Ideatum is a team of PhDs in AI and Science. We consult, design, and build production AI — and we assess the systems others have built. From strategy to deployment, we bring the rigour that matters.
We work across the full AI lifecycle — from identifying where AI creates value, to building and deploying production systems, to assessing and governing the ones already running. Every engagement is led by senior researchers with deep domain expertise.
We help organisations identify where AI creates real value — and where it doesn't. Practical, evidence-based roadmaps that align AI capabilities with business objectives and technical feasibility.
StrategyEnd-to-end design and engineering of production AI systems — from data pipelines and model training to deployment infrastructure. ML, NLP, computer vision, and domain-specific solutions.
DevelopmentRigorous assessment of AI systems using the AI-SOFA framework. Quantitative risk profiling for investors, boards, and regulators — built to withstand scrutiny.
AssessmentMonitoring, maintenance, and continuous improvement of deployed AI systems. We ensure your models stay accurate, your pipelines stay stable, and your systems degrade gracefully.
OperationsPre-audit mapping against EU AI Act, DORA, and sector-specific requirements. Documentation packages built for supervisory expectations and board-level governance.
ComplianceBoard-level and technical advisory on AI strategy, model governance, and publication-quality system documentation — particularly for life sciences, health tech, and financial services.
Advisory"The question is not whether an AI system can perform a task. It is whether it degrades gracefully — or collapses."AI-SOFA Framework · Ideatum
Frontier AI systems are being deployed into production faster than the frameworks to evaluate them have matured. Investors fund them. Boards approve them. Regulators are still writing the rules.
When they fail — they fail in ways that are opaque, sudden, and systemic. The financial system has stress tests. Clinical medicine has validated endpoints. AI deployment, by contrast, largely relies on benchmark scores and vendor assurances.
That gap is where catastrophic risk lives. We built Ideatum to close it — with both the scientific depth to understand AI failure and the engineering capability to prevent it.
"Knight Capital lost $440M in 45 minutes. Zillow wrote down $569M. In both cases, the system passed every internal benchmark before deployment."
"Rigorous AI development and rigorous AI assessment are not separate disciplines. They are the same discipline, applied at different stages."
AI risk cannot be assessed with checklists or qualitative impressions. We apply clinical severity scoring methodology — adapted from ICU medicine — to derive a structured, numerical risk profile for every system we build or assess.
Our research identified a specific threshold — the AI-Shock Condition — that separates recoverable failures from terminal ones. We engineer systems to stay above that boundary, and we assess others against it.
Whether it's a production system or an assessment report — our deliverables are built for board scrutiny, regulatory audit, and real-world stress. The methodology is derived, not assembled.
AI-SOFA (AI Systemic Operational Failure Assessment) is Ideatum's proprietary quantitative framework for evaluating catastrophic failure risk in deployed AI systems. Developed by analogy with the SOFA score used in intensive care medicine to predict organ failure — applying the same logic of multi-dimensional severity scoring to AI systems in production.
The framework assesses eight dimensions: Robustness, Controllability, Transparency, Alignment, Scalability, Dependency, Reversibility, and Oversight. Each is scored and combined into a risk profile that drives specific, prioritised recommendations.
The core empirical finding — the AI-Shock Condition — emerged from case analysis of historical AI failures across financial services, real estate, and health technology. Systems scoring above threshold on both Robustness and Controllability degraded gracefully. Those below did not recover.
Robustness > 2 AND Controllability > 2 cleanly separates terminal failures from survivable ones. This threshold drives all remediation priorities.
We carry no vendor relationships, no software commissions, no platform affiliations. Our only incentive is the quality of the work — whether we're building a system from scratch or assessing one built by others.
The Assessment Protocol — the operational heart of AI-SOFA — remains unpublished. It is the reason clients trust our assessments and the foundation of everything we build.
AI risk cannot be assessed qualitatively. We quantify all eight SOFA dimensions and derive risk classifications from evidence, not narrative.
Our core empirical finding separates terminal failures from survivable ones. This single threshold drives the entire remediation agenda for every system we build or assess.
Our methodology draws simultaneously on clinical medicine, algebraic statistics, and machine learning. This cross-domain depth cannot be replicated by single-discipline teams.
All proprietary methodology remains unpublished. Client data never leaves the engagement perimeter. NDAs are standard from first contact.
Whether you need to build an AI system, assess one, or navigate the regulatory landscape — we'd like to hear from you.