Partners

How we engage.

The work output is always the same — a system that runs in production. The engagement shape varies. Sometimes we build inside your operations. Sometimes we co-build with you. Sometimes a product came out of our own operating environment. Here's how each path actually works.

For Enterprises

Enterprise Co-Development

We redesign operations around intelligent systems.

Hands-on engagement — not advisory. We embed with your team to identify the workflow where AI changes the economics, then build, validate, and deploy the system inside your operating environment. The deliverable isn't a deck. It's a working system with measurable outcomes.

Workflow diagnosis and target metrics
Build inside your stack, with your data
Validate against real operational pressure
Hand off with documentation, training, and ongoing support

For Operators & Founders

Co-Building with Operators

We co-build with people closer to the customer than we are.

When we meet a founder or operator with a real wedge, we bring the technical depth, the operating infrastructure, and the distribution to accelerate it. Engagements range from structured co-development to longer-term shared ownership. Some end up as standalone products. Some end up as spinouts. The shape is decided by what the work actually needs.

Co-development with shared IP economics
Technical and operational acceleration
Backed through partnership, capital, or both
Commercialization and go-to-market support

From Our Own Operations

Born in Production

When the workflow keeps showing up, we build it as a product.

Some of what we ship came out of external engagements. Some was built inside our own operating companies — born from a real problem in production, then productized once the pattern was clear. Fahim, Data Transformation, Legal Intelligence, and the Commercial Studio platform all came out of this path. If your team is sitting on a workflow problem worth productizing, we'd like to hear about it.

Built in production, not in a lab
Validated under real operational load
Spun out as standalone products when the thesis proves
Continues to compound across the broader platform

Fit Criteria

What we look for.

We work with a small number of partners each year. These are the filters we apply — not as gatekeeping, but so we can be honest about where we'll create value and where we won't.

Real pain, not a wish list

We work best on workflows where the cost of the status quo is measurable. "We'd like to do something with AI" is not yet a partnership.

Reusable intelligence

We favor problems where the system we build can compound — across customers, across deployments, or across the broader portfolio.

Operational seriousness

We don't run pilots that go nowhere. Engagements have target outcomes, decision points, and a path into production from week one.

Aligned upside

We structure the relationship so incentives stay aligned — through co-development economics, partnership, or capital, depending on what fits.

Have a thesis we should know about?

Tell us what you're working on. We'll respond within a day with whether we think there's a fit and how the engagement could be structured.

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