Interos.ai adds predictive AI to help leaders act faster on supply chain risk
Startup profile: Interos.ai
- Founded by: Jennifer Bisceglie
- Year founded: 2005
- Headquarters: Arlington, VA
- Sector: Supply chain management
- Funding raised: ~$310M to date; valuation undisclosed
- Key customers/partners: Department of Defense, Freddie Mac
- Team size: ~150 (including contractors)
What's new
Interos.ai secured $20 million in additional funding from existing backers Blue Owl Capital and Structural Capital. The company plans to double down on AI that turns mapped supply chain data into actionable recommendations.
Today, Interos.ai maps and compiles risks across supplier networks. The next step: collect first-party data directly from customers and use it to train models that suggest mitigation actions. As CEO Ted Krantz put it, "It's not a full new product, but it will provide substantially more value… to determine now what to go do."
The risk lens (six core areas)
The platform aggregates public and private data to flag issues across cyber, financial, and geopolitical vectors-plus events like extreme weather, data leaks, and unrest in markets such as Russia and China. Coverage spans items from enterprise software used in financial systems to uniforms for the US Navy.
Interos.ai is onboarding first-party data from about 100 public and private customers to sharpen prediction quality and relevance over time. The more it's used, the better it gets at ranking risk and suggesting next moves.
Why this matters for management
- Faster decisions: Move from dashboards to recommended actions. Reduce manual analysis and shorten time-to-response.
- Clear triage: Prioritize suppliers and sites by probable impact across cyber, financial, and geopolitical risk.
- Governance-ready: Traceable inputs and alerts support internal audit, compliance, and board reporting.
- Resilience gains: Shift spend and inventory with more confidence when risk spikes.
Growth plan and runway
Since 2005, Interos.ai has raised roughly $310 million, including a $40 million round in 2025. The new capital supports product development and a push to break even by the end of 2026. Krantz described near-term hiring as "modest."
Competitive angle
Several firms work in this space, including nearby competitor Exiger. Interos.ai's pitch: broader risk coverage and a new predictive layer that recommends actions. As Krantz noted, "Anything in the supply chain is fair game," which helps the platform align its risk factors with a customer's own data.
What leaders can do now
- Map your critical tier-1 and tier-2 suppliers against the six risk areas. Start with revenue-critical SKUs or regulated lines of business.
- Feed clean first-party data (ERP, TMS, risk incidents). AI recommendations are only as good as what you put in.
- Pilot with one network segment (e.g., a region or product line) and pre-define what "good" looks like: time-to-detect, time-to-act, and avoided cost.
- Set an escalation playbook for supplier risk: thresholds, approvers, and alternate-sourcing rules.
- Measure outcomes monthly: incidents avoided, cycle time saved, working capital impact, and disruption cost variance.
If your team needs fast upskilling on practical AI use cases across operations and risk, browse curated options by role here: Complete AI Training - Courses by Job.
For a standards-based view of supply chain risk practices, see NIST's guidance: NIST SP 800-161 Rev. 1.
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