company to help you bring AI into production within your current ecosystem. We’ve been the trusted interoperability partner to firms like J.P. Morgan, Citi, and Fidelity for years.

What Is io.Intelligence?

io.Intelligence is three things at once:

1. A business unit – a focused team inside interop.io whose full-time job is AI enablement for our clients.
2. A product line – a set of capabilities that extend io.Connect for copilot and (eventually) agentic workflows.
3. A framework – an enterprise-grade architecture that gives you choice around models, UIs, and deployment patterns.

The io.Intelligence Framework

We’ve built the framework in the same way we’ve always built io.Connect: modular, extensible, and opinionated where it needs to be.

At a high level, it includes:

io.Assist UI

A ready-to-use copilot interface that plugs directly into io.Connect. If you want to give users a governed, “ChatGPT-style” experience that actually understands your desktop, io.Assist is the fastest way to start.

Web SDK

Prefer your own UX? The Web SDK lets your teams build custom front-ends while benefiting from the underlying io.Intelligence services and integrations.

io.Intelligence MCP Server

Built on Anthropic’s Model Context Protocol (MCP), this server exposes:

• A growing library of tools we’ve authored
• Deep, bespoke integrations into io.Connect
• Support for dynamic tools so you can bring your own

In plain terms: it’s the plumbing that lets your AI talk to your applications and take action across workflows.

Designed for Optionality: Meet Us Where You Are

Every client I speak with is at a different point in their AI journey. Some have full internal copilot teams. Others are still at the “we bought some licenses—now what?” stage.

We built io.Intelligence to be flexible:

Bring your own UI, or use ours

Use io.Assist out of the box, extend it, or plug in your own front-end via the SDK.

Bring your own models

We’re deliberately multi-model. Whether you’re using Anthropic, OpenAI, Google, or others, our goal is to make them all first-class citizens.

Bring your own copilot

Already built a copilot internally? Great. Use our MCP server to give it more tools and deeper access to your io.Connect environment.

Bring your own prompts and tools

We provide sensible defaults, but you can plug in your prompts, tools, and resources so the system reflects your domain expertise.

io.Intelligence adds value wherever you are in your AI journey.

From Experiments to Production: The io.Intelligence Engagement Model

One of the consistent themes I hear is: “How do we move from pilot to production?”

That’s why we introduced a structured way to work with our team—a production-focused engagement model.

Pick a real use case

Together, we choose a concrete, high-value workflow—usually something copilot-driven where success is clearly measurable.

Scope a Proof of Value

We define success criteria, data and system touchpoints, and how this will fit into your existing desktop or browser experience.

Build with our framework and your stack

We combine:

• The io.Intelligence framework (UI, server, MCP, integrations)
• Your chosen models and internal systems
• Our consulting expertise across architecture, governance, and UX

Deliver into production

We demonstrate a live, governed deployment to your users.

Leave you better equipped

You come away with a working solution and a repeatable pattern you can use internally.

We do have finite capacity. This is a hands-on partnership with a focused team. If you’re stuck between pilot and production, this is where we can help most immediately.

Where We’re Headed

Looking into 2026, our goals for io.Intelligence are straightforward:

1. Make it easier and safer to deploy copilots into production on top of io.Connect.
2. Turn interoperability into a foundation for your AI initiatives.
3. Codify best practices—governance, UX, architecture—into the framework so you don’t have to reinvent them.
4. Keep AI woven into your desktop strategy.

If you’ve invested in interop.io, I genuinely believe you have a structural advantage when it comes to AI. io.Intelligence is our commitment to helping you turn that advantage into real, live production value.

Reach out to your Client Success Manager or contact the io.Intelligence team, and we can explore whether a focused engagement makes sense.