interos.ai
The global economy does not break in headlines. It breaks in silence, somewhere between a 3rd tier supplier no one mapped and a tariff shift no one modeled. That is where interos.ai operates, and if you spend enough time inside SaaS, you start to recognize the difference between software that reports and software that sees. This one sees.
Jennifer Bisceglie built Interos in the mid 2000s, long before supply chain risk earned a permanent seat in the boardroom. The insight was simple and uncomfortable. The real exposure is not in the vendor you manage. It is buried in the relationships you cannot see. In 2024, Ted Krantz Jr. stepped in as CEO, bringing 20+ years of scaling AI and enterprise platforms across data.ai, C3.ai, SAP, Oracle, and Skai. Jennifer Bisceglie moved to Founder and Executive Vice Chair. This is not a ceremonial shift. It is a calculated handoff from origin insight to operational scale.
The platform behaves less like software and more like infrastructure. interos.ai maps multi tier supplier networks and assigns an i Score across financial, cyber, geopolitical, restrictions, catastrophic, and ESG risk. That score becomes a shared language inside the enterprise. Then the system leans forward. itariffs models tariff exposure across those same layers. Similar Suppliers identifies replacements ranked by overlap and risk profile. In a category crowded with dashboards, this is SaaS that makes decisions move faster, not just look prettier.
The customer list reads like a stress test. Google. NASA. U.S. Navy. L3Harris. Over 100 Fortune 1000 companies and federal agencies using the platform where failure is not theoretical. The capital stack follows the same pattern. Kleiner Perkins, Venrock, and NightDragon backed early conviction. A $100M Series C in 2021 pushed valuation past $1B. Blue Owl Capital reinforced the bet with $40M in 2024 and another $20M alongside Structural Capital in 2026. In SaaS, capital tends to chase growth curves. Here, it is chasing inevitability.
Timing is doing its part. Tariffs now shift faster than procurement cycles. ESG compliance is enforceable, not optional. Cyber risk flows through vendors as easily as code. What used to live in operations now sits in audit committees and board decks. interos.ai is positioning the i Score as a standard, and if that standard holds, this becomes less a product and more a baseline expectation across SaaS and beyond.
Inside the company, the culture reflects the problem set. Engineers and analysts working in ambiguity, building models on incomplete data, solving for consequences that ripple across industries. Distributed teams across the U.S. and Europe, aligned around systems that touch trade, security, and climate in the same motion. This is not clean data work. It is consequential data work.
They are hiring across engineering, machine learning, and go to market roles, with a clear expectation. You are not here to maintain systems. You are here to understand them deeply enough to challenge them.
If you are operating anywhere near global supply chains, this is not optional awareness. The companies that understand their dependencies will move with precision. The ones that do not will learn about them in real time, under pressure, when options are already thin.
