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Analytics
Explains What Happened
Analytics reveals historical patterns and trends. It helps explain the past - but not why complex systems behave as they do.
Past → description
Discovery Intelligence
Make Complexity
Computable
Traditional AI predicts what’s knownWe discover what isn’t
Transform complex systems into computable models that reveal hidden relationships, eliminate costly blind spots, and uncover new knowledge beyond the limits of traditional AI, and knowledge graphs.
The Gap
You Can’t Predict Your Way Thru Complexity
Traditional technologies help us analyze the past, predict likely outcomes, and organize existing knowledge. But complex problems emerge from hidden relationships, incomplete evidence, sparse data, and interacting dimensions that cannot be fully predefined.
Discovery requires making complexity computable.
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Artificial Intelligence
Predicts What Is Likely
AI recognizes patterns and predicts outcomes from large data sets. But prediction alone can’t model the interacting dimensions driving a complex system.
Patterns → prediction
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Knowledge Graphs
Organize What Is Known
Knowledge graphs connect and represent existing knowledge. Their entities and relationships must be pre-encoded, validated, and maintained as complexity grows.
Encoded knowledge → representation
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Discovery Intelligence
Makes Complexity Computable
Discovery Intelligence models complex systems to reveal hidden relationships, evaluate competing theories, and uncover new knowledge.
Complexity → discovery
Discovery begins where prediction and representation end
Prediction tells you what is likely. Knowledge graphs organize what is known. Discovery Intelligence reveals what neither can tell you.
See How It Works →Discovery Intelligence
A New Category of Intelligence for Complex Systems
Discovery Intelligence is designed to make complexity computable - transforming interacting dimensions, incomplete or competing evidence, and hidden relationships into real-world models where new knowledge can emerge.
- Reveals relationships that can’t be predefined
- Works with real-world data - sparse, wide, and unnormalized
- Separates signal from noise across competing theories
- Generates new knowledge - and validates what other AI predicts
Complex systems call for real-world data.
insitro CEO Daphne Koller argues that AI’s “magic wand” promise rests on a false assumption - that we already understand biology well enough for powerful AI to find cures in what we know. The real bottleneck is the data reality complex systems actually produce. RYLTI was purpose-built for that data reality.
- Daphne Koller, “Drug Discovery Has No Magic Wands,” a16z, August 2026
insitro CEO Daphne Koller argues that AI’s “magic wand” promise rests on a false assumption - that we already understand biology well enough for powerful AI to find cures in what we know. The real bottleneck is the data reality complex systems actually produce. RYLTI was purpose-built for that data reality.
- Daphne Koller, “Drug Discovery Has No Magic Wands,” a16z, August 2026
From Complex Problem to New Knowledge
How Discovery Intelligence Works
A proven methodology for turning complexity into discovery.
1
Define the Discovery Problem Domain
The problem inquiry frames the discovery boundary - what you’re trying to understand, not what data you have.
2
Model the Problem Dimensions
Each dimension is modeled independently as its own expertise graph, built from its own structure, evidence, and context. Domain experts can embed rules and knowledge directly into the model.
3
Compose the Real-World Model
Compose independent expertise graphs into a real-world model of the full multidimensional problem space.
4
Run the Discovery Engine
The engine explores relationships across all dimensions of the model to surface what no one has predefined or encoded.
5
Surface New Knowledge
New relationships, insights, and discoveries emerge - ready to be acted upon and built into reusable knowledge.
Published Research
Discovery That’s Been Proven
Our discovery methodology has been independently validated in peer-reviewed research across two disease areas - findings other pipelines could not surface, confirmed by an outside research team.
Genomics · Variant Discovery
Novel Genetic Variant Associations Invisible to Conventional Pipelines
Our multidimensional analysis surfaced genetic associations in endometriosis that conventional genomic pipelines could not detect - associations that were independently validated by the research team.
Journal of Molecular Diagnostics
Published with Harvard, Johns Hopkins, and QIAGEN
Published with Harvard, Johns Hopkins, and QIAGEN
5Novel genetic variant associations discovered
2Present in 100% of cohort, absent from controls
0These associations existed in prior literature
20+ Years of Discovery Across Complex Domains
Patent-pending methodology | Real-world complexity | Repeatable discovery
Lung Cancer · Oncology
Surfacing EGFR Mutation Signals in Pathology Metadata
Applied to lung cancer pathology, our AI scan metadata analysis model identified a concentration threshold linked to EGFR mutation status. Built on Moroccan and North African patient data, a population absent from the EGFR models built on European, Asian and American data. It’s an evidence-based approach: the model keeps the attributes that carry real signal and drops those that don’t, such as age and sex.
Discovery finding selected for conference presentation (Nov 2026), with peer-reviewed publication in preparation.
Healthcare · Fraud Discovery
Identified Fraud No Other System Could Find
Applied to a complex healthcare program, the methodology surfaced specific individuals responsible for hundreds of thousands of dollars in fraudulent drug claims - a pattern invisible to every existing compliance and analytics system in the organization.
Discovery surfaced what analytics, audits, and AI-based monitoring all missed - the signal lived across dimensions no single system modeled together.
Aviation · Crisis Decision Systems
Decision Signals Under Real-Time Uncertainty
Applied to crisis decision-making in aviation operations - modeling resource constraints, operational uncertainty, and decision pathways under real-time pressure - the methodology surfaced actionable decision signals amid volatility and ambiguity that conventional planning tools could not produce.
A compositional structure for modeling real-world volatility and ambiguity across airports to discover actionable decisions that factor in competing priorities.
The Discovery Ecosystem
Market-Ready Discovery Solutions
Each module answers a domain-specific problem - the essential starting point for a market-ready solution. Built with partners and domain experts from validated discoveries: some public, some private, more on the way.
Available nowPrivate · by requestComing soon
VUS Discovery
Genomics · Variant Interpretation
Surfaces clinically meaningful signal in variants of unknown significance - turning genomic ambiguity into interpretable, testable findings. Public and in use today.
Explore VUS Discovery ↗EGFR Discovery (working name)
Oncology · Pathology Metadata
Developing the validated lung-cancer metadata model into a reusable discovery module that flags when EGFR molecular testing is warranted. Phase 2 validation underway.
Contact UsDrug Repurposing
Biopharma · Drug Repurposing
Surfaces non-obvious matches between existing drugs and new indications - repurposing candidates conventional screening misses because the signal lives across interacting biological dimensions no single model connects.
Contact UsAviation Crisis Decisioning
Aviation · Critical Operations
Surfaces actionable decision signals under real-time uncertainty - resource constraints, volatility, and ambiguity conventional planning tools can’t model. Available to partners by request.
Contact UsFraud Discovery
Healthcare · Fraud & Anomaly
Finds fraud living across interacting dimensions that single-system compliance and analytics tools miss. Deployed with partners under private engagement.
Contact UsDifferent domains. Different dimensions.
One compositional discovery methodology.
Each solution begins with a focused inquiry and a starting model for a complex-system domain - built with a partner who knows the field.