Pods are our strategic focus areas. Each pod is a small universe—one thesis, many ventures—where wins reinforce each other.
Instead of scattering bets, we concentrate in places where insider pain is clear and modern AI lets us do what wasn't possible five years ago.
Why Pods?
Shared thesis → shared customers → shared playbooks. Every success inside a pod lowers the cost, time, and risk of the next—turning repetition into reinforcement.
Anatomy of a Pod
Thesis: our point-of-view on a problem space and why now.
Core beliefs: the invariant truths we'll build around.
Observations: signals that the space is important, under-explored, and ripe.
Master metrics: control points that prove compounding value.
Opportunity areas: where multiple startups can emerge.
Calls to action: bring in founding partners, corporate partners, and co-investors to co-build.
How compounding happens
01 →
More real-world assets
02 →
Better data & models
03 →
Fewer failures / higher ROI
04 →
Lower unit cost & faster sales
05
More adoption → category lead
This "virtuous cycle" is why we concentrate instead of spray-and-pray.
Master metrics
% unplanned downtime ↓
% asset/service life ↑
% cost to serve ↓
% workflow automation ↑
% compliance / accuracy ↑
Data scale → model accuracy ↑
We tailor these per pod; they're the proof that learning and data are compounding (and that customers feel it).
Pick a pod
Who plugs into a pod?
Founding partners: insider operators who've lived the pain.
Corporate partners: design partners, data access, real pilots.
Co-investors: bring networks and follow-on firepower.
Studio team: AI engineering, product, GTM, compliance, and venture ops.
We don't fund ideas; we co-build products with customers from day one—AI by design, not as an afterthought.
How we build
Pods: focused theses that compound
Pods are our strategic focus areas. Each pod is a small universe—one thesis, many ventures—where wins reinforce each other.
Instead of scattering bets, we concentrate in places where insider pain is clear and modern AI lets us do what wasn't possible five years ago.
Why Pods? Shared thesis → shared customers → shared playbooks. Every success inside a pod lowers the cost, time, and risk of the next—turning repetition into reinforcement.
Anatomy of a Pod
Thesis: our point-of-view on a problem space and why now.
Core beliefs: the invariant truths we'll build around.
Observations: signals that the space is important, under-explored, and ripe.
Master metrics: control points that prove compounding value.
Opportunity areas: where multiple startups can emerge.
Calls to action: bring in founding partners, corporate partners, and co-investors to co-build.
How compounding happens
More real-world assets
Better data & models
Fewer failures / higher ROI
Lower unit cost & faster sales
More adoption → category lead
This "virtuous cycle" is why we concentrate instead of spray-and-pray.
Master metrics (pod-level control points)
% unplanned downtime ↓
% asset/service life ↑
% cost to serve ↓
% workflow automation ↑
% compliance / accuracy ↑
Data scale → model accuracy ↑
We tailor these per pod; they're the proof that learning and data are compounding (and that customers feel it).
Pick a pod
Who plugs into a pod?
Founding partners: insider operators who've lived the pain.
Corporate partners: design partners, data access, real pilots.
Co-investors: bring networks and follow-on firepower.
Studio team: AI engineering, product, GTM, compliance, and venture ops.