Do you know your top risks?

Using AI to Transform Risk Management

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Hi, I’m Milad.
 

I lead the team behind Oromni, an early-stage startup building a next-gen risk intelligence engine to rethink how risks can be better identified, assessed, and managed so we can use it to make better decisions for organizational resilience and project delivery.

Most risk registers are treated as compliance checklists — Stale, subjective, and rarely used.
 

But when bad or missing data can cost up to 25% of revenue on average, risk isn’t a formality—it’s a decision advantage.
 

What’s Changed — And Why It Matters

Large language models (LLMs) can now process massive unstructured datasets—finding valuable data that can applied to a given context.

But off-the-shelf AI like ChatGPT isn’t built for enterprise-grade risk work. The results are inconsistent, prone to errors (hallucinations), unaware of context, and heavily reliant on prompts. You can’t stake decisions on that.

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So why & how are critical sectors like Defence already using LLMs for high-stakes operational tasks?

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Because when LLMs are purpose-built, trained on domain-specific data, and deployed with specialized R&D, they become dependable—secured, scoped, and surgically applied to real-world problems.

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That’s What We’re Doing at Oromni

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At Oromni, we’ve committed to that same principle: purpose-built AI for risk management for the organizations delivering major projects.

We’re developing a secure, specialized system—currently in beta—that connects to your existing risk register, ingests contextual project data, and transforms your risk view from partial to panoramic.

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And the Payoff is Real

• In a study of 23,000 capital projects, teams with live, continuously updated risk registers reduced cost growth by 7% and finished 17% faster.²

• Companies that score as “mature” in the RIMS Risk Maturity Model trade at around 25% higher market value than peers.³

• Data-driven capital project teams have cut overruns by up to 50%.⁴

These results show what’s possible when risk management becomes evidence-based, & not limited to the experience of the team & potential optimism bias.

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How It Works

Oromni is a secure risk-intelligence engine that:

• Ingests your context — including matrices, registers, project business cases, contracts and more.

• Harvests lessons learned — from case studies, industry failures, safety alerts, expert research, and past projects.

• Re-evaluates all these data points to what is precisely relevant to your current specific context to surface hidden risks, re-evaluate known ones, and assess new concerns. It auto-rates risk levels, uncovers causal links, and recommends proven mitigations—all tailored to your current specific situation.

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What That Means for You

✅ Fewer surprises — with early warnings rooted in real-world evidence.
✅ Faster decisions — building or refreshing a risk register in hours, not weeks.
✅ Hard ROI — from less rework, fewer blind spots, and stronger confidence in planning.

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We don’t replace your expertise. We amplify it—with better data, sharper insights, and faster analysis.
Better risk awareness doesn’t come from working harder. It comes from working smarter—with AI on your side.

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Want to See It in Action?
Contact us below to request a beta demo, where we show you how it all works with our sample data relevant to you. If we are a fit, you can join our growing waitlist of early adopters wanting support in delivering their ambitious and complex major projects - or join us as a R&D partner.

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Sources

1. MIT Sloan Management Review, Seizing Opportunity in Data Quality (2017).

2. Independent Project Analysis, What Does a Good Risk Register Look Like? IPA Newsletter, Vol. 16 Iss. 4, December 2024.

3. Farrell, M. & Gallagher, R., “The Valuation Implications of Enterprise Risk Management Maturity,” Journal of Risk and Insurance, 82(3), 2015.

4. McKinsey & Co., Saving time and money in capital projects (2022).

5. Pragmatic Institute, Overcoming the 80/20 Rule in Data Science (2024).

6. McKinsey & Co., The Construction Productivity Imperative (2015).

7. KPMG, Future of Risk global executive survey (2024)